Meanwhile, companies are doubling down. Corporate AI spending will hit approximately 1.7% of revenues in 2026, more than double last year’s allocation.

So what separates the 12% of organizations achieving both revenue growth and cost savings from AI from the majority spinning their wheels?

PwC’s global chairman, Mohamed Kande, put it bluntly:

People forgot the basics.

The companies seeing results focused on clean data, well-defined processes, and strong governance before deploying AI. Everyone else rushed to adopt the technology without the foundation to support it.

Key takeaways

• 56% of companies report no meaningful gains from AI investments, while only 12% have achieved both revenue growth and cost savings
• Unplanned downtime costs Fortune 500 manufacturers $1.4 trillion annually (11% of revenue), with predictive maintenance reducing these costs by 25-40%.
• 53% of bankers rank fraud detection as their top AI use case for 2026, ahead of back-office automation (39%) and customer service (39%)
• Gartner predicts 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025. However, over 40% of agentic AI projects will be canceled by 2027 due to escalating costs or unclear business value
• High-performing organizations are three times more likely to successfully scale AI agents than their peers, with the key differentiator being workflow redesign rather than technology sophistication

This piece breaks down the current state of enterprise AI adoption, explores proven applications in predictive maintenance and fraud detection, and outlines practical strategies for achieving ROI from custom AI implementations.

Enterprise AI adoption in 2026: The gap between spending and results

Three years after generative AI tools entered mainstream business use, adoption rates have stabilized at a high level. 

The BCG AI Radar report, which surveyed 2,360 executives, found that 72% of CEOs now serve as their organization’s primary decision-maker on AI, twice the share from the previous year. 

The gap between “using AI” and “getting value from AI” keeps growing. And it explains why so many executives are frustrated. Half of them believe their job security depends on successfully implementing AI strategies.

The financial commitment reflects this urgency. Companies plan to spend approximately 1.7% of revenues on AI in 2026, more than double the 0.8% allocation in 2025. Technology and financial services firms lead this investment, with both sectors planning to allocate roughly 2% of revenues to AI initiatives.

The gap between AI experimentation and scaled production

The oft-cited statistic that “95% of AI projects fail” from MIT requires context. Most pilots stall due to organizational factors: unclear success metrics, weak executive sponsorship, skills gap, cultural resistance, rather than technical limitations. 

Technology alone doesn’t separate AI winners from the rest. Only 6% of organizations qualify as “AI high performers,” meaning they attribute 5% or more of EBIT to AI initiatives. 

The defining factor is workflow redesign. High performers are nearly three times more likely to have fundamentally restructured processes around AI capabilities (55% compared to 20% for everyone else). 

They also put real money behind it: over 20% of digital spend goes to AI, versus just 7% at average organizations. Perhaps more telling, these companies are 3.6 times more likely to pursue enterprise-wide transformation, targeting growth and innovation rather than settling for isolated efficiency wins. 

Agentic AI adoption: Enterprise projections and market realities

AI agents, autonomous systems capable of planning and executing multi-step tasks without continuous human prompting, represent the next frontier of enterprise automation. 

40% of enterprise applications will incorporate task-specific AI agents by the end of 2026, up from less than 5% in 2025. In its best-case scenario, agentic AI could generate approximately 30% of enterprise application software revenue by 2035, exceeding $450 billion.

There is a warning worth heeding: over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. 

About 130 of the thousands of vendors claiming “agentic AI” capabilities offer genuine agent technology, with many engaging in “agent washing” by rebranding existing products such as chatbots and RPA tools.

AI ROI benchmarks for custom AI solutions

AI investment keeps climbing, with Gartner projecting enterprise AI software spend to nearly triple to $270 billion this year. Only about one-third of enterprises have seen tangible cost reduction or revenue increase from AI in the past 12 months. 

Matt Marze, Vice President at New York Life Insurance Company, told CIO magazine that his team approaches AI investments “the same way we think about all our investments,” evaluating each project against operating expense reduction, margin improvement, and revenue growth. 

Banking use cases: AI-powered customer service and fraud detection

Bank of America’s Erica virtual assistant has surpassed 3 billion client interactions since its 2018 launch, now serving nearly 50 million users and averaging 58 million interactions per month. 

The bank reports that 98% of users find the information they need through Erica, significantly reducing call center volume. 

On the employee side, over 90% of Bank of America’s workforce uses Erica for Employees, which has reduced IT service desk calls by more than 50%. 

According to Holly O’Neill, president of consumer, retail, and preferred lines of business, the two million daily consumer interactions with Erica save the bank the equivalent of 11,000 staffers’ daily work.

On the fraud side, the UK government’s Cabinet Office reported that AI-powered detection tools helped recover £480 million between April 2024 and April 2025, the highest amount ever recovered by government anti-fraud teams in a single year. The Fraud Risk Assessment Accelerator, developed internally, cross-references data across government departments to identify vulnerabilities before they are exploited.

Manufacturing use cases: Predictive maintenance and quality inspection

Shell’s predictive maintenance platform, built with C3 AI, now monitors over 10,000 pieces of critical equipment across its global operations, ingesting 20 billion rows of data weekly from more than 3 million sensors. The system identified two critical equipment failures in advance, allowing preventive maintenance that saved approximately $2 million and “substantially improved operational reliability.”

In automotive, Siemens and Audi deployed AI-powered visual inspection in Audi’s car body shops, where 5 million welds are made daily. According to NVIDIA, integrating the models with Siemens’ Industrial AI Suite helped Audi achieve up to 25x faster inference directly on the shop floor, where defects can be addressed in real time. A separate Siemens deployment documented in R&D World showed an automotive OEM reducing unplanned downtime by 12% within 12 weeks of connecting more than 10,000 assets across four continents using Senseye Predictive Maintenance.

The predictive maintenance market is projected to grow from $10.93 billion in 2024 to over $70 billion by 2032, reflecting a compound annual growth rate exceeding 26%.

Industrial AI implementations must account for the specific demands of manufacturing environments. Edge deployment capabilities become critical for operations in remote locations or facilities with limited connectivity. Systems must integrate with existing PLCs, SCADA infrastructure, and ERP platforms while meeting regulatory and safety requirements. 

Custom solutions developed with industrial data integration expertise address these technical constraints while delivering production-ready analytics.

What defines a custom AI solution for enterprise automation

To be effective in enterprise automation, AI must be purpose-engineered, designed with the organization’s data, workflows, controls, and compliance frameworks embedded from the outset.

1. Domain-specific AI models

Custom solutions often extend or fine-tune large foundational models with proprietary data and business logic to ensure accuracy and relevance in domain tasks. This goes beyond generic training to include task-specific reasoning, industry taxonomies, and operational constraints.

2. Workflow orchestration

AI must do more than generate outputs. It must execute multi-step workflows:

• Automate decisions where business rules match data evidence
• Trigger human review loops when confidence is low
• Ensure audit trails and accountability by design

This orchestration layer serves as the navigator between AI predictions and enterprise systems.

3. Integration with core systems

Integrations with CRM, ERP, document repositories, compliance systems, and analytics platforms are central to delivering ROI and closing the loop between AI automation and existing enterprise processes.

4. Governance, security, and compliance

Custom solutions embed governance by default, including role-based access, explainability logs, policy controls, and anomaly reporting, to meet regulatory and risk standards.

5. Outcome-driven KPIs

The shift from experimentation to performance mandates operational KPIs rather than model metrics:

• cycle time reduction
  
• cost per transaction
  
• error rates and exception volume
  
• compliance pass rates
  
• real ROI dashboards monitored by business owner

Strategic recommendations for scaling enterprise AI

For manufacturing organizations:

1. Prioritize predictive maintenance: Focus initial AI investments on reducing the $2.8 billion annual downtime costs
2. Implement Edge Computing: Deploy AI systems capable of operating in remote manufacturing locations
3. Develop visual inspection capabilities: Leverage computer vision for real-time quality control
4. Create multi-agent systems: Design collaborative agent networks for complex production optimization

For financial services:

1. Enhance fraud detection: Invest in real-time transaction monitoring and pattern recognition
2. Deploy customer service agents: Implement virtual assistants to handle routine inquiries and reduce call center volume
3. Automate compliance processes: Use AI for KYC verification, AML surveillance, and regulatory reporting
4. Focus on identity management: Develop robust systems for managing both human and AI agent identities

Universal success factors:

1. Adopt the 10-20-70 framework: Invest 70% of resources in people and process transformation
2. Implement strong governance: Establish AI firewalls and security frameworks before scaling
3. Measure outcome-driven KPIs: Focus on operational metrics rather than model performance alone
4. Plan for multi-agent orchestration: Design systems that can evolve from single agents to collaborative networks

Conclusion: AI that drives enterprise value

The era of AI experimentation is giving way to performance-aligned custom solutions. CIOs and business leaders are moving beyond proof-of-concept to enterprise-grade deployment by engineering AI into business processes with governance, integration, and measurable outcomes at the core.

Custom AI solutions perform best when they address specific business problems with domain expertise, embed governance from the start, integrate with existing systems, and measure real operational outcomes. Whether the application is predictive maintenance, reducing million-dollar downtime incidents, or fraud detection protecting billions in transactions, the pattern is consistent: foundation first, technology second.

In 2026 and beyond, success will be determined not by how many AI tools you deploy, but by how your AI delivers measurable impact on business outcomes across the organization.

The post Custom AI solutions for enterprise automation: ROI benchmarks, use cases, and adoption trends appeared first on Xenoss - AI and Data Software Development Company.

According to PwC, EU-based banks are submitting 9.4% fewer suspicious activity reports despite a steady rise in fraud attempts, meaning more crimes go undetected.

To close this gap, banks are exploring machine learning capabilities to enhance legacy detection systems. 

In this post, we examine how malicious actors use AI to develop advanced fraud techniques, the technologies engineering teams can deploy in response, and key challenges to consider when implementing AI-enabled fraud detection.

Impact of financial fraud on banks

Christine Benz, Director of Personal Finance and Retirement Planning at Morningstar, recently shared on LinkedIn how scammers were using her personal data to lure consumers into bogus investments, just as she was warning her team about impersonation fraud. 

Market data reinforces her point: the scale and impact of financial crime are rising sharply.

In the US, consumers lose over $12 billion annually to identity fraud and other scams. In the UK, fraud accounts for 41% of all crime, costing the country over £6.8 billion per year.

As executives brace for more frequent and sophisticated fraud attempts, many are recognizing that existing systems can’t keep pace. Currently, only 23% of banking executives believe they have reliable programs to counter financial fraud risks. In the coming years, concerns of low fraud detection effectiveness are likely to grow as financial crime becomes increasingly AI-assisted and harder to detect.

AI is transforming common types of fraud

Fraud detection teams are under constant pressure to keep pace with rapidly evolving scam techniques. The rise of generative AI in financial crime is blurring the line between bot behavior and authentic user activity, until it is nearly impossible to tell the two apart.

The latest omni-channel models, like GPT-4o, Sora, and others, are making traditional schemes like phone and email phishing more effective and harder to spot, as well as enabling entirely new scam techniques.

According to Signicat, deepfake attempts increased by 2,137% between 2021 and 2024. In a separate report, financial executives noted that 50% of all fraud attempts now involve AI, with 90% expressing particular concern about voice cloning.

More concerningly, banks are adopting AI more slowly than the fraudsters themselves. Only 22% of surveyed institutions use any form of machine learning to detect financial crime.

To counter these advanced threats, banks and financial institutions need to embrace AI and predictive analytics, not only to improve detection accuracy but also to ease the burden on financial crime teams, which are now processing a deepfake attempt every 5 minutes on average.

AI technologies banks can use for fraud detection

Real-time predictive analytics for risk scoring

Fraud types it helps detect

• Card-not-present payment fraud
• Authorized push payment scams
• Synthetic identity fraud
• Account takeover–driven transfers
• Merchant or transaction laundering patterns

Predictive analytics for transaction risk scoring is the workhorse of modern fraud detection.

Engineering teams train supervised ML models on datasets that include labeled historical fraud logs, expert annotations, and chargeback outcomes. These models are then deployed to classify new events as normal or suspicious in real time.

Transaction scoring models combine multiple signal types: transaction attributes (amounts, velocity, merchants), customer context (tenure, typical behavior), and channel data (device, session) to reduce false positives and catch subtle fraud patterns. 

By improving detection at the first line of defense with fewer unnecessary declines, they directly protect both revenue and customer trust.

Real-world example: Natwest

Approach: NatWest, one of the UK’s largest retail and commercial banking groups, upgraded its payment-fraud controls to a real-time transaction risk-scoring platform built on adaptive machine learning models. The system learns normal behavior at the individual-customer level, integrates contextual signals like device profiling, and uses this data to accurately flag anomalous payments.

Outcome: The rollout delivered immediate, measurable gains, including a 135% increase in the value of scams detected and a 75% reduction in scam false positives. Across fraud more broadly, NatWest reported a 57% improvement in the value of fraud detected and a 40% reduction in overall fraud false positives.

Graph ML and identity resolution

Fraud types it helps detect

• Money mule networks
• Collusive fraud rings
• Shell-company laundering structures
• Linked synthetic identities
• Trade-based laundering networks

Financial fraud teams can use graph analytics to model financial crime as a network of entities (customers, accounts, devices, counterparties) connected by relationships (transfers, shared devices, common addresses, beneficial ownership).

Here’s how graph ML improves transaction profiling:

1. Entity resolution. Graph ML algorithms deduplicate and link records that represent the same real-world entity across messy, siloed datasets.
2. Behavioral mapping. Creating a graph of all actions linked to a single customer helps distinguish normal behavior from suspicious activity.
3. Pattern detection. Once a reliable graph exists, graph features and graph ML techniques (including graph embeddings and GNNs) expose coordinated behavior that appears normal in isolation but suspicious when viewed across the network.

Real-world example: HSBC

Approach: HSBC, one of the world’s largest multinational banks, adopted graph ML and entity-resolution technology to modernize its financial crime detection stack across AML and fraud use cases.

Engineers unified fragmented internal and external datasets: customers, accounts, counterparties, corporate registries, and transactions into a single, continuously updated entity graph. 

Advanced entity resolution linked records referring to the same real-world person or organization, while network analytics and graph-based features exposed hidden relationships, mule networks, and complex laundering structures that transaction-by-transaction analysis would miss.

Outcome: Following the rollout, HSBC reported £4 million in potential cost savings from replacing its incumbent system while improving analytical depth and investigative efficiency.

By providing investigators with a contextual, network-level view of risk, the bank reduced manual reconciliation effort, accelerated case resolution, and scaled financial crime monitoring more efficiently across regions and business lines.

Unsupervised anomaly detection for anti-money laundering

Fraud types it helps detect

• Novel money laundering typologies
• Suspicious SWIFT and correspondent patterns
• Trafficking- and exploitation-linked flows
• Structuring and smurfing behaviors
• Previously unseen scam “playbooks.”

Unsupervised anomaly detection learns baseline “normal” behavior from data without requiring labeled fraud examples. 

Semi-supervised approaches combine this with limited labels to improve precision. 

Both are valuable in AML, where labeled data is sparse, and typologies evolve faster than rule-based systems can adapt.

The practical impact of unsupervised anomaly detection is seen in earlier detection of emerging patterns and reduced reliance on brittle rules. It also reduces the need for human review and cuts case queues by shrinking false positives.

Real-world example: Santander

Approach: Santander, a global banking group based in Spain, integrated an unsupervised anomaly detection solution into its transaction monitoring to enhance AML and financial crime screening across its operations.

Rather than relying on static thresholds and rules, the system models normal behavioral patterns across millions of transactions and flags statistical deviations that could indicate complex criminal activity, particularly typologies that traditional systems struggle with, such as human-trafficking-linked payment patterns and subtle money flows.

The AI ingests historic and ongoing transaction data to establish dynamic behavioral baselines, enabling earlier detection of abnormal sequences that would otherwise blend into noise under legacy rule-based systems.

Outcome: By deploying unsupervised anomaly detection, Santander achieved significant reductions in false positives. In some jurisdictions, the bank saw over 500,000 fewer unnecessary alerts per year.  

NLP for screening, KYC/AML enrichment, and alert triage (names, watchlists, adverse media, narratives)

Fraud types it helps detect

• Sanctions and watchlist evasion
• Identity fraud via aliasing and transliteration
• Hidden beneficial ownership signals in text
• Adverse-media-linked financial crime risk
• High-risk onboarding and KYC inconsistencies

NLP applies language models and text-mining methods to the unstructured data that fraud and compliance teams rely on: names, addresses, corporate registries, adverse media, and investigator notes.

Modern NLP approaches allow teams to learn from historical analyst decisions, generate consistent recommendations, and provide written rationales that speed up alert disposition. 

A deeper understanding of context around customer interactions helps fraud detection systems produce fewer false matches, make faster screening decisions, and handle large volumes of multilingual, messy real-world identity data.

Real-world example: Standard Chartered

Approach: Standard Chartered, a major global bank, enhanced its financial crime compliance operations by integrating NLP and machine learning–based name screening and alert-triage technology into its sanctions, watchlist, and adverse-media screening workflows.

The system uses two key components: 

1. NLP models that interpret names, aliases, addresses, news, and watchlist sources 
2. Machine learning algorithms that replicate human screening decisions. 

It continuously learns from historical analyst decisions, enriches alerts with contextual signals, and generates explanations that help compliance teams understand and act on risks more quickly and consistently.

Outcome: After deployment across 40+ markets, the solution delivered dramatic reductions in manual workloads and false positives. The AI-driven screening system automatically resolves up to 95% of false positive alerts, enabling compliance teams to focus on genuinely suspicious matches rather than low-risk noise.

AI agents for investigation automation

Fraud types it helps detect

• Sanctions screening alerts
• AML transaction-screening alerts
• Watchlist and PEP-related matches
• Cross-border payments linked to risk patterns
• High-risk customer and counterparty linkages surfaced during the investigation

Banks and financial institutions are increasingly implementing agentic workflows to handle end-to-end alert management.

AI agents can pull relevant customer and transaction context, evaluate whether an alert is likely a true match or false positive, generate a clear narrative explaining the rationale, and route the case while ensuring full auditability and human oversight.

In operational areas like alert triage and disposition, where volume and false positives overwhelm teams, agentic workflows reduce manual effort, standardize decisions, and accelerate time-to-resolution without weakening governance.

Real-world example: DNB

Approach: DNB, Norway’s largest financial services group, implemented intelligent AI agents to execute high-volume, compliance-critical work across financial crime and adjacent finance operations.

The company embedded hyper-specialized agents into pre-submission checks on stock transaction data and AML-driven remediation actions, such as terminating customers who failed to refresh required identification. 

To boost efficiency, DNB augmented these agents with APIs, OCR for document scanning, and ML-based keyword search for customer communications.

Outcome: AI agents are now involved in 230 processes, have returned over 1.5 million hours to the business, and saved €70 million, while eliminating AML errors within the targeted automation scope.

In one AML-related remediation, 90 AI agents processed 500,000 customer accounts to offboard non-compliant customers in time to meet a government deadline.

Challenges and risks of using AI for fraud detection

Despite hundreds of successful implementations of machine learning and generative AI, financial institutions should not underestimate the risks of letting AI agents and detection systems process sensitive customer data.

Understanding these risks helps internal engineering teams develop contingency plans and maintain regulatory compliance.

Overblocking and false positives 

Modern fraud detection models rely on anomaly detection and risk scoring across signals such as device fingerprinting, geolocation, transaction velocity, and behavioral deviation. 

When these algorithms are tuned conservatively or when downstream decision rules collapse nuanced scores into binary outcomes, they can over-trigger transaction blocks. 

The false positives generated by ML-enabled fraud detection tools may escalate to account freezes, interrupt legitimate access, and strain customer support and dispute handling.

In one such incident, Monzo, a UK-based online bank, blocked a customer’s account after its fraud detection systems flagged a new mobile device attempting access. The customer could not use their card or view their balance until they completed identity verification. To resolve the matter, Monzo paid 8% interest on the full account balance plus an additional £1,000 for the distress caused.

Isolated false positives may not cause significant monetary damage, but at scale, settling customer complaints and managing reputational fallout creates substantial operational and budget strain.

How to address this challenge: Organizations should accept some level of friction when applying transaction monitoring, but thoughtful implementation helps minimize negative impact.

Rather than initiating a full account freeze for a possible fraud attempt, institutions can implement softer verification methods. 

Here a few fallback strategies teams can implement: 

• Confirming intent in-app, 
• Limiting transaction size or destination
• Placing temporary holds while checks run in the background.

Operationally, institutions should support customers with clear explanations, predictable timelines, and a fast path to a human when automated checks fail.

Biometric and identity AI can be biased or inaccessible

Biometric checks such as selfie matching or liveness detection promise fast, low-friction identity verification. In practice, they don’t work equally well for everyone. Poor lighting, older devices, physical differences, or accessibility issues can all lead to repeated failures. 

These rejections can propagate into onboarding and account recovery flows, disproportionately affecting certain customer segments and creating fairness and accessibility risks.

How to address this challenge: Treat biometrics as a convenience, not a bottleneck. Banks should account for potential malfunctions by offering alternatives that let customers proceed with authentication or transactions. 

Fallback paths include: 

• document checks
• verified bank credentials
• assisted reviews. 

To improve customer experience across the authentication process, organizations should communicate upfront that these alternatives exist.

Additionally, financial institutions should monitor biometric check performance to identify failure conditions and adjust flows accordingly.

Data leakage and confidentiality risk when GenAI is used in fraud operations

Generative AI is increasingly used by fraud teams for case summarization, entity extraction, and investigative support, often requiring access to transaction data, internal notes, and SAR-adjacent context. 

Without strict controls on data ingress, retention, and model scope, these tools can inadvertently expose regulated or confidential information beyond approved boundaries. 

The risk is amplified when GenAI systems are integrated informally or outside established financial crime governance frameworks. 

This is a challenge for global financial organizations where employees may use off-the-shelf LLMs to streamline workflows without reporting to management. 

How to solve this challenge: Rather than restricting generative AI use and risking productivity slowdowns, successful institutions design GenAI as a controlled workspace. Organizations with access to top-tier engineering talent can build proprietary models trained on approved internal sources and compliant with industry-specific privacy regulations.

Morgan Stanley implemented this approach by deploying AI @ Morgan Stanley Assistant, an internal GenAI tool powered by OpenAI’s GPT-4. The assistant supports 16,000 financial advisors in the bank’s Wealth Management division, letting them query internal research, data, and documents in natural language. 

Rather than risk sensitive data leaking through consumer versions of ChatGPT, Morgan Stanley rolled out an enterprise-grade edition trained on a library of 100,000 internal documents.

Adversarial AI undermining fraud detection

Fraud prevention systems are increasingly confronting adversarial inputs generated by AI, including deepfake audio and video, synthetic identity documents, and algorithmically generated behavioral patterns. 

These artifacts are designed specifically to exploit model assumptions and bypass automated verification layers.

DBS, a Singapore-based bank, faced this challenge directly when scammers created deepfake videos of the bank’s executives to lure customers into investment scams. The bank was forced to issue a public warning to protect customers from engaging with AI-generated content on social media.

This and similar incidents are proof that traditional trust signals—visual identity checks, voice confirmation, static documents—are losing reliability, forcing detection systems to operate in an increasingly hostile and adaptive threat environment.

How to solve this challenge: As fraudsters exploit generative AI to create complex, hard-to-detect scams, financial crime teams must accept that traditional verification signals like a face, a voice, or a document can now be faked.

One-touch identity checks are no longer reliable. Instead, teams should prioritize layering customer behavioral context over time: understanding how a user typically behaves, which devices they trust, how a transaction compares to their normal patterns, and whether multiple independent signals align. 

This approach offers a more robust defense against deepfakes than any single verification checkpoint.

Bottom line

As AI becomes more accessible, financial fraud groups are leveraging cutting-edge models to bypass traditional identity controls, execute illegal transactions, and lure bank customers into fraudulent investment schemes.

To stay ahead of malicious actors, financial institutions must intentionally deploy AI in fraud detection. 

Supplementing existing transaction scoring and identity controls with tools like graph ML for added context or intelligent AI agents for automation improves both detection accuracy and investigator productivity.

At the same time, given the sector’s sensitive nature, banking teams need to ensure their AI tools remain compliant, carefully validate detection models to reduce false positives, and keep humans in the loop for edge cases. Balancing AI-driven analysis and automation with thoughtful human oversight allows institutions to adopt innovative fraud detection tools while minimizing risk to customers.

The post Finance fraud detection with AI: A complete guide appeared first on Xenoss - AI and Data Software Development Company.

In regulated industries, organizations process up to 250 million documents each year across claims, underwriting, onboarding, and invoicing workflows. Documentation gaps in any of these workflows become compliance risks that surface later as audit findings, denied claims, and abandoned applications.

Automation has helped with throughput, but regulators now expect evidence-grade data: proof that every decision ties back to the correct source, that nothing critical is missing, and that extracted data is accurate. That’s a higher bar than most document capture systems were built to clear.

Therefore, investments shift from throughput-focused automation toward compliance-driven document processing, where document intelligence validates completeness, checks consistency, and flags problems before they propagate into core systems. 

The sections below break down how this works across insurance claims, underwriting, banking onboarding, and manufacturing invoicing, with practical benchmarks for evaluating compliance-ready initiatives.

Document capture vs. intelligent document processing 

Standard document capture focuses on field-level extraction. However, regulated workflows require traceability to the right source document. 

A claim file missing an adjuster note or a valuation report with mismatched fields might pass through a capture pipeline without a “system error,” but fail an audit. That same mismatch can route a claim straight to adjudication, only to trigger a denial-and-appeal cycle that costs more to resolve than the original claim.

In health insurance alone, 19% of in-network and 37% of out-of-network claims were denied in 2023, with documentation gaps cited as a leading cause.

Document processing for regulated industries reduces these risks by:

• Validating packet completeness upfront
• Checking document consistency
• Flagging missing or outdated evidence before a case reaches a core system.

Core components of document intelligence for regulatory compliance

Document intelligence relies on a defined set of components designed to meet regulatory expectations for accuracy, traceability, and control.

Data extraction with confidence scoring

Every extracted field carries a confidence score, typically expressed as a probability between 0 and 1.

A service date pulled cleanly from a structured form might score 0.98; the same field handwritten on a faxed document might score 0.62. That score determines what happens next: high-confidence values move straight through, while low-confidence values route to human review.

Rossum’s automation framework, for example, uses a default threshold of 0.975, meaning documents are auto-exported only when the system is at least 97.5% confident in each extracted field.

Confidence also rolls up to the packet level. If three of twelve documents in a claim file have low extraction confidence, the system flags the entire submission for intake review before it enters adjudication.

Document governance across ingestion, review, and release

Governance defines what happens before data reaches core systems. 

Validated ingestion channels ensure documents enter through approved sources. File-type and format checks reject submissions that don’t meet requirements. Role-based review enforces segregation of duties, so the same person can’t submit and approve a case.

Override controls matter here, too. When a reviewer changes an extracted value, the system requires a rationale, logs the change, and locks the record against silent edits.

Accuracy metrics for regulated document workflows

Overall accuracy numbers can be misleading. A system might report 96% extraction accuracy, but if errors concentrate in high-impact fields like claim amounts or policy dates, the operational risk is much higher than that number suggests.

In ML terms, precision measures the proportion of correct positive predictions, while recall measures the proportion of positives the model identified. The F1 score balances these two metrics into a single number.

In document processing terms:

• Precision: How often the system’s extracted values are correct.
• Recall: Did we find all the values we were supposed to find?

Mature programs track this specifically in critical fields, calibrating the trade-off between false acceptance (bad data that gets through) and false rejection (good data that gets flagged unnecessarily).

End-to-end visibility across document packets

Regulated decisions rarely depend on a single document. 

A claim file might include bills, clinical notes, adjuster reports, and policy documents. An underwriting submission might combine inspection reports, loss histories, and financial statements.

Packet-level visibility ensures completeness across all required documents, checks that identifiers align (same claimant, same policy, same dates), and surfaces inconsistencies before the case moves downstream.

Document intelligence for insurance claims

Insurance claims operations run on documentation: itemized bills, clinical notes, adjuster reports, policy records, and supporting evidence that arrives in dozens of formats. Small inconsistencies in any of these documents can determine whether a claim moves straight through to payment or falls into an exception queue that takes weeks to resolve.

Nearly 15% of all claims submitted to payers for reimbursement are initially denied, and 77% of those denials stem from paperwork or plan design rather than medical judgment. Administrative issues account for 18% of in-network claim denials.

Estimates show that hospitals and health systems spend $19.7 billion annually on fighting denied claims, at an average cost of $47.77 per claim. More than half of those denials (51.7%) are eventually overturned and paid, meaning billions go toward resolving claims that should have been approved in the first place.

Document intelligence addresses this by catching problems before they trigger denials.

Claim packet assembly and completeness validation

Each line of business carries its own documentation requirements. A workers’ compensation claim needs different evidence than a health insurance claim; an inpatient stay requires a discharge summary that an outpatient procedure wouldn’t.

Document intelligence models these requirements as a library of expected and conditional documents. 

When a claim arrives, the system classifies each document, attaches it to the correct record, and evaluates the packet against completion rules. If an inpatient claim is missing a discharge summary, it flags immediately rather than waiting for a reviewer to notice.

This produces a packet-level completeness score. High-completeness claims flow straight into adjudication. Low-completeness claims route to intake review with specific prompts: “missing wage statement” or “adjuster note not attached.” 

Since 45% of providers cite missing or inaccurate data as their top cause of denials, upfront completeness checks are among the highest-leverage interventions available.

Data lineage and versioning for audit trails

Regulators expect every decision to be reproducible. Data lineage tracks three things

Origin: Where did the value come from?

Transformation: How was the data cleaned or mapped?

Human intervention: Who approved an override and why?

This makes outcomes auditable. When a reviewer modifies an extracted field, the system logs the original value, the correction, the rationale, and the reviewer ID.

Banks that have adopted modern data lineage tools report 57% faster audit preparation and roughly 40% gains in engineering productivity. 

Extraction accuracy and claims adjudication alignment

Even minor extraction errors can alter outcomes. For example, a misread service date can shift a claim outside the coverage window or trigger the wrong prior-authorization rule.

Up to 49% of claims are affected by routine coding and documentation issues. Document intelligence reduces this by cross-checking extracted values against other evidence in the packet: 

• Service dates validated across clinical notes and billing records
• Billed amounts reconciled against itemized line items
• Procedure codes checked against supporting clinical documentation

When values don’t match, the system routes the claim for review with a clear explanation rather than allowing it to proceed to adjudication, where it’s more likely to be denied.

As a result, claim files remain fully reproducible during audits, with complete version histories and transformation logs.

Document intelligence for underwriting

Underwriting depends on interpreting evidence (inspection reports, valuations, financial statements, etc.) and turning that evidence into consistent, defensible risk assessments. Unlike claims, underwriting is not about adjudicating a past event, but about predicting future loss. That prediction is only as reliable as the documentation behind it.

Manual document review remains a bottleneck. Industry data shows that underwriters spend up to 40% of their time on administrative tasks, including gathering and verifying supporting documents. For commercial lines, turnaround times for standard policies have dropped from 3-5 days to as little as 12.4 minutes with AI-assisted processing, provided extraction and validation are tightly integrated.

Extracting structured insights from supporting evidence

Underwriters rely on diverse documents, such as loss histories or property photographs, that were never designed for automated processing. Underwriting document automation transforms these materials into structured inputs by classifying each document, extracting key attributes, and validating them against business rules. For example, inspection reports are parsed for building characteristics, and financial statements yield revenue, debt, and liquidity indicators.

Building reliable documentation chains

Underwriting files must show how a conclusion was reached. Document intelligence links extracted values to their source pages, maintains version histories, and records reviewer adjustments. During peer review or audit, underwriters can replay the file to see which evidence supported a pricing decision and why conflicting information was resolved a certain way.

Reducing decision variability across underwriters

Variation between underwriters is a well-known source of pricing inconsistency. By standardizing document classification, completeness checks, and extraction logic, document intelligence ensures that every submission enters review with the same normalized evidence set.

Document intelligence for banking onboarding and KYC automation

In the 2025 Fenergo global survey, 70% of institutions lost clients in the past year due to inefficient onboarding, with abandonment rates averaging around 10%.

Corporate client onboarding is particularly slow: full KYC reviews take an average of 95 days. Document intelligence helps reduce this drop-off by standardizing how evidence is captured, checked, and reconciled across the KYC process.

Structured validation of ID documents and supporting materials

Document intelligence classifies each document, extracts regulated fields, and validates them against jurisdiction-specific rules: 

• ID expiration dates
• MRZ consistency
• Issuer authenticity
• Address-issuer validity
• Income-document coherence
• Ownership declarations 

that must match corporate registries or supporting evidence.

These checks reveal issues that basic extraction misses: outdated IDs, incomplete address proofs, or income statements that contradict declared information.

Mismatch detection and risk signaling

Extracted fields from IDs, proofs of address, income statements, and declarations are cross-checked against each other and against external records. When values diverge (name variations, addresses that don’t match public records, ownership that conflicts with company filings), the system raises a structured alert and routes the case for additional review.

Audit-ready onboarding trails

What makes onboarding audits difficult is showing exactly which version was used when a decision was made, how discrepancies were resolved, and why a reviewer cleared a risk flag.

Document intelligence creates an audit-ready onboarding record by preserving every document and decision in a reproducible, time-indexed chain. Each upload becomes an immutable version with provenance metadata; extracted fields are stored alongside the model version and validation rules used to generate them; and every reviewer action is time-stamped and linked to an individual.

Document intelligence for invoice automation in manufacturing

Manufacturing invoicing looks structured on paper, but in practice, formats vary by vendor, plant, and region. Quantity and price discrepancies, missing receipts, and incorrect cost centers all create exceptions that finance and plant teams must resolve manually.

Across industries, accounts payable teams still spend about $9–10 to process a single invoice, with cycle times averaging 9.2 days and invoice exception rates around 22%.

Line-item validation and structured reconciliation

Document intelligence normalizes vendor-specific invoice layouts into a consistent schema, then applies PO-based and three-way matching rules at the line level. 

Quantities, unit prices, tax amounts, and freight charges are checked against purchase orders and goods receipts within defined tolerances. This catches issues such as overbilled units, duplicate freight, or misapplied discounts before the invoice is posted.

Cross-system lineage for financial accuracy

Each line item that passes validation ultimately feeds ERP, AP, inventory, and forecasting systems. Document intelligence maintains lineage from invoice line to PO line, receipt, and GL posting, so controllers and auditors can see precisely how a billed amount flowed into COGS, accruals, or capital projects.

Discrepancy tracking and exception clustering

Not all issues are one-off errors. Some vendors systematically overinvoice freight,  certain plants may miscode cost centers, and specific product lines may have recurring mismatches between shipping documents and invoices.

By aggregating and clustering exceptions, document intelligence highlights these patterns: which vendors generate the most mismatches, and which plants or buyers approve out-of-tolerance invoices.

Document intelligence benchmarks: accuracy and efficiency gains by workflow

The table below summarizes typical performance bands used in enterprise evaluations of document intelligence programs.

Workflow	Extraction accuracy	Efficiency impact	Cycle-time improvement
Insurance claims	95-99% (vendor-reported; varies by document type)	STP rates for low-complexity claims: 30-40% achievable, up to 95% potential for P&C	25-40% faster for simple claims
Banking onboarding	92-97% (document-dependent)	40%+ of onboarding time consumed by KYC/account opening	Baseline: 49 days average (commercial); improvement varies by maturity
Manufacturing invoicing	Measured by exception rate and touchless processing	Touchless rate: 23.4% average → 49.2% Best-in-Class; Exception rate: 22% → 9%	7.4 → 3.1 days (82% faster for Best-in-Class)

Architectural requirements for audit-ready document intelligence

Architecture determines whether extracted data holds up under regulatory scrutiny months or years after a decision. Three layers matter most.

Controlled ingestion and extraction

Documents entered through validated channels are checked for format and integrity, and the system rejects or flags inputs that fail prerequisites. 

At the extraction layer, every transformation is logged and versioned. Each field ties back to a source page, extraction logic, model version, and timestamp. Reprocessing the same document under the same configuration must yield identical results.

Governance and lineage

The governance layer maintains end-to-end traceability from source document to decision input, records reviewer actions and overrides, and enforces segregation of duties. Overrides require justification, approval, and permanent audit trails.

Ongoing accuracy monitoring

Document formats change, vendors update templates, and rules evolve. Mature programs track discrepancy rates on high-impact fields (amounts, dates, identifiers) rather than headline accuracy alone. A rise in discrepancies signals degradation before overall metrics show it. Override patterns, such as frequent fixes to the same fields or document types, identify gaps in extraction logic. Model updates undergo formal retraining cycles, are tested on validation sets, and are versioned for auditability.

Conclusion: Document intelligence as a compliance multiplier

In regulated industries, document processing is a foundation for defensible decision-making. Accuracy, completeness, and traceability now determine whether claims are paid correctly, risks are priced consistently, clients are onboarded compliantly, and invoices are approved without downstream disputes.

Document intelligence reframes automation around these requirements. By combining field-level accuracy metrics, document lineage, and embedded governance controls, organizations can drive cycle-time reduction in document workflows while limiting downstream rework.

As regulatory scrutiny increases, this compliance-first approach turns document processing from a source of risk into a measurable, scalable advantage across claims, underwriting, onboarding, and invoicing.

The post Document processing and intelligence for regulated industries: Claims, underwriting, onboarding, invoicing appeared first on Xenoss - AI and Data Software Development Company.

For enterprises generating thousands of invoices monthly, these inefficiencies magnify into significant operational costs and risks:

– Vendor relationship damage from delayed payments

– Compliance exposure from manual errors

– Missed revenue and productivity from staff time diverted to manual work 

– Growth constraints from non-scalable processes and fragmented tooling

Industry research indicates that automation is a practical lever for the finance sector. 

According to McKinsey data, automation can help finance teams reach over 90% straight-through processing rates, compared to the current 50% industry average.

Deloitte reports that automated reconciliation reduces errors by 75% and accelerates financial close by 2-4 days. 

That said, traditional automation approaches, such as rules-based systems and simple AI tools, struggle with the complex invoice processing cases, like overpayments and invoice-to-receipt mismatches.

In these cases, a network of specialized AI agents, controlling every step and catching edge cases, outperforms ‘vanilla automation’. Сompound systems are more accurate (66% vs. 55% for single agents) and have better reasoning benchmark scores (3.6 vs 3.05). 

However, orchestration comes with latency and infrastructure cost challenges. In the same comparison, single agents produced outputs in 61 seconds, whereas compound systems needed 325 seconds. 

To demonstrate how to build and optimize compound AI systems for invoice reconciliation on the Databricks Data Intelligence Platform, we’ll share architectural decisions, cost optimization strategies, and performance outcomes. 

From a production implementation that reduced processing time from days to minutes while maintaining enterprise-grade governance and auditability.

Why Databricks for a compound AI system 

Our multi-agent invoice reconciliation system runs on Databricks for several practical reasons. 

1. Purpose-built agent tooling. Databricks’ Mosaic AI Agent Framework and Agent Evaluation provide native support for multi-agent orchestration with built-in testing capabilities. 

This eliminates the complexity of integrating multiple third-party tools and enables systematic evaluation of agent performance across the entire workflow.

2. Reliable retrieval on unstructured data. Databricks Vector Search is optimized for unstructured content, which is particularly important because most invoices arrive as PDFs. Accurate retrieval was crucial for matching invoices, receipts, and exceptions without relying on brittle heuristics.

3. Enterprise governance and lineage. Unity Catalog provides attribute-based access control and automatic data lineage tracking across all agents and datasets. 

For financial services organizations, this built-in governance eliminates the need for custom audit trail implementations. 

4. Unified platform architecture. Rather than stitching together separate tools for data ingestion, model serving, workflow orchestration, and monitoring, Databricks provides these capabilities within a single platform. 

This reduces integration complexity, minimizes data movement costs, and simplifies troubleshooting across the entire compound AI pipeline.

Compound AI delivers value only when data, orchestration, and governance live in one place. On a unified platform like Databricks, shipping use cases like invoice reconciliation, exception handling, and compliance reporting is faster and has fewer moving parts. The scalability and robust capabilities help turn prototypes into reliable enterprise outcomes. 

— Dmitry Sverdlik, CEO, Xenoss

Architecture and cost optimization for compound AI reconciliation

Building compound AI systems requires careful architectural decisions and cost management strategies. 

Each agent in our reconciliation pipeline was designed with specific performance and economic constraints in mind.

Data ingestion

The primary challenge in invoice reconciliation involves processing diverse, high-volume data sources, including invoices, purchase orders, statements, receipts, and vendor communications, all in multiple formats. 

To build a cost-effective ingestion pipeline, the engineering team prioritized:

• Autoscaling on new arrivals to prevent idle compute from burning the budget.
• Creating source-faithful, replayable raw copies for audit and replay scenarios.
• Capturing rich metadata (sender, system of origin, timestamps, checksums).
• Tolerating schema drift (new columns, attachment types, EDI segments) without outages.
• Exposing stable data contracts for downstream agent consumption.
• Preserving lineage and access control that auditors and contractors can navigate.

Data ingestion with the Databricks ecosystem

Our invoice ingestion pipeline leverages Databricks Workflows, Auto Loader, and DLT to automatically collect, process, and store data from multiple sources with built-in error handling and schema management.

Workflows run on a 30-minute schedule and fire in response to event triggers (file arrival).

Parallel Workflows tasks poll each data source: Gmail invoice mailboxes, SFTP servers, ERP export APIs, and vendor portals. A coordinating Workflow standardizes error handling, and a successful uploads trigger the incremental load.

Auto Loader ingests new objects incrementally into Delta tables, maintains checkpoints, and handles schema inference and evolution automatically.

A Bronze layer keeps a verbatim, defensible record with complete metadata. 

Delta Live Tables (DLT) enforces deduplication and constraints to ensure downstream agents receive clean data without duplicates.

TCO considerations for the Databricks ingestion setup

Our key TCO consideration was minimizing waste from upstream volatility by stopping DBU churn from failed retries and cutting per-request Model Serving calls on non-actionable payloads.

We were looking for ways to profile cost hot spots (retry storms, reprocessing, unnecessary inference) and redesign the ingestion path to filter inputs early and only escalate clean, schema-vetted data. 

With that in mind, the engineering team implemented a few architectural considerations. 

Adopting a “rescue first, promote later” approach to schema evolution. Unexpected changes in vendor exports and EDI can disrupt ingestion jobs, resulting in a series of failed retries that burn DBUs and then require additional costs for reprocessing. 

To avoid this, route unknown attributes to the Auto Loader’s rescued data column, and then run a “schema steward” task to inspect and approve the rescued fields. 

To eliminate non-invoices from passing down the pipeline, we set up microfilters before passing tasks over to the capture agent. A Workflows task that uses MIME allowlists, size thresholds, and filename heuristics to filter logos or signatures and filter only elements that look like invoices. 

These tweaks created significant compound savings on Model Serving costs, which are calculated per request. 

Business outcomes

The optimized ingestion pipeline delivered measurable improvements across key performance indicators.

Combining time-based scheduling with event-driven processing reduced time-to-post from 9 to 4 days. A robust metadata layer with stable data contracts minimized duplicate records passed to downstream agents, increasing straight-through processing by 12%. 

Auto Loader checkpoints that reduce idle compute consumption decreased DBU usage per 1,000 processed records by 27%. 

Pre-filtering non-invoice content through MIME validation, file size thresholds, and filename heuristics reduced unnecessary processing overhead for downstream AI models by 40% at current data volumes.

Step 1. Invoice capture

Invoice capture represents the highest-risk component of the reconciliation pipeline. Errors here cascade through all downstream agents, making accuracy, scalability, and reliable deployment practices critical for system performance.

The Capture agent processes invoice documents using specialized OCR and extraction models trained on financial document formats. When confidence scores fall below predefined thresholds (typically 85% for critical fields like amounts and vendor information), the system automatically routes invoices to human reviewers with specific guidance on required validation.

The capture process handles diverse input formats—PDFs, scanned images, photos, and EDI files, through a multi-stage pipeline: document classification, OCR processing, field extraction, and line-item parsing. This multi-modal approach ensures consistent data extraction regardless of how vendors submit their invoices.

Databricks tools supporting the Capture agent

Serverless Model Serving provides a low-latency document processing that scales automatically with invoice volume while avoiding “always-on” compute costs. The autoscaling endpoints ramp up resources when new invoice batches arrive and scale down during idle periods.

MLflow Model Registry versions every change (OCR parameters, fine-tuned extractors, next-gen models) and allows engineers to promote or revert after accuracy/calibration review, so iteration never jeopardizes operations. MLflow enables cohort-specific models that route invoices to pipelines optimized for specific vendor formats (e.g., non-standard document layouts or complex multi-page invoices). 

Delta Live Tables with Expectations reads capture outputs, materializes silver tables, and enforces type, range, semantic, and referential checks. 

Records that pass the data quality check flow straight to Normalization and Matching. Records that fail land in a quarantine table with machine-readable reasons and flagged low-confidence fields, which automatically create human-in-the-loop tasks (e.g., “Low confidence regarding invoice_total”).

This architecture delivers a capture layer that stays fast under load, aligns spend with demand, and produces auditable, high-quality inputs for the rest of the reconciliation workflow.

TCO considerations for building an invoice capture agent in Databricks

For data capturing, we focused on squeezing down inference spend per document to avoid unnecessary model calls, cut re-runs, and keep GPU/DBU usage predictable under bursty loads. 

Monitor budget and pre-endpoint cost attribution. To keep infrastructure costs lean, our engineering team tracked DBU spend, QPS, and latency per serving endpoint, using tags mapped to teams and suppliers. Instant detection of overloaded endpoints prevented multi-day cost overruns. 

Set rate limits for OCR endpoints. We added QPS ceilings per user to flatten activity bursts, reduce the financial burden of load tests or agent storms, and keep infrastructure spend predictable. 

Use tiered model routing by directing standard invoice formats to lightweight general models while routing complex or non-standard formats to specialized vendor-specific models. This reduced per-invoice inference costs because the majority of invoices use “cheap” compute, while high-accuracy endpoints were only called on demand. 

Prevent small file writes. Tuning batch sizes and trigger intervals prevents the extractor from creating small files that increase metadata overload and read I/O for every downstream agent. Larger files reduce DBU consumption and improve query performance.

How AI-enabled invoice capture improved reconciliation outcomes

Cohort-specific models deployed through MLflow significantly improved extraction quality for critical fields: supplier data, dates, totals, and tax information, with validation error rates below 2%.

Setting up data quality checks in DLT Expectations improved confidence calibration, with expected calibration error (ECE) dropping from 0.12 to 0.05. 

On a broader scale, an improved invoice capture pipeline helped cut total AP cycle time from 9 to 4 days thanks to serverless autoscaling endpoints, event and time triggers, and instant exception routing. 

Step 2. Data normalization 

The Normalization agent receives structured outputs like invoice headers, line items, confidence scores, and raw vendor identifiers from the Capture stage and transforms them into canonical business entities. 

This process involves standardizing currencies and amounts, applying tax logic, enforcing consistent units of measure, and mapping vendor strings or IDs to unified canonical entities.

Invoice normalization with Databricks 

On Databricks, the pipeline runs in Delta Live Tables (DLT), where Expectations enforce quality checks before records move downstream. 

We express business logic in SQL for joins, windowing, aggregates, and invariants, and use PySpark when we need richer programmatic control, like complex conversions or jurisdiction-specific legal lookups.

Tax policy is centralized and governed by user-defined functions (UDFs). It’s a single source of truth that the Normalization agent calls to navigate rate tables, determine whether a jurisdiction is tax-inclusive, and apply the correct rounding mode. Because these UDFs are shared across pipelines, invoice totals are computed consistently regardless of source.

A recurring challenge is vendor identity drift across regions (e.g., “International Business Machines Corporation” vs. “IBM Italia S.p.A.”). VAT/tax IDs are the preferred deterministic keys, but in edge cases, they may be missing or corrupted. 

To increase recall without hard-coding name variants, we add a semantic layer using Mosaic AI Vector Search. The vector index is auto-synced with Delta tables and governed in Unity Catalog, and it can be queried using multiple signals (names, addresses, email domains, bank accounts). This change-aware approach reduces scanned bytes, minimizes downstream cache churn, and prevents Delta log growth. 

TCO considerations for the Invoice normalization agent in Databricks

When building the agent, we had to watch out for wide joins, repeated passes over the same data, and costly external lookups that ballooned DBUs. 

We took three steps to prevent these events and slash TCO for data normalization. 

Implement incremental normalization. Rather than reprocessing all daily data, the agent only recomputes invoices with changed inputs from reviewer corrections or field updates. This change-aware approach reduces scanned bytes, minimizes downstream cache churn, and prevents Delta log bloat.

Use two-layered vendor validation: deterministic-first, semantic-later. The agent runs deterministic checks (exact matches on tax IDs or stable fields) before expensive semantic searches. Most vendor aliases resolve through simple matching. Reserve vector search for failed deterministic searches, with QPS caps and human-in-the-loop fallbacks to prevent repeated expensive queries.

Move expensive checks offline. Keep inline validation narrow (type compliance, required fields, vendor ID checks). Run heavy or low-yield checks in separate daily jobs that write to dedicated tables rather than blocking hourly processes.

How a Normalization agent optimizes invoice reconciliation

Introducing an intelligent normalization agent helped reduce errors and increase straight-through processing (matching with no human oversight) by 12%. 

Intelligent vendor aliasing cut false positives by 40% and cut the total number of vendor duplicates in master data to 0.5% of the total. Tax discrepancy defects dropped by 55% after the engineering team created a single source of truth for tax rates. 

Step 3. Invoice data matching

The matching layer that executes company policy deterministically, reacts to late-arriving receipts, and keeps an auditable trail, so most invoices are auto-approved, edge cases are surfaced with context, and only actual variances reach humans.

The Matching agent automates the reconciliation by retrieving POs, receipts, and ERP entries. It approves every incoming invoice in accordance with the company’s policy, including two-way or three/four-way matching. 

The Matching agent can yield three outcomes: 

• Approved
• Flagged for policy acceptance/review
• Variance raised for human decision.

Data engineering toolset for invoice matching built with Databricks

On Databricks, policy is encoded as set-based SQL over Silver (normalized) Delta tables, making decisions transparent, scalable, and easy to audit. 

Workflows orchestrate the process in an event-driven way: a job fires only when a normalized invoice arrives in SILVER, and listeners monitor receipt updates (since invoices often arrive first), automatically queuing items marked awaiting receipts.

For real-time context in borderline cases, the platform connects to ERPs via native connectors where available and RPA bridges for legacy systems without APIs. 

This two-way link enables the agent to both retrieve fields needed for reconciliation and attach evidence (e.g., service acceptance documents) to the ERP record. 

As a result, a policy-driven matching process runs on change instead of a timer, minimizing reprocessing and keeping every decision traceable.

Databricks TCO considerations for building a reconciliation matching agent

We wanted to keep matching costs linear and predictable, which is why the engineers decided to compare only what changed today instead of rescan the entire ledgers. 

We noticed that the biggest budget leaks came from reprocessing full tables, uneven join keys that cause expensive shuffles, and scoring lots of unlikely record pairs.

Here is how we fixed this problem and built a cost-effective reconciliation matching agent. 

Materialize open-receivable states. We converted window aggregations into O(1) lookups to reduce shuffle volume and executor memory usage. 

Set up ERP/RPA evidence cache with TTL and batching. ERP and RPA connections are compute-intensive. Caching results to reduce repeated reads solved this problem, and batching kept per-call overhead under control. 

Use persistent match bindings. We created an input hash for invoice lines and reused decisions from prior lines unless the input hash changed. When it did, engineers evaluated only the specific line and appended the new version to the existing records. 

How the Matching agent contributed to higher reconciliation efficiency 

Intelligent matching helped APs spend less time handling exceptions: 10 minutes on average compared to 28 minutes per invoice before the introduction of the new system. 

Infrastructure cost optimization techniques like persistent bindings reduced DBUs per 1k invoices by 25%. Evidence caching with TTL brought RPA reads per 1000 invoices down by 30%. 

Step 4. Variance resolution

In a variance workflow, which is policy-consistent and auditable by design, routine discrepancies are resolved automatically, reviewers see only well-contextualized edge cases, and each decision strengthens the system’s future reasoning.

The Variance resolution agent, notified about invoice discrepancies by the Matching agent, classifies the variance, explains the likely root cause, recommends (or executes) the proper fix, and leaves a complete audit trail.

How Databricks tools support an agent for variance resolution 

On Databricks, the variance-resolution loop runs inside the Mosaic AI Agent Framework, where granular permissions, preconditions, and a traceable event log enforce policy before any action is taken. When the Matching agent flags a discrepancy, the Variance agent is invoked to investigate.

The agent first classifies the variance type (e.g., a price variance within a discretionary band) and reviews similar prior cases and outcomes, such as adjusted receipts, updated prices, blocked payments, or re-invoicing. It then recommends corrective actions by combining deterministic finance rules with patterns learned from previous resolutions. Low-impact fixes are executed automatically; higher-impact or ambiguous cases are routed for human review.

For human-in-the-loop reviewers, work is conducted in DBSQL/Lakeview dashboards that present each variance with its type, retrieved similar cases, deltas, and the system’s recommended next steps. After a decision is made (e.g., approving a correction or escalating to the buyer), the input is versioned and written back to the agent. 

The agent re-evaluates the outcome and records human choices to strengthen future recommendations, while the framework’s event log preserves an auditable trail end-to-end.

TCO considerations for building AI-enabled variance resolution in Databricks

Invoking high-performance models to address variance issues that could be solved deterministically would drive TCO, paradoxically reducing resolution accuracy (LLMs are significantly more unpredictable than simple heuristics). 

That’s why we set up guardrails to make sure the agent only escalates variances to AI when deterministic rules can’t solve the problem. 

The agent auto-resolved repeated exceptions. Creating a list of recurring variance patterns and their outcomes helped detect similar exceptions and short-circuit them. 

This approach cuts the total number of Vector Search and LLM calls, simplifies the pipelines, and reduces human involvement in HITL validation. 

We adopted tiered reasoning to classify all detected issues. Simple variances were addressed through deterministic policy rules, based on historical data. 

Only if these systems fail should an LLM Advisor-powered agent step in. This approach conserves LLM calls and tokens, adds a layer of predictability to the system, and enables faster resolution for less complex variations. 

The Variance resolution agent contributes to higher reconciliation efficiency

1.2 days is the new variance closure time, down from 2 days (60% reduction), achieved through combined deterministic and AI-powered reasoning that resolves repeated variances while focusing compute on edge cases. 

47% reduction in cost per variance check resulted from tiered reasoning, QPS limits, and infrastructure optimizations.

12 minutes is the average time APs now spend reviewing exceptions per variance, down from 35 minutes, despite humans remaining part of the HITL pipeline.

Step 5. Invoice posting

In a posting workflow, policy decisions are converted into ERP transactions and scheduled payments consistently, accurately, and on time. Routine postings run automatically, while edge cases carry the necessary evidence for swift review, and every action leaves a clear record.

The Posting agent takes the outcome from matching and variance resolution, then creates the ERP transaction and payment run. 

It calculates due dates, discount windows, payment blocks, and preferred payment cycles based on vendor terms, treasury rules, cutoff times, and the holiday calendar. It also produces remittance details and, on AP request, generates payment files (e.g., XML) for treasury approval.

Databricks toolset for intelligent invoice posting

On Databricks, posting is driven by a Model Serving endpoint that packages the deterministic checks and utilities needed before anything enters the ERP: cash-discount eligibility, control validations, remittance preparation, and payment-file generation. 

Each call returns a signed, reproducible validation and parameter record, so posting decisions are traceable and easy to roll back if required.

Workflows orchestrate the process end-to-end. A job triggers as soon as the Matching agent marks an invoice ready to post; schedules define payment-run windows (e.g., daily at 3 PM), and period-close holds pause posting at month/quarter end and resume automatically after close. 

The Posting agent writes outcomes to Gold postings, enabling learning components and analytics to track results without repeatedly calling the ERP.

TCO considerations for building an invoice posting agent in Databricks

Duplicate submissions, posting low-confidence invoicing, and ERP retries rack up infrastructure costs and negatively affect the agent’s performance. 

The tweaks helped prevent this expensive rework and keep TCO under control. 

Setting up posting hash verification. Use hashing in Model Serving endpoints to prevent duplicate postings, ERP reversals, and redundant connector jobs.

Designing a two-lane posting queue for invoices. Process critical vendor invoices immediately in micro-batches, utilizing scheduled payment runs (e.g., 3 PM) to generate single payment files per batch, thereby reducing posting costs.

Creating an ERP evidence cache. Save answers to repeated status checks (e.g., payment blocks) to reduce API calls and prevent ERP system overload by limiting connections.

Intelligent invoice posting workflow streamlined reconciliation

The invoice posting agent helps APs capture discounts and cut late-fee incidents by over 60%. Thanks to pre-posting validation, the ERP acceptance rate reached 98% compared to 92% for the pre-automation workflow. 

Since the implementation of automated posting, the total posting time has gone down from 45 to 10 minutes per invoice on average. 

Step 6. Learning and iteration

In a learning workflow, the system monitors itself in production and improves with every cycle. 

The Learning and Iteration agent observes outcomes across components and human-in-the-loop decisions to recommend targeted changes, such as adjusting confidence thresholds, switching models, or refining routing rules. 

The Learning and Iteration agent ingests three types of signals: 

• Quality: correctness, the need for human overrides
• Cost and latency: serving costs, DBU, queueing, and processing time
• Safety: policy violations and unsupported actions. 

Building a Learning and Iteration agent in Databricks

With Databricks, evaluations are set up in Lakehouse Monitoring for GenAI to measure behavior in real workloads.

The Learning agent queries logs emitted by other agents to quantify drift, check confidence thresholds, validate guardrails, and score category metrics (e.g., price-variance resolution accuracy).

Proposed changes are implemented via MLflow: promising runs are registered, rollouts can be introduced gradually, and any underperforming update can be reverted immediately. This closes the loop, ensuring that each decision informs the next without sacrificing governance or auditability.

Cost reduction mechanisms for the Learning and Iteration agent

The most challenging part of designing the learning agent that closes the loop on the entire system was to have the agent make the most out of the data it has before starting new experiments. 

We made a few workflow tweaks that minimized resource consumption and helped capture more insight from the entire system’s performance. 

Right-sized infrastructure per cohort. The system validates lower-cost paths by gradually routing small invoice cohorts (5%) to cheaper stacks. This helps expand successful configurations while maintaining SLAs.

Capped token usage and retrieval costs. We set hard budget caps per agent and cohort, cached vector embeddings to avoid recomputing context during A/B tests, and normalized artifacts to reduce per-experiment costs.

How the Learning and Iteration agent maintains high  reconciliation efficiency

Through continuous learning and iteration, agents observe and mimic the decisions of AP reviewers. Since the system was adopted in real-world scenarios, the amount of human involvement gradually went down by 68% and the average posting speed improved by 55%. 

The takeaway

Compound AI systems deliver quantifiable improvements in multi-step workflows. Our invoice reconciliation implementation produced sustained performance gains, with APs now spending just 5 minutes on average to reconcile an invoice compared to much longer times before automation.

This project demonstrated that Databricks offers a comprehensive toolset for building scalable, cost-effective compound AI systems. The platform’s integrated components, from Auto Loader and Delta Live Tables to Model Serving and Workflows, work together seamlessly without requiring complex integrations.

For TCO optimization, workflow orchestration delivered the biggest impact. Fine-tuning batch sizes, trigger intervals, and task coordination reduced both compute waste and processing bottlenecks. 

However, the most reliable cost control came from managing resource consumption directly: QPS caps prevent runaway spending from traffic spikes, while auto-scaling ensures you pay only for resources actually needed.

The key takeaway is that compound AI success depends as much on infrastructure discipline as it does on model performance. Get the orchestration and resource management right, and the AI capabilities can deliver their full potential at predictable costs.

The post Building a compound AI system for invoice management automation in Databricks: Architecture and TCO considerations appeared first on Xenoss - AI and Data Software Development Company.

Your team receives invoices in dozens of formats: PDFs buried in email attachments, EDI transactions, paper documents that somehow still find their way to your desk in 2025. Each invoice triggers a complex dance: data extraction, vendor validation, purchase order matching, goods receipt verification, exception handling, and finally, if you’re lucky, approval and payment.

Here’s the uncomfortable truth about most “automated” invoice processing: systems fail not because the software lacks intelligence, but because they don’t recognize their own limitations. You’ve probably seen it too. A pixelated vendor logo, a missing dash in the PO number, a unit-of-measure quirk, and suddenly your “touchless” pipeline is all hands on deck.

The trillion-dollar AP challenge

In the context of invoice reconciliation, companies must match invoices against purchase orders (POs), contracts, and payment records across multiple ERP systems, banks, and vendor systems. The key challenges are:

Format complexity

• PDFs, Excel files, EDI transactions, scanned images
• Inconsistent vendor references and missing fields

Business logic exceptions

• Partial deliveries and quantity variances
• Multi-currency transactions and tax differences
• Discount calculations and payment term variations

Risk management

• Duplicate invoice detection across systems
• Fraud prevention and vendor validation
• Compliance audit trail requirements

Even top performers still leak value through errors, rework, and duplicate or erroneous disbursements. Recent APQC benchmarks indicate that top performers achieve 98% of first-time, error-free disbursements, compared with 88% for bottom performers. This means that up to 12 out of every 100 payments are late or inaccurate in lagging organizations. That is not a rounding error at scale. 

The deeper issue lies in years of digitizing broken processes. If the upstream PO lacks line-level detail or receiving is slow to post goods receipts, even perfect OCR won’t deliver a clean three-way match. So we codify more exceptions, add another approval step, and call it “governance.” 

What you really need is a system that knows when to proceed, when to pause, and when to escalate – with proof.

Multi-agent hyperautomation addresses these challenges through coordinated AI agents that clear the routine complexity while leaving exceptions and high-risk calls to human oversight.

How multi-agent AI transforms invoice processing 

Traditional automation reaches its limits with complex, unstructured processes like invoice reconciliation. 

Multi-agent hyperautomation adds the next step, orchestrating focused AI agents that collaborate intelligently instead of relying on rigid, sequential workflows. This approach addresses the variability and complexity that single-bot solutions cannot handle, from messy, unreadable attachments to dynamic policy decisions and exception handling.

Think of it as the best kind of intern who handles 80–90% of the work, asks for help when it should, and leaves an audit trail your controller will actually like. 

Here is a visualized comparison between the traditional automation and hyperautomation approaches.

Organizations implementing multi-agent hyperautomation typically experience a 55-70% reduction in processing costs, achieve straight-through processing rates of 90% or higher for standard invoices, and resolve exceptions 80% faster, with complete audit trails.

The agentic architecture makes this possible through intelligent specialization and coordinated execution.

Architecture that works: The core agent lineup for invoice processing  

Enterprise multi-agent hyperautomation for invoice reconciliation operates as a team of high-profile specialists coupled with the precision of AI and the coordination of sophisticated orchestration platforms. Each agent operates under clearly defined contracts that specify inputs, outputs, and performance metrics.

The agentic architecture can differ based on the specific needs, size, budget, technology capabilities, and goals of each organization, allowing them to tailor the setup and how components interact in a way that best supports smooth, reliable, and flexible financial processes. 

Due to a modular approach that adapts to every operational reality, some companies start with a few core agents and scale up, while others deploy numerous agents using the best-fitting solution.

Capture agent: Document intelligence

When an invoice arrives, whether it’s a PDF from your largest supplier or an EDI transaction from a new vendor, the system doesn’t just extract data and hope for the best. 

A specialized Capture agent (with intelligent document processing capabilities), trained on millions of invoice formats, extracts every line item with confidence scores. If confidence is high, the process continues autonomously. If not, it immediately routes to human review with specific guidance on what needs attention.

Business value: Minimizes manual data entry while maintaining accuracy controls.

Normalization agent: Data consistency

Next, a Normalization agent takes over, handling data consistency that breaks traditional systems, including real-time multi-currency conversions, jurisdictional tax calculations, unit-of-measure standardization, and vendor identity resolution. 

This goes beyond simple field mapping to context-aware interpretation that follows your business rules. For example, it recognizes that “IBM Corporation,” “International Business Machines,” and “IBM Corp” refer to the same entity, preventing duplicate vendors and payment errors.

Business value: Standardizes invoice data, reducing exceptions and accelerating straight-through processing.

Matching agent: Intelligent reconciliation

The Matching agent performs the time-intensive reconciliation work. It retrieves POs, goods receipts, and service entries from your ERP (SAP, Oracle, NetSuite, Dynamics 365). 

It applies your established policies, including two-way or three-/four-way matching with tolerances, handling real-world cases such as partial deliveries, over-shipments, freight allocations, and service charges.

Business value: Automates the bulk of standard matching while honoring existing tolerance policies.

Variance Resolution agent: Exception intelligence

When discrepancies occur, the Variance Resolution agent identifies the root causes and proposes corrective actions. It combines deterministic rules with patterns learned from your team’s past decisions (e.g., how you handle freight differences, tax rounding, partial deliveries), so exceptions are resolved the way your experienced AP team would—consistently and quickly.

Business value: Resolves invoice discrepancies, reducing exceptions and accelerating payment cycles.

Posting agent: Settlement precision

The Posting agent executes settlements with precision, interfacing with your ERP to post or park transactions, apply payment blocks as required, and schedule payments to optimize cash flow and maximize discounts. 

It generates append-only, time-stamped audit logs and prepares payment files or runs for bank submission under your approval controls.

Business value: Improves cash flow and payment accuracy while strengthening audit readiness.

Learning agent: Continuous optimization

The Learning agent closes the loop. It observes outcomes at scale, captures reviewer decisions, and turns those signals into controlled changes, retuning extraction for tricky suppliers, adjusting confidence thresholds and routing, and tightening or relaxing match tolerances by vendor cohort.

Business value: Raises straight-through rates and reduces exceptions over time without adding rule sprawl.

Beyond those cores, as the program scales, teams can add specialized agents for duplicate detection, vendor-master change control (with out-of-band bank-detail verification), fraud/anomaly scoring, supplier communications (querying missing POs/receipts), cash optimization (discount capture and payment scheduling), and others.

Orchestration that keeps you in control

The orchestration layer is a stateful workflow graph that coordinates agents. It acts as a conductor, routing each invoice based on model confidence, business policies, and real-time context, and can branch, reassign, or pause for human review when human judgment is needed.

Frameworks and platforms like LangChain, LlamaIndex, LangGraph, CrewAI, Microsoft AutoGen, Microsoft Copilot Studio, or Agents for Amazon Bedrock provide branching, retries, and observability, so the flow adapts cleanly to your rules and controls. 

The payoff is modularity: you can adjust or change a single agent without reworking the entire process when a supplier changes templates.

The orchestration layer embeds governance, integrations, and controls upfront. 

It records immutable, time-stamped, and attributed events for every transition, decision, and human action, allowing finance to produce SOX-aligned audit trails and evidence on demand. Integrations default to APIs and webhooks for speed and resilience, with RPA bridging legacy systems that lack modern interfaces.

Security and compliance are also built in.

Role-based access control and segregation of duties govern who can edit vendor masters, approve over-tolerance exceptions, or change bank details, with agent-level checks so no single actor can move a payment end-to-end. 

As a result, an orchestration layer runs efficiently under normal conditions, slows down intelligently when risk appears, and leaves a clear, defensible record for finance and audit.

While agents deal with routine tasks, making automation more secure, faster, and auditable, they will not replace your finance teams.

Why human-in-the-loop automation changes everything

Touchless processing is shifting to a baseline expectation: IFOL data reported by NetSuite show that two-thirds of respondents expect their AP processes to be fully automated by 2025, and 76% of AP departments will leverage AI within the next few months as the engine behind touchless workflows.

In payables, however, exceptions are where the risk lives, so human judgment serves as the circuit breaker. A human-in-the-loop (HITL) layer makes automation more defensible by routing the right decisions to the right people with the proper evidence, then folding those decisions back into the system, so it gets sharper every month.

Agents do the heavy lifting with capture and matching, but they never guess with money. 

Review process

When confidence about critical fields (invoice number, totals, tax, line items) drops or an item falls outside tolerance, the orchestrator pauses and opens a review task. 

Approvers see the source image, extracted fields, PO/receipt context, and only compliant actions (approve, short-pay, request credit, fix receipt). Decisions take minutes, not days, and every step is time-stamped and attributed to create an audit trail.

That immutable trail is the difference between “trust us” and “here’s the evidence,” which is exactly what finance and audit expect.

Security by design

Segregation of duties is enforced in-flow: the person who requests a vendor-master or bank-detail change cannot approve or execute it; high-risk actions require dual approvals and out-of-band verification. Suspected duplicates are blocked before payment and routed to AP with full context. Clean cases go straight through, shifting human effort from re-keying to risk control.

Compliance readiness

As AI regulations tighten across jurisdictions, having human oversight built into your financial processes is a regulatory requirement.  External auditors don’t get a black box; they get clear decision trails showing where people validated AI recommendations, especially on high-value or high-risk items. The append-only log provides the evidence that finance and audit expect.

Learning loop

When an AP manager overrides a recommendation (e.g., short-paying after a partial delivery, adjusting a tolerance, rejecting a bank detail change), the system records the rationale and applies it to similar scenarios. Your team’s expertise becomes part of the decision logic, improving automation without compromising accountability.

Measured business impact

Organizations with mature HITL implementations report:

• Higher accuracy: more first-time, error-free disbursements, as only ambiguous cases reach people
• Faster cycles: approvers resolve exceptions with full context in centralized interfaces
• Reduced leakage: duplicates and misposts are stopped before cash moves
• Stronger audit confidence: every exception and approval carries time-stamped evidence

The goal of human-in-the-loop practice is to let automation run at full speed where it’s safe, pull a human in precisely where it isn’t, and make every decision train the machine. 

As a result, payables are faster, cleaner, and audit-ready without risking your cash or credibility.

Business outcomes of the multi-agent hyperautomation your CFO will measure

Multi-agent hyperautomation offers scalability (various agents can process different invoices simultaneously); flexibility (each agent has its specialization, so updates are modular); resilience (if one agent fails, others still function); adaptability (the system learns from exceptions and evolves); end-to-end coverage (from ingestion to fraud detection, to final payment).

Some measurable benefits that show up in your P&L and balance sheet include:

Immediate financial impact 

AP automation delivers tangible operational expense relief. Goldman Sachs demonstrated years ago that automation achieves cost reductions of 60-70% per invoice. 

The recent APQC studies confirm this trend continues: automated top performers process invoices at $2.07 each, while manual operations spend nearly $10. 

These cost savings per invoice accumulate across every expense category: labor costs drop by 70-80%, while the hidden drains of physical goods (such as paper checks and stationery) and transaction/credit card processing fees are systematically eliminated, as automated costs become only 33% of the manual processing costs. 

Working capital optimization

Multi-agent systems identify and capture early payment discounts that manual processes miss. A 2% discount on invoices paid 10 days early delivers a 36% annualized return (better than most investment portfolios). 

Organizations report a 15-25% improvement in discount capture rates, translating to millions of dollars in additional cash flow for large enterprises.

The system also optimizes Days Payable Outstanding (DPO) within your policy constraints. Instead of paying everything at the last minute or leaving money on the table with early payments, intelligent agents schedule payments to maximize cash on hand while capturing available discounts.

Minimum risks and losses, even those you don’t know about

Duplicate payments are the silent profit killer in AP operations. Reports claim that organizations typically lose 0.8-2% of disbursements to duplicate payments and overpayments.  

Multi-agent systems cut this to near zero through detection algorithms that cross-reference supplier information, invoice amounts, dates, and line-item patterns. 

Fraud prevention evolves from a reactive to a predictive approach. The system flags suspicious patterns, like new vendors with banking details matching those of existing suppliers, manipulated invoice sequences, or amounts strategically positioned just below approval thresholds, delivering risk-scored alerts with specific recommended actions.

Suppliers’ relationship enhancement

Your suppliers value predictability over speed, and automated systems deliver both. Real-time invoice status visibility, clear exception communication, and consistent payment timing translate directly to better contract terms, priority allocation during shortages, and partnership relationships that drive advantages when you need them most.

Audit and compliance efficiency

External auditors demand comprehensive, immutable audit trails. Multi-agent systems create complete evidence packets for every transaction, from the original invoice, matching documents, approval chains, to payment confirmation. SOX compliance becomes a natural byproduct of regular operations, instead of a separate audit preparation exercise.

Multi-agent automation scenarios across industries

Custom multi-agent hyperautomation systems appear to be a perfect solution, but it’s not a universal playbook. Every industry sector needs to approach the implementation with a focus on business operating nuances, unique requirements, and regulatory constraints.

Manufacturing

In Manufacturing and Production, complexity is the norm, and control without drag is the goal.

Challenge: Multi-site receiving, partial shipments, and multi-currency POs strain manual matching and handoffs.

Solution: Multi-agent orchestration enforces two-, three-, or four-way communication across POs, receipts, and invoices, with policy-based routing for variances.

Outcome: Fewer handoffs, consistent cross-location controls, faster, cleaner period closes, and reduced manual coordination overhead.

Retail, eCommerce & CPG

In volume businesses, like Retail, eCommerce & CPG, scale and seasonality test throughput and control.

Challenge: High-volume, low-value transactions with seasonal spikes, promotions, deductions, and short-pays.

Solution: Agents buffer peaks, push clean POs straight through, and route only ambiguous invoices and trade claims to the right owners with full context.

Outcome: On-time supplier payments, shorter cycle times, fewer deduction disputes, and audit-ready trails.

Healthcare 

For Healthcare providers, discipline and explainability come first.

Challenge: Varied reimbursement models and strict audit requirements around medical services and sensitive supply purchasing.

Solution: Agents perform nuanced matching with role-based approvals and documented evidence aligned to healthcare privacy and audit needs.

Outcome: Fewer escalations, defensible audit evidence, and a timely close without loosening controls.

Pharma

In Pharmaceuticals, pricing programs and chargebacks raise the stakes on accuracy.

Challenge: Complex pricing and chargeback programs, distributor relationships, and risk of duplicate discounts.

Solution: Agents validate eligibility, detect potential duplicate discounts, and link delivery/EDI records to invoices before posting.

Outcome: Reduced revenue leakage, cleaner settlements with wholesalers, and stronger compliance posture.

Financial Services & Banking

In regulated Finance and Banking, policy enforcement is non-negotiable.

Challenge: Fraud control, regulatory reporting, and risk management require strict approvals and reconciliations before money moves.

Solution: Agents encode maker-checker, dual controls, and pre-funds reconciliation as an executable policy, auto-documenting who did what, when, and why; ambiguous signals are escalated with context.

Outcome: Lower operational risk, faster clean throughput, examiner-ready documentation.

Energy & Oil & Gas

For the Oil & Gas industry, allocation accuracy and layered approvals are critical.

Challenge: Joint-venture accounting (JIB/JVA), field tickets, and non-operated interests across entities and jurisdictions.

Solution: Agentic systems automate multi-entity allocations, tie field tickets to invoices, and enforce role- and project-based approvals.

Outcome: Faster acceptance, accurate cost splits, tighter governance across assets.

iGaming & Digital-native payouts

In iGaming businesses, speed must coexist with AML/KYC control.

Challenge: Affiliates, creators, and player withdrawals across multiple payment partners and jurisdictions.

Solution: Daily agent-led reconciliation of platform balances, settlement reports, and bank movements; clean payouts auto-clear, anomalies (identity mismatches, unusual velocity) route with evidence.

Outcome: On-time payouts, fewer write-offs and disputes, and regulator-ready logs.

Sales & Marketing

In Sales & Marketing, the ad/media spend ties up budget when billing doesn’t reconcile quickly with orders and deliveries.

Challenge: Reconciling insertion orders, delivery, and invoices across platforms and agencies.

Solution: Multi-agent automation standardizes billing data, confirms delivery against contracted terms, and routes only exceptions to media, finance, or vendors.

Outcome: Faster billing close, fewer make-goods and credit notes, stronger working-capital discipline.

Based on the Xenoss case study, showing a 55% reduction in accounting staff costs through multi-agent reconciliation automation, we can see that successful hyperautomation isn’t about deploying generic solutions, but about architecting systems that understand and adapt to each industry’s operational DNA. The most effective implementations work within existing enterprise infrastructure while building intelligence that scales with business complexity.

How to make the right choice between Build vs Buy 

This is the decision that keeps CIOs and CFOs in heated budget discussions. 

The framework for making this choice is based on four key dimensions: capability requirements, total cost of ownership, implementation timeline, and organizational readiness.  

1. Start with capability evaluation. For invoice reconciliation, you need three things working together, whether custom-made or off-the-shelf: dependable document ingestion (target for 95%+ field-level accuracy), an orchestration layer that adheres to ERP controls (e.g., two/three/four-way match), and explainable exceptions that your auditors can follow. 

Most organizations need solutions that integrate with their existing financial systems without expensive middleware or custom development. 

The good news is that major cloud service providers offer pre-built agents for common scenarios and allow customization for your specific business rules.  

Look for solutions that offer explainable AI, as you need to understand why the system made particular decisions.

2. Then consider the total cost of ownership. Licenses are just the tip of the iceberg; implementation, integration, training, and ongoing operational expenses make up the bulk. 

Justify the spend with CFO-grade outcomes: higher first-time error-free disbursements, fewer duplicate or erroneous payments, and shorter cycle times.

For TCO optimization, buying is often the sensible default. Procure commodity components (extraction, workflow, human-in-the-loop) and build the policy and risk “brain” that enforces your controls. 

This hybrid approach delivers value sooner and reduces the costs of staffing a full AI/automation stack. 

Reserve full custom builds only for unique reconciliation logic that helps you operate more cost-effectively at scale. 

Tie your choice to key metrics and select the option that moves them within a reasonable timeframe without inflating your operational spend.

3. As for the implementation timeline, “build” approaches typically require 12-18 months for full deployment, assuming you have the right technical talent and project management capabilities. 

“Buy” solutions can be operational in 3-6 months, but they call for a careful vendor selection and a straightforward implementation methodology.

The fundamental question shifts from speed to risk management. Building gives you complete control when policy is the product, but only if you can develop AI expertise internally. 

Buying transfers technical risk to vendors but creates dependency on their roadmap and development priorities. 

Here, the human-in-the-loop approach lets finance teams approve exceptions with complete evidence packets, allowing you to govern outcomes, not watch bots.

4. Evaluate organizational readiness honestly. This means considerable changes to supplier communication, internal workflows, role definitions, approval processes, exception SLAs, and vendor-master data hygiene on top of new software and systems.

Many organizations underestimate the investment needed for change management. Budget for training and communication programs, as the process changes affect supplier relationships and internal operations beyond just installing technology. 

Regardless of your choice, ensure that vendor bank detail changes are locked down with segregation of duties and out-of-band verification. This is a well-documented fraud vector, and stopping it prevents expensive mistakes.

Practical recommendation

For most organizations, the pragmatic answer is a hybrid approach.  

Buy a proven foundation for extraction and workflow, and tailor the policy/risk logic that makes your business unique. 

Whichever path you choose, define the non-negotiables in business terms:

• Reliable invoice and line-item capture
• ERP controls enforced
• Explainable exceptions
• Clear approval accountability
• Results measured by straight-through rates, cycle time, and payment accuracy

If a vendor or your internal build can’t show measurable progress on these within a couple of quarters, keep looking.

Getting started with multi-agent hyperautomation: 90-day roadmap  

Here’s a tried-and-tested plan for launching multi-agent hyperautomation for invoice reconciliation, structured to minimize risk, demonstrate value quickly, and set you up for scalability.

Ownership and alignment (pre-work)

We recommend appointing a single executive sponsor as the initial step, typically the CFO, to own outcomes, funding, and change management operations. 

Stand up a core team: IT (architecture and integration), AP (process and controls), and Procurement (supplier communication). Use this group to lock scope, KPIs, decision rights, and the pilot plan.

Days 1-30: Foundation and discovery

This is the staging step, where you need to run a current-state review (invoice volumes by type and source, exception rates and root causes, cycle times and bottlenecks, compliance gaps, and audit findings). 

Then, map system touchpoints and data-quality issues. 

The next point is to set baseline KPIs against which you will report. In parallel, evaluate vendors using proof-of-concept tests on your real invoices, especially the messy edge cases. 

A platform that handles exceptions reliably will handle routine transactions at scale. With facts, baselines, and an honest vendor read, you can design a pilot that matters.

Days 31-60: Pilot planning and preparation

During this phase, translate the findings into a focused pilot, typically one vendor segment or business unit that reflects broader patterns without excess complexity. 

Define success criteria, measurement methods, and rollback steps. Additionally, prepare the infrastructure by connecting data sources, finalizing security and access controls, and specifying audit logging. 

Begin change management with affected teams, focusing on how roles evolve (fewer manual touches, clearer exception ownership). With scope locked and people briefed, you’re ready for a controlled rollout. 

Days 61-90: Pilot execution and optimization

Launch the pilot with daily monitoring and weekly review cycles. Multi-agent systems learn from experience, so ensure your team tunes rules, thresholds, and assignments as signals arrive. 

Capture lessons learned, refine agent configurations, and document standard operating procedures. 

Most importantly, measure processing accuracy, cycle time improvements, exception reduction, user satisfaction, and financial impact. These metrics become the business case for broader rollout.

Finally, at every stage, instead of perfection, we advise aiming for clear proof of value, control comfort for audits, and a credible way to support organizational learning that enables confident scaling.

The future of touchless AP

Ten years ago, we shipped AP “projects,” nursed them along, and rebuilt from scratch when requirements shifted. Today’s approach treats AP automation as a product: stable, secure, and evolving nonstop. Regular refactoring, tech upgrades, and component retirement aren’t glamorous; they keep you out of the “legacy, do not touch” death spiral.

Adaptive multi-agentic intelligence is designed to optimize outcomes, such as adjusting payment timing to maximize discounts while meeting DPO targets, or systems that automatically renegotiate payment terms with suppliers based on historical performance and market conditions.

The future of touchless AP centers on the key technological shifts:

• Policy as code replaces tribal knowledge: match/variance/approval rules live in versioned engines that agents read and execute. 
• Adaptive tolerances adjust by supplier risk, historical accuracy, spend, and criticality. 
• Confidence-native UX lets reviewers confirm or correct AI suggestions with a single click, feeding corrections back into the training pipelines. 
• Real-time payments with real-time controls integrate RTP capabilities while maintaining pre-release checks for duplicates, vendor changes, and sanctions. 
• Process mining evolves into closed-loop optimization, where systems diagnose, propose, and safely apply graph changes, such as tightening tolerances.

The combination of multi-agent AI with other technologies promises even more powerful possibilities. We are already witnessing the experiments with blockchain integration for immutable audit trails, IoT sensors for automatic goods receipt confirmation, and predictive modeling for cash flow optimization.

Most of all, these systems will grow more autonomous, with complete transparency and control built in, automating routine complexity and routing atypical cases to human judgment.

Meanwhile, the most operationally disciplined companies are revisiting financial process automation. They figured out the secret of multi-agent systems that are smart enough to say, “Hey, I’m not sure about this one,” and hand it to someone who is. That’s what multi-agent hyperautomation for invoice reconciliation actually does. 

No claims to fix everything, it commits to solving the critical issues and being upfront about what it can’t.

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Regulatory framework shifts: Federal Reserve normalizes fintech oversight 

On August 15, the Federal Reserve said it’s scrapping its “novel activities” supervision program (set up in 2023 to police banks’ crypto/fintech experiments) and folding that oversight into regular bank exams. It means that crypto and digital finance technology are no longer treated as experimental outliers; they’re just banking, and they’ll be examined like everything else. Now we can expect less friction, faster partnerships, and cleaner board conversations about digital assets and automation systems. Make no mistake, risk teams still run the show, but the paperwork treadmill eases.

In the meantime, India raised the bar. A Reserve Bank of India (RBI) committee published a comprehensive AI framework for finance, with 26 recommendations across six pillars (infrastructure, capacity, policy, governance, protection, assurance), a call for domestic AI models, and tie-ins to Unified Payments Interface (UPI) with a standing multi-stakeholder committee. It’s policy-grade responsible AI for core rails: KYC, fraud, payments, and auditability by design.

And if you operate in Europe, the clock is ticking for instant payments to go from “available” to “accountable.” Under the EU Instant Payments Regulation, Verification of Payee (VoP) becomes mandatory by October 9, 2025, for SEPA credit transfers, both instant and non-instant. That means real-time name/account matching becomes a compliance requirement, not a nice-to-have, and banks as well as PSPs have just weeks to harden matching and fraud defenses or risk customer blowback and compliance heat.

The overall result is clearer compliance pathways, though not necessarily easier ones.CIOs can move faster with less “special handling,” and COOs can press for safer automation in production rather than endless pilots. Regulators haven’t stepped back; they’re just getting more selective, methodically tightening frameworks and oversight. At the same time, businesses have moved beyond viewing AI as a strategy to operationalizing it.

Major banks deploy enterprise AI: Santander and Wells Fargo lead adoption

Banco Santander detailed a push to become an “AI-native bank”, where decisions, processes, and interactions are powered by data and intelligent tech. 

In the first two months of the OpenAI partnership, Santander gave 15,000+ employees access to ChatGPT Enterprise and is targeting 30,000 users by year-end. The bank says AI initiatives delivered €200M in savings in 2024; AI copilots now support 40%+ of contact center interactions, and in Spain, speech analytics processes around 10M voice recordings annually, auto-updating CRM, and freeing 100,000+ hours for higher-value work. 

The 2026–27 plan, under incoming COO/CTO Juan Olaizola, focuses on scaling agentic AI across front- and back-office and delivering fully conversational banking, with bank-wide AI training ramping from 2026.

That puts Santander in a fast-growing club: a banking giant, BBVA, rolled out 11,000 ChatGPT Enterprise seats since May 2024; Brazilian Nubank is deploying OpenAI-powered enterprise search and service copilots; and NatWest, the first UK bank to partner with OpenAI formally, plans to supercharge its Cora+ and internal AskArchie+ assistants under the tie-up. 

According to TCS, the drumbeat keeps growing globally. Its BFSI study found that 55% of firms are building enterprise LLMs, and among top performers 88% are leaning into AI to drive innovation over mere cost-cutting. 

In APAC, Commonwealth Bank of Australia inked a multi-year OpenAI partnership, rolling ChatGPT Enterprise to 52,000 employees and co-engineering use cases in fraud detection and personalized banking for nearly 17 million customers.

Further down-market, adoption is getting even more practical. Gate City Bank picked Lama AI to modernize business-loan origination, while IDB Bank tapped ThetaRay’s cognitive-AI stack for transaction monitoring to tighten financial-crime controls.

Wells Fargo expanded its Google Cloud partnership and began deploying agentic AI to staff across the bank via Google Agentspace, plus tools like Gemini for Google Workspace, NotebookLM, and Gemini Deep Research. The bank has started rolling out access to all 215,000 employees, with 2,000 employees already piloting Deep Research and NotebookLM. The remit covers branch bankers, investment bankers, customer relations, and corporate teams – think navigating policies, synthesizing large doc sets, and real-time market insights. 

For regulated banks looking for a reference pattern for enterprise agentic deployment, this is the blueprint. AI is now baked into core banking operations: credit scoring and decisions, marketing campaigns, customer service, and back-office workflows. What started as experimental tech now requires enterprise-grade governance that auditors can examine. The infrastructure demands are also serious: model inventories, lineage tracking, approval workflows, break-glass runbooks, and real-time telemetry. 

Insurance: Assisted ops today, autonomous AI agents tomorrow

Insurity, a cloud-native software provider for the insurance industry, has integrated advanced AI into claims processing, traditionally the industry’s most expensive operational area. The upgrade pairs generative AI for triage and inquiry handling via Floatbot.AI, blockchain-backed evidence validation and fraud detection via Attestiv, and a redesigned, AI-assisted claims UI. The prize is shorter time-to-settlement and lower loss-adjusting expense, which is exactly the kind of plumbing CFOs sign off on. As a strategic positioning, Insurity’s approach focuses on augmenting rather than replacing human adjusters, making AI a productivity multiplier rather than a job replacement technology. 

On the horizon, San Francisco-based Superagent AI announced plans to debut fully autonomous insurance agents, handling sales, advice, and service 24/7 without human intervention by year-end. The company made a big claim: the products will cut new-hire ramp-up time by up to 50%, boost close rates by double digits, and reduce average call-handle time through AI-driven training, real-time call assistance, automated objection handling, compliance alerts, and intelligent client-engagement prompts. Early adopters report improved conversion rates and faster onboarding processes, with SaaS-style pricing models. As a caveat: it’s an announced launch, not production-validated, so the regulators and carriers will most likely want hard QA and licensing clarity. If successfully deployed, it could fundamentally alter insurance distribution and service economics.

These developments illustrate the insurance industry’s direction. Expect hybrid teams where licensed adjusters supervise AI that pre-reads evidence, fills claim files, flags fraud patterns, and drafts decisions for sign-off. The productivity math is compelling: this development could alter the $1.3 trillion global insurance market by reducing human intermediaries in sales, advisory, and service functions. Competitive pressure is mounting as agencies without AI capabilities risk losing commercial viability and revenue. On top of that, governance issues will determine who ships first.

Payment infrastructure AI: Smart processing meets geopolitical challenges 

Payment systems are getting faster and smarter with cash flow. Bank payment company GoCardless launched Same Day Settlement+, an AI feature that speeds up Direct Debit payments and reportedly cuts late payment failures by over 80%. Using proprietary machine learning algorithms trained on data from 38 million accounts, the company says it can pay out most collected payments the same day, reducing the typical two-day BACS wait. It offers a new real cash-flow relief for finance teams reliant on pull payments, reducing the cost and frustration of late payment failures. GoCardless’s launch puts it on the map in AI-powered payment infrastructure, as businesses demand faster, more reliable processing capabilities.

The payout leg is catching up, too. Routable, the accounts payable automation platform, switched on FedNow and RTP for instant AP payouts, claiming coverage of up to almost 85% of U.S. bank accounts, including many smaller and regional banks. This addition to their existing RTP offering enables customers to send funds instantly 24/7/365, which is useful if your payable mix spans supplier segments beyond card acceptance.

Distribution is shifting at the same time. Wise is teaming up with Google to shake up money transfers. Users can now check real-time exchange rates and fees right in Google Search, then send money through Google Wallet to complete the transfer with participating providers. It’s starting as a test run in the U.S., Wise is in the first wave, alongside Ria and Xe, with early focus on high-demand corridors like the U.S. to India, Mexico, the Philippines, and Brazil.  

This creates a visible shift in how customers discover remittance services. Search becomes the remittance front door, price opacity collapses, and providers are forced to compete on transparent quotes and time-to-delivery at the very top of the funnel. For banks and PSPs, that means exposing quote-level APIs, tightening KYC/AML and fraud signals inside the Wallet handoff, and adding instant payout options where rails allow, or risk losing the customer at the results page. 

In cross-border B2B, programmable payment infrastructure is moving to center stage. Crypto and blockchain heavyweight Ripple is dropping $200 million to buy Rail, a platform that lets businesses send payments worldwide using stablecoins. Rail already handles 10% of the $36 billion global business stablecoin market and can settle international payments faster than traditional banks. 

The acquisition strengthens Ripple’s position in challenging SWIFT’s dominance with cryptocurrency infrastructure. The deal arrives just after the GENIUS Act created the first U.S. framework for dollar-backed crypto coins, though regulatory approval is still pending. 

Rail brings virtual accounts and automated back-office tools that let companies move money 24/7 without holding actual crypto on their books. If the deal clears, expect faster corridors and programmable payouts to seep from crypto-native into mainstream B2B workflows, replacing slow, expensive international transfers.

Meanwhile, the Dutch payment processor Adyen reminded everyone that policy shocks beat perfect tech. After U.S. tariff changes throttled volumes from China-based eCommerce clients and kneecapped a key marketplace partner, eBay, the company trimmed guidance and saw shares drop roughly 20%. The trigger was Washington’s suspension of the “de minimis” duty-free rule that first hit China/Hong Kong in May and is now set to expand to most low-value imports on Aug 29, 2025. With that change, sub-$800 parcels face full customs duties and procedures, a body blow to ultra-low-cost cross-border models. Previously, Temu and Shein reported a slowdown in the U.S., as their low-cost shipping models collapsed, and European postal operators have begun pausing American-bound parcels to retool for the new rules.

This exposes fintech’s Achilles heel: even sophisticated payment routing and instant settlement systems become irrelevant when trade policy rewrites the economics overnight. 

The payments sector faces competing pressures: accelerating technological capabilities alongside increasing geopolitical uncertainty. Payment orchestration platforms are democratizing enterprise-grade capabilities: routing optimization, fraud detection, and the instant payment adoption boom, giving smaller players the ace they need to compete with payment giants through AI-powered infrastructure. But the tech-first approach with advanced algorithms needs to be tuned into a dual-track game plan with risk frameworks modelled into your payments P&L. 

Capital markets infra: Edge-native AI arrives

Beeks, a cloud computing and connectivity solutions provider for financial markets, launched Market Edge Intelligence. The AI/ML layer passively analyzes capital market telemetry at the network edge (in colocation) to predict anomalies, forecast capacity/risk, and even generate trading signals from network and order data “invisible to traditional feeds.” It supports open integration (Kafka, QuestDB) and major exchange protocols, with options to run as part of Beeks Analytics, standalone, or hybrid.

The platform offers brokers, buy-side firms, market makers, trading venues, and exchanges real-time AI analytics with reduced latency and actionable alerts designed to lower operational costs and minimize downtime. Technical benefits include reduced mean time to recovery (MTTR), fewer system incidents, and early warnings when network performance approaches capacity limits.

Investment flows: Funding the financial AI infrastructure boom 

To keep the engine fed, the money is moving upstream into silicon. AI compute tailwind, Japan’s SoftBank is buying $2 billion of Intel common stock, slotting itself among Intel’s top holders (roughly sixth, per LSEG) as the chipmaker grinds through a turnaround. Intel popped on the news; the companies framed it as a straight equity infusion, not a purchase-commitment deal. 

SoftBank’s CEO Masayoshi Son called Intel a “trusted leader in innovation.” The timing is notable as the White House is currently weighing whether to convert portions of CHIPS Act grants into non-voting equity stakes of up to 10%, but this remains under discussion. For the financial industry, this will likely translate into steadier, cheaper compute that lowers the all-in cost of copilots, fraud stacks, low-latency risk engines, and multi-agent workflows. The companies can then stretch context windows, fine-tune in-house, and stop rewriting roadmaps around hardware shortages.

If more affordable, steadier computing is the supply-side enabler, distribution is the demand engine. That’s where Robinhood is pressing the gas. Trade-press reports say the US neobroker applied for a DFSA Category 4 license in Dubai and hired Mario Camara (ex-Equiti; earlier Saxo) to lead MENA, a move that would drop a mobile-first broker into one of the world’s most retail-active, pro-innovation jurisdictions. 

It fits the wider expansion arc: front-of-shirt sponsorship with OGC Nice to raise brand signal across Europe, a Legend desktop platform rollout in the UK aimed at serious traders, and a declared Asia push with a Singapore regional HQ on deck. 

If the DFSA approval lands, expect a step-function in A2A funding, FX, and cross-border investment flows, and a fresh fight for banks and PSPs to win those on-ramps with better onboarding, faster payouts, and tighter identity checks.

PNC Bank is taking friction out of corporate banking by meeting finance teams where work happens. Its PINACLE Connect® platform now lives inside Oracle Fusion Cloud ERP, so treasurers can check balances, move money, and reconcile without hopping between portals. The integration is available through Oracle’s B2B marketplace and turns “swivel-chair” tasks into API calls, precisely the kind of upgrade that wins treasury share when volumes spike. By embedding banking services, PNC is betting that convenience trumps everything and forcing other banks to follow suit or lose corporate customers.

Similar integration principles are emerging at the national level. The Central Bank of the UAE published a detailed Digital Dirham progress report confirming a cross-border application and a real-value retail pilot under its Financial Infrastructure Transformation program. It’s a legitimate signal that programmable settlement is moving from white papers to real corridors.  First adopters will likely be government and large enterprises (guarantees, escrow, trade), which means banks and PSPs should already be wiring name-match, AML, and wallet-KYC into pilot flows and deciding which treasury systems become the CBDC’s ledger-of-record.

While public financial infrastructures modernize, private ones are lining up to compete on SLAs instead of slogans. The reports indicate that the fintech giant Stripe is developing a payments-focused Layer-1 blockchain (codename Tempo), in collaboration with Paradigm. It’s unannounced and still in stealth, so treat it as in development. 

However, this pushes the case for a branded chain that prioritizes deterministic latency, predictable fees, and compliance controls that enterprise CFOs can underwrite. If Tempo ships, tokenized payouts and policy-first settlement could migrate from pilot decks to production roadmaps, which is one more reason banks should dust off stablecoin or tokenized-deposit strategies now, not later.

Industry implications: The Xenoss perspective on financial AI trends 

Regulation is normalizing, not relaxing. Recent courtroom fights over AI training data and web scraping make it clear the rulebook’s still being written. Judges are drawing lines case-by-case, while lawmakers inch forward on model transparency and data-provenance bills. 

Expect sharper board talks on hyperautomation and AI agents, spotlighting smart, accountable systems, and cutting red tape for finance-tech partnerships. Those are only tentative promises, but they signal fintech is becoming accepted, likely nudging institutional adoption of digital financial services and crypto-adjacent products.

As reluctant as the industry historically is, AI is crossing the chasm from copilots to process owners. The step-change moves from better prompts to AI agentic workflows that read policies, traverse systems, invoke APIs, and return artifacts for human sign-off. That shifts engineering from demo apps to orchestrated, monitored services with lineage, approvals, red-team tests, and production telemetry baked in.

Payments are turning into programmable infrastructure. AI-powered collections, instant AP disbursements, smart remittance quotes, and tokenized settlement are all saying: build compliance checks into the payment flow now or waste time and money cleaning up the mess later.

Capital markets are moving to the edge. The lowest-latency signals live in the inside collocation, where packets originate. Expect anomaly detection, capacity predictive modelling, and risk signals to run next to matching engines. The data will flow through open pipelines into streaming and time-series databases, backed by the kind of reliability and monitoring you’d expect from top-tier site reliability engineering.

Private settlement networks are back in play. Programmable, semi-gated payment fabrics competing on SLA, compliance posture, and unit economics will win enterprise workloads. Treasury cares about determinism and auditability, so the digital finance technology will have to be architected accordingly.

Now, how to make it operational:

• Ship agentic patterns, not pilots. Stand up agents that parse policy, call internal services, and draft regulated outputs under human-in-the-loop gates. Keep a model inventory, approvals, red-team cadence, and runtime telemetry that an auditor can follow.
• Industrialize identity in-flow. With VoP deadlines and CBDC trials, wire name-match, sanctions, and KYC into the transaction path. If these checks are batch, you’re already late.
• Treat orchestration as your control plane. Abstract optimization levels, cards, and compliant tokenized pathways behind policy-driven routing so you can re-path in minutes when risk or regulation shifts.
• Lock compute and talent early. Align your 12–24-month model roadmap to actual GPU/CPU availability and upskill teams into AI supervision roles (monitoring, bias testing, incident response).

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As synthetic voice and video capabilities improve, fraudsters gain new tools for elaborate schemes. Deloitte’s Center for Financial Services estimates that banks will suffer $40 billion in losses from genAI-enabled fraud by 2027, up from $12.3 billion in 2023.

For banking leaders, countering fraud means staying ahead of attackers. AI-detected schemes require stronger AI detection algorithms that can identify and stop fraud in real time, before damage occurs.

The finance industry is mapping out approaches for real-time fraud detection powered by transformers, retrieval-augmented generation, federated learning, and other machine learning tools.

This post reviews four approaches to countering voice fraud, multi-channel attacks, money laundering, and credit card fraud, each backed by real-world deployments. It also covers key considerations banking leaders should evaluate before building AI-based fraud detection systems.

The rising cost of financial fraud

Financial fraud strains the banking industry significantly. McKinsey projects banks will lose $400 billion to fraudulent activity by 2030, with authorized push payment (APP) fraud growing at 11% annually.

The European Banking Authority reports similar trends: €2 billion in losses occurred in the first half of 2023 alone. Domestic credit payments represented the highest fraud risk, followed by cross-border credit card payments within the EEA.

Despite $6 billion in AML-related fraud fines issued globally in 2023, financial institutions must strengthen protection efforts.

Real-time fraud detection helps identify unauthorized transactions before completion. Traditional methods include enhanced user authentication and GPS/biometric integration in identity verification workflows.

Machine learning real-time detection algorithms have gained significant traction over the past three years. Banks are developing proprietary algorithms trained on customer transactions to detect anomalous behavior.

Compared to traditional statistical methods, AI algorithms offer superior flexibility and fewer false positives through self-learning capabilities. Large language models and agent-based AI systems now scan for anomalies, impersonation attempts, and laundering patterns in real time.

#1: Real-time credit card fraud detection with advanced transformer models

Credit card fraud detection presents a classic machine learning challenge: massive class imbalance. Fraudulent transactions represent less than 0.2% of all credit card activity, meaning training data is dominated by normal behavior. This imbalance causes models to misclassify fraudulent events as legitimate, leading to multimillion-dollar losses.

U.S.-based researchers recently tackled this issue with a transformer-based architecture designed for real-time fraud detection. Their approach outperformed traditional methods like XGBoost, TabNet, and shallow neural networks across multiple benchmarks.

How advanced transformer models improve prediction accuracy

A five-step framework helps train a more accurate engine for detecting credit card fraud. 

1. Balance training data: Remove safe transactions until fraudulent and normal data points reach a 1:1 ratio
2. Eliminate outliers: Apply the “box-plot rule” to remove values beyond normal spread and reduce model noise

1. Validate dataset: Remove incorrect or duplicate data points
2. Train transformer model: Use balanced data to identify and apply patterns to transaction history in real time
3. Benchmark performance: Compare against other ML techniques using F1 scores

Benefits and use cases of this approach

Transformers excel in fraud detection because they evaluate all available data simultaneously, unlike decision trees or gradient-boosted trees that study one feature at a time. This enables detection of sophisticated patterns, such as transactions at atypical locations during unusual times.

Transformers also require minimal maintenance. Models update through minutes to hours of training on new data, while traditional approaches require redesigning decision trees from scratch.

In practice: How Stripe applies this approach

Stripe’s fraud detection engine, Radar, uses a hybrid of XGBoost and deep neural networks to scan over 1,000 characteristics per transaction. The system achieves 100ms response time and a 0.1% false-positive rate.

The approach suits omnichannel fraud detection, assessing up to 100 events simultaneously. Banking teams can build transformers that scan credit card fraud 24/7 across web, mobile, and ATM channels.

Stripe’s engineers note that expanding the dataset further improves accuracy, demonstrating the compounding returns of data-driven model tuning in fraud prevention.

#2: Stopping AI voice fraud with a RAG-based detection system

Phone fraud costs banks about $11.8 billion per year, and rapidly improving voice AI models make new schemes harder to detect. In 2024 and 2025, large-scale AI-voice scams went unchecked in the UK, the US, and Italy. 

To counter this, researchers from the University of Waterloo introduced a real-time detection system powered by Retrieval-Augmented Generation (RAG). The architecture combines audio transcription, identity validation, and live policy retrieval to stop deepfake voice attacks before any sensitive information is shared.

The deployment pipeline includes four key steps:

Step 1: Audio capture and encryption

The bank’s system records audio conversations, encrypts them with AES for sensitive data protection, and shares encrypted data with speech-to-text models like Whisper (keep in mind that users need to opt in for recording each call). 

Step 2: Transcription and analysis

Text-to-speech models convert audio to text, and a proprietary LLM extracts relevant data, such as company and employee names, from the transcript.  

The paper suggests using RAG to retrieve the company’s current policy and cross-check whether the conversation’s flow is normal or potentially fraudulent. 

Step 3: Parallel identity verification

While a RAG-trained LLM analyses the audio, an imposter module runs a parallel identity check. It validates whether the caller’s name is listed in the employee directory of the bank they claimed to be working for. 

Step 4: Real-time response

The employee receives an OTP identity confirmation if the name is valid. 

Because every stage is stream-based and lightweight, the whole loop is completed fast enough to warn the victim or hang up the call before sensitive information is leaked.

Adding RAG to the workflow allows swapping and updating documents in real time, without having to pause the system.  

Benefits and real-world applications

The model reported in the paper has three powerful benefits. 

One advantage of using RAG is that it allows updating the knowledge base in real time so that the model adheres to the bank’s up-to-date policies. 

Two, the architecture allows for baked-in explainability. In the paper, engineers requested the LLM to support its output with a justification, which helps reverse-engineer the algorithm’s thought process. 

Finally, by introducing OTP identity checks, finance teams can avoid false positives if the wording used is legitimate but the caller’s identity is not. 

In practice: Mastercard’s 300% boost in detection

Mastercard deployed a RAG-enabled voice scam detection system in 2024, achieving a 300% boost in fraud detection rates. This demonstrates RAG’s practical effectiveness in current FinTech applications.

#3: Using generative AI to prevent multi-channel banking fraud

Financial fraud is no longer confined to a single point of access. Attackers probe multiple channels—ATM, mobile app, web banking, even call centers, before executing a scam. That complexity complicates fraud detection: signals are fragmented, formats vary, and latency constraints challenge real-time analysis.

The International Journal of Computer Engineering and Technology outlines a five-step framework for generative AI-enabled omnichannel fraud prevention, designed to unify detection logic across all customer touchpoints.

Step #1: Channel-specific ingestion. Using a timestamped, format-specific protocol, the system logs every customer interaction, whether it’s a tap at an ATM, a browser session, or a mobile transfer. A monitor lock ensures that only complete and validated data enters the model.

Step #2: Behavioral profiling. Using historical data, the system builds a customer-specific “normal behavior” profile. It includes transaction velocity, average value, geo-coordinates, and device identifiers.

Step #3: Generative augmentation with GANs. Since fraud events are rare and it’s difficult to have a statistically significant number of data points, a generative AI model (usually a GAN network) generates scenarios with a few anomalous variables. This helps expose the model to commonly used fraud tactics. 

Step #4: Unified decision layer (gut scoring). Outputs from each channel aggregate into a single risk score, enabling detection of multi-step fraud such as credential stuffing via call centers combined with mobile transfers.

Step #5: Real-time actioning. Upon detecting risk, the system makes split-second decisions: block the transaction, trigger biometric re-authentication, or escalate to manual review.

The engine achieved 96% accuracy over six months with only 0.8% false positives, though researchers recommend human oversight for edge cases.

Benefits and real-world applications

Cross-channel fraud detection allows banks to get a big-picture view of user activities across multiple touchpoints. Analyzing all inputs in an integrated manner yields higher accuracy of predictions compared to each channel making an independent judgment. 

Xenoss engineers applied a similar cross-channel architecture to help a global bank modernize its credit scoring systems. By integrating multi-touchpoint behavior data into one model, the client achieved a statistically significant lift in prediction accuracy and greater confidence in transaction-level risk scoring.

In practice: Commonwealth Bank’s genAI system

Commonwealth Bank of Australia built a genAI-enabled system for detecting suspicious payments across its mobile app, online banking, branches, call centers, BPAY, and instant payment rails. 

The model flags out-of-pattern transactions, drives app push messages, and integrates with NameCheck/CallerCheck to catch scams that start in one channel and execute in another. The bank reports a 30% fraud reduction following model adoption and sends approximately 20,000 daily alerts.

#4: Real-time money laundering detection with federated learning

Money laundering drains up to $200 billion annually from the global economy, remaining one of the costliest and most elusive financial crimes. The difficulty lies in operational scale and fragmented data requirements for detection.

Banks, clearing houses, payment processors, and messaging networks each hold pieces of the puzzle, but privacy laws like GDPR, CCPA, and strict AML regulations prevent free data sharing between entities.

Researchers from Rensselaer Polytechnic Institute proposed federated learning for relational data (Fed-RD), a privacy-preserving architecture that allows banks to train shared fraud models without centralizing sensitive inputs.

How the federated detection model works

The framework relies on multiple financial actors contributing different slices of the transaction pipeline:

• Payment or messaging network operators (SWIFT, Visa/Mastercard, crypto-exchange matching engines, and others) supply the model with the transaction fingerprint, a record of communications between institutions. 

• Banks that hold the sender’s account and keep KYC files, internal risk scores, and activity reports. They contribute to the model with the sender’s fingerprint – a record of a customer’s habits and “red flags”. 

• Beneficiary banks (usually with entities abroad). They supply the receiver account fingerprint and help detect when deposited money enters high-risk sectors (a common pattern in the “integration” phase of money laundering). 

• Intermediaries that are not part of all money laundering workflows but occasionally get involved in cross-border transfers. The data they store locally (nostro/vostro ledgers, SAR history) helps create a money trail. 

• Regulators are typically outside the data flow. They provide a system of checks and balances, making sure the transaction meets privacy controls and satisfies legal requirements, usually without accessing raw inputs. 

Privacy-preserving collaboration: Sharing data in real time

Federated learning allows each actor to keep their data on local servers. Instead of shipping data to a centralized server, institutions share the mathematical scores they derived based on the data. This way, the model can get input from the entire transaction pipeline without having to access raw data. 

Here is the pipeline machine learning engineers built to implement this model. 

• Local crunching: The payment network turns a transaction record into a short numeric fingerprint. Each bank does the same to the sender’s and beneficiary’s fingerprints. 

• Privacy-preserving sharing. A pinch of random noise is added to numerical records, a practice ML engineers refer to as noisification. The index cards are then summed up with a secure calculator inaccessible to third parties. 

• Joint verdict. A fusion program, holding all fingerprints, produces a real-time score that estimates the probability of money laundering. 

• Iterative learning. Each actor in the model gets anonymized feedback on the transaction, which they can use to improve their fraud detection algorithms. This approach allows everyone involved to use the contribution of other institutions’ data without ever directly accessing it. 

Benefits and real-world applications

The engineering team behind the model reported a 25% increase in money laundering attempt detection compared to gold-standard single-bank models. The algorithm also reduced data traffic between banks, allowing institutions to minimize exposure to security breaches. 

In practice: Swift and Banking Circle’s federated learning deployments

Swift has built a fraud-and-AML sandbox that uses federated learning and confidential computing on Google Cloud. Each of the 12 participating banks retrains Swift’s anomaly-detection model locally and returns gradient updates to a trusted execution server, allowing them to collectively flag mule networks (criminal groups orchestrating money laundering activities). 

The Luxembourg-based bank Banking Circle built an internal federated learning system dubbed FLAME. It allows EU and US business units to collaborate within a single anti-money-laundering model.

Technical considerations for AI adoption in fraud detection

Generative AI has opened new frontiers in real-time fraud detection, but adoption carries inherent risks. Leaders in high-stakes banking environments must weigh performance gains against regulatory exposure, model explainability, and operational readiness.

How can organizations maximize AI-powered fraud prevention impact without jeopardizing compliance or customer trust?

McKinsey offers a practical three-layer model for evaluating machine learning use cases in financial crime detection, serving as both a risk assessment framework and an MLOps strategy guide.

Layer 1: Risk and compliance alignment. Evaluate exposure to privacy violations, security breaches, and audit failures. Ensure that any AI-based fraud engine aligns with Industry-specific regulations like AML, broader AI governance rules (e.g., EU AI Act, CCPA, GDPR), and internal policies on model explainability and human-in-the-loop oversight.

Layer 2: Business case and scalability. Measure the use case’s potential to improve the bottom line and save operational costs, readiness to scale across the organization, and superiority to non-AI ways of addressing the same concerns. 

Layer 3: Technical readiness. Examining organizational readiness for introducing AI-based fraud detection – the state of data and the tech stack, as well as skilled engineering talent. 

Applying this model to AI fraud detection use cases helps banks prioritize pilots and choose those with the highest impact-to-risk ratio. 

Bottom line

While banks and attackers have an even playing field regarding AI advancements (both parties can use LLMs, deep learning, and multi-modal generation to detect and perpetrate fraud, respectively), finance leaders can gain the upper hand through cross-institutional collaboration. 

Banks should develop strategies to collaborate with partners within (other institutions and FinTech companies) and outside (AI tech vendors and talent sources) to stay one step ahead of fraudsters. 

Increasing customer awareness of emerging fraud schemes through in-app push notifications or targeted email marketing campaigns will fortify the bank’s defenses and pressure attackers to look for new schemes. Training and upskilling the workforce to detect and prevent AI fraud will be another guardrail for reducing attack vulnerability. 

Combining these strategies, new technologies, impactful collaborations within and beyond the industry, customer education, and workforce upskilling, helps ward off fraud threats and keep banks impenetrable to financial crime. 

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Given the need to compete with FinTech and neo banks, financial institutions are opening up to innovation. 

Replacing deterministic customer service chatbots with intelligent virtual agents is one of the ways banking teams can get their edge back. These AI-powered systems already enhance 24/7 accessibility and support asynchronous communication across channels.

But standard chatbots come with limitations. They typically serve as triage systems, routing users to human agents, rather than fully autonomous assistants capable of resolving issues end-to-end.

In this post, we examine how AI agents improve upon a traditional banking chatbot, how teams can use them to raise the bar for personalization, process automation, and fraud detection, and what technical considerations CIOs should keep in mind. 

Why now is the right time to adopt intelligent agents

A careful look at the macrotrends dominating banking in the last five years explains why CIOs see intelligent virtual assistants as a way to kill several birds with one stone.

AI agents drive hyperpersonalized banking experiences

With the surge of innovative, flexible, and nimble FinTech companies, banks no longer gatekeep financial services. Customers have more options as to how they handle financial operations, and many prefer highly personalized interactions. 

74% of customers surveyed by Harris Poll would like to have more tailored services, and they stick to the institutions where they find them. Banks that succeed in creating personalized customer journeys report 72% higher total shareholder return (TSR) scores than the teams that were slow to adapt. 

Personalization is becoming a competitive differentiator in finance, and CIOs believe Intelligent virtual assistants can help lead the race. 

Advancements in generative and conversational AI 

Chatbot adoption in banking has a long history of wins and losses. Early implementations offered modest gains in productivity and cost reduction but struggled with limitations like rigid decision trees, poor language understanding, and clunky user experiences.

Until recently, the inability to parse complex queries, lack of emotional intelligence, language barriers, and high implementation costs were legitimate concerns deterring CIOs from adopting intelligent virtual agents in banking. 

Machine learning learning has come a long way. By 2025, AI agents can run multi-step tasks with minimal human supervision, and large language models are much better at gauging intent or reacting to unforeseen scenarios. A tighter competition in the space led to pricing democratization, and adopting an AI-enabled banking virtual assistant no longer comes at a high upfront cost. 

AI agents for fraud prevention

While generative and conversational AI in finance have unlocked significant opportunities for banking, it has also introduced new risks. After synthetic voices started beating fraud detection systems, CIOs had to scramble for ways to protect themselves from AI-enabled attacks. 

To counter this, banks are beginning to deploy agents as proactive fraud prevention AI finance tools. Equipped with agentic capabilities, these systems can monitor transactions in real time, detect anomalies, and flag suspicious behavior faster than traditional rule-based methods.

Although the landscape has just started to emerge, platforms like Rulebase or Feedzai have helped banks in developing markets like LATAM keep financial fraud under control. 

Intelligent virtual agents vs. banking virtual assistants

Intelligent virtual agents draw upon the basic features of interactive virtual assistants, platforms that banks use to deploy to automate rigid workflows. That generation of chatbots was trained to respond to a set of pre-defined scenarios with a restricted number of available services.

While they helped reduce call volumes, they often left customers frustrated. In many cases, chatbots failed to understand user intent or didn’t provide a pathway to escalate the issue to a human representative, forcing users to abandon the interaction entirely.

Intelligent virtual agents help address miscommunication challenges by relying on context-aware language-learning models that continuously analyze and adapt to the flow of interaction. 

Beyond better communication, IVAs unlock powerful operational capabilities. They can directly access external tools and systems, such as CRMs, core banking platforms, or internal databases, to carry out complex tasks autonomously.

Besides improving call containment rates and automating internal operations, intelligent agents can build omnichannel customer experiences by memorizing and contextualizing interactions across all touchpoints: The bank’s website, social media, web interface, and mobile application. 

Top use cases for finance AI agents

Customer service and account management

Customer service and account management remain the top-of-the-mind AI applications in finance. 

To date, Bank of America’s virtual assistant Erica is the most successful pilot of customer support enhanced with conversational AI for banking. In 2024, the platform surpassed 2 billion interactions. It took four years to reach 1 billion engagements and only 18 months to repeat the milestone. 

Now Erica is far more than a transactional tool. It helps customers analyze spending habits, initiate money transfers, and locate specific transactions. Just as important, it demonstrates emotional intelligence, sharing over 37,000 jokes with users and recognizing personal milestones with tailored messages.

Payments and transfers

AI agents’ ability to use tools outside of a browser and access data outside of the web enables a fairly new use case: AI-led transactions. 

Recently, Visa released its Agent Interface and Mastercard shipped Agent Pay. Both platforms allow AI assistants to make payments on a payer’s behalf. The human stays in the loop by defining spending limits, setting preferences, and preferred merchant categories. 

These systems strike a balance between automation and oversight, allowing banks to streamline transactions without sacrificing control or compliance. Visa’s tokenization-based privacy framework, for example, is specifically engineered to reduce security risks and prevent data exposure.

Product guidance and onboarding

Customer onboarding, especially in corporate and cross-border contexts, remains one of the most time-consuming processes in banking. According to McKinsey, onboarding a corporate client can take up to 100 days on average, with additional delays for multinational accounts.

On the other hand, streamlining global transaction onboarding can unlock trillions of dollars of added value. 

AI agents have the potential to be at the heart of a more seamless and cost-effective onboarding process. Automating the processing of handwritten paperwork, report generation, and account setup can drastically reduce the time customers spend on KYC due diligence. 

Since CIOs may be reluctant to commit to the use case due to the lack of successful large-scale pilots, it’s worth noting that AI-assisted onboarding is gaining momentum, with up-and-coming finance AI chatbot vendors like Glide and Lyzr. 

Fraud and security

Better context awareness and the ability to draw upon higher volumes of real-time data make AI potentially superior to traditional statistics-based and anomaly-based fraud detection techniques. 

Bans can build intelligent agents that go through a customer’s transaction data, on-site behavior, credit history, and other records to flag suspicious activity. What makes these systems especially appealing to CIOs is their capacity for continuous learning. As agents ingest more data and refine their models, their accuracy improves, translating into growing ROI over time.

A multi-agent fraud detection system, outlined below, shows how anomaly detection, pattern recognition, and network analysis agents can share data, exchange knowledge, A multi-agent fraud detection system, outlined below, shows how anomaly detection, pattern recognition, and network analysis agents can share data, exchange knowledge, build plans, and coordinate actions. This system closely mimics the actions of human investigators and can supplement fraud detection teams. 

Loan and mortgage servicing

In a high-rate, low-demand lending environment, banks are competing for a shrinking pool of qualified loan applicants. At the same time, they’re contending with the growing influence of mortgage brokers, who now account for up to 70% of loan originations in key markets like Australia and the UK.

To keep their ground, companies need to both target applicants with a more seamless servicing experience and design new workflows for partnering with brokers. 

Intelligent assistants can help banking teams reduce the strain of loan and mortgage servicing workflows. They help keep track of a borrower’s payments, send automated due date reminders, process payments, and manage escrow accounts to help bank customers make insurance and property tax payments on time. 

Building omnichannel banking experiences with AI agents

Fragmentation is a pain point for most banks, especially if they operate internationally and focus on meeting customers’ needs across multiple touchpoints. 

A typical bank aims to engage customers in physical branches, ATMs, mobile apps, online banking platforms, call centers, and chatbots. 

Yet, the lack of data sharing between these touchpoints adds friction to the user experience. For instance, if a customer starts a transaction via the online banking portal, there’s rarely a way to seamlessly continue it via a mobile app. 

Strict guardrails make real-time data integration between touchpoints challenging for banks, but if successful, it could drive double-digit growth in transaction completion rates. 

AI agents can serve as connective tissue across this fragmented ecosystem. Banks can orchestrate a truly unified customer journey by deploying dedicated agents on each platform, all tied into a shared, real-time knowledge base.

For example, if a customer initiates a transaction on the mobile app, the mobile agent immediately updates the central knowledge base. When that customer switches to a web interface, ATM, or speaks to a human operator, the interaction can resume exactly where it left off.

This level of continuity and engagement, ramped up with conversational AI in banking transforms isolated digital touchpoints into a fluid, intelligent network, ultimately driving higher engagement and loyalty.

How to keep cross-platform intelligent agents aligned with your brand

Banks planning to bring AI agents into their customer service should consider how the new platform fits the company’s broader strategy. Although large-language models are self-learning systems, they can “miss the beat” in conversation. 

Early adopters of generative AI in customer support warned that large-language models can “be too chatty”, “overexplain”, or recommend products that are not part of the company’s portfolio. 

Keeping virtual assistants on-brand requires an ongoing partnership between human designers and the AI. Prompt engineers and conversation designers play a crucial role in shaping how virtual agents speak, react, and represent the institution. They fine-tune outputs based on user feedback, business rules, and the brand voice.

Regulatory considerations for building an AI-first banking experience

In a heavily regulated domain like banking, a shift from fully deterministic assistants to blackbox generative AI in finance introduces new legal and operational complexities. 

The inability to predict how the model handles every interaction and difficulties in reverse engineering the algorithm’s decisions call for external guardrails that would govern the model and the data it’s trained on (MIT research states that most genAI-related lawsuits in banking were data-focused). 

Here are the key regulatory considerations for AI agent adoption that banking leaders should keep in mind. 

• A screening program that validates if generative AI use cases in banking poses regulatory risks 
• Protocols for overseeing and testing AI agents before launch
• Assessment and documentation of third-party agreements and vendor-associated risks
• A roadmap for ongoing oversight for deployed AI agents, with assigned accountability managers. 
• Practices for keeping track of pending AI regulations and updated legislation. 

Data sovereignty is another focus area banking teams should not gloss over when planning to implement AI in accounting and finance. To reduce risks, CIOs need to verify that the training data used by the model is not shared with other providers. 

Building intelligent agents: In-house vs third-party vendors

The “build vs. buy” dilemma is as relevant in AI agent adoption as it is in other areas of building a banking tech stack. In the last five years, banks have been shifting from implementing off-the-shelf solutions to building custom software. 

For some banks, this shift is strategic. Proprietary platforms are increasingly seen not just as internal enablers but as potential revenue streams. Several institutions are exploring ways to monetize their internal tech stacks by offering them to other financial service providers.

Building proprietary technology is a logical strategic direction in a market where technology is becoming the main differentiator and success predictor. At the same time, managing in-house engineering teams requires building operations from the ground up. For banking teams, removed from best engineering practices, it may mean reinventing the wheel. 

That’s why leaders are choosing the middle ground: Building business-critical features in-house and buying the rest of the tooling. 

Scott Andrews, the COO of the Commercial Division at BOKF, told Deloitte: “When we make technology decisions for the Commercial Division, we generally ask whether this is impacting something that is unique to us and the way we do business, or is this something that we can buy and configure way that works for us?”. 

For the former group of technologies, banks choose to build; for the latter, they hit the FinTech market to build partnerships or explore M&A opportunities. 

Final thoughts: How AI agents reshape banking customer service

Using chatbots to automate the growing number of interactions has been the mainstay in banking for the last decade. 

But it’s only after generative AI hit a growth spurt in the last few years that traditional virtual assistants can evolve into interactive agentic systems that can support customers across multiple platforms. 

It’s essential that banking organizations do not run AI agent pilots by old chatbot adoption playbooks and recognize how much more versatile the technology has become. Understanding how to use the building blocks of agentic ecosystems in different scenarios (personalized experiences, faster onboarding, fraud detection, loan servicing) will help CIOs increase the impact of AI solutions for finance and drive meaningful change throughout the entire organization. 

The post Why banking CIOs should replace chatbots with AI agents to boost personalization and reduce fraud appeared first on Xenoss - AI and Data Software Development Company.

In this article, we break down the five foundational data engineering challenges that shape the infrastructure of today’s leading payment processors and gateways, from Stripe to PayPal to Adyen. We’ll also explore how these companies are solving them with real-world case studies and why mastering these challenges is now a competitive necessity.

Core data engineering challenge #1: Extreme transaction volume and velocity

The most immediate and fundamental challenge for payment processors is handling the sheer scale and speed of transactions across global systems. Billions of transactions must be ingested, processed, authorized, and logged in real time. often within a sub-millisecond latency window, while supporting fraud detection and fault tolerance at a planetary scale.

Every transaction, whether it’s a micro-purchase on an app or a large cross-border transfer, demands near-instant decision-making. To meet this, platforms must ingest and analyze petabytes of transactional and contextual data daily, maintaining sub-millisecond latency as a baseline expectation.

That scale is not just theoretical; it’s backed by staggering numbers that illustrate just how immense and relentless this flow truly is. Consider the numbers:

• Stripe: In 2024, Stripe processed an astounding $1.4 trillion in total payment volume, marking a vigorous 38% year-on-year growth. This represents countless individual transactions flowing through their systems moment by moment.
• Visa: Visa’s Q1 2025 total transaction volume soared to $3.937 trillion, a testament to the continuous, global stream of payments requiring validation and routing.
• Mastercard: Mastercard commanded an astonishing $9.757 trillion in total transaction volume for the entirety of 2024, illustrating the monumental data throughput required.
• PayPal: PayPal managed $1.5 trillion in total payment volume in 2023, processing approximately 41 million transactions every single day. Peak events, such as Black Friday 2018, saw PayPal process over $1 billion in mobile payments alone, pushing data pipelines to their absolute limits.
• Adyen: Adyen’s processed volumes in 2024 reached an impressive €1,285.9 billion (approximately $1.3 trillion USD). During the 2024 Black Friday/Cyber Monday period, their platform processed over $34 billion in transaction value globally, hitting peaks of over 160,000 transactions per minute.
• Klarna: Even the relatively newer players demonstrate this accelerating scale: Klarna’s gross merchandise volume hit $93 billion in 2024, processing around 2.5 million transactions daily.
• Afterpay: Afterpay’s Gross Payments Volume (GPV) reached $8.24 billion in Q3 2024.

What makes this data problem uniquely difficult

• Billions of transactions daily with millisecond-level SLA
• Fraud signals span device telemetry, merchant history, and behavioral patterns
• Systems must remain available globally with high fault tolerance

Proven engineering strategies

High-throughput real-time streaming architectures

High-throughput real-time streaming architectures (Apache Kafka, Apache Flink) to ingest and distribute transaction data in near real-time. These architectures ensure that transaction data is captured at the point of origination, ingested in sequence, and forwarded for real-time decision-making, whether for authorization, fraud checks, or routing. This architecture is not just about speed, but consistency, resilience, and the ability to adapt to bursty, unpredictable loads during peak events like Black Friday or Singles’ Day.

Distributed low-latency databases

Distributed low-latency databases (e.g., Cassandra, HBase)  for real-time lookups and transaction updates. These systems are essential for handling queries that must return results in milliseconds, supporting applications from fraud checks to transaction verification. Their scalability and resilience ensure reliability during traffic spikes, even under peak conditions like Black Friday.

Advanced fraud detection pipelines

Advanced fraud detection pipelines feed real-time data into ML models for scoring and blocking fraud within milliseconds. These pipelines combine streaming data, historical patterns, and contextual signals to enable rapid decision-making. They are frequently updated to adapt to evolving fraud tactics, ensuring platforms remain ahead of threats.

In-memory processing

In-memory processing (e.g., Redis, Hazelcast, Aerospike) for immediate access to risk factors. In-memory layers are used to store high-value, frequently accessed data like risk scores, recent transaction flags, and velocity checks. Their ultra-low latency enables instantaneous decision-making that traditional disk-based systems can’t match.

How leading payment platforms use real-time data engineering to power fraud detection and transaction speed

Visa and Mastercard exemplify this challenge with their AI/ML-powered fraud detection engines like Visa Advanced Authorization and Mastercard Decision Intelligence. These tools operate on top of real-time streaming architectures, often Kafka-based, that ingest and process transaction data within milliseconds. Their systems are backed by distributed NoSQL databases and in-memory compute layers to ensure sub-second risk assessment and scoring. Mastercard, for instance, publicly emphasizes its investment in AI for real-time fraud prevention, which rests entirely on high-performance data engineering. Visa builds its infrastructure to support millions of transactions per second while feeding models with clean, contextual data in real time.

PayPal faces similar velocity constraints. Their engineering teams have developed vast ingestion pipelines to aggregate data from transactions, device signals (e.g., via Fraudnet and Magnes), and behavioral events. These streams feed into real-time deep learning models for dynamic fraud detection and adaptive rule setting. Their Fraud Protection Advanced platform combines historical intelligence with live inference to block suspicious activity before it settles.

Stripe, processing over $1 trillion in annual volume, built its own internal document database, DocDB, on top of MongoDB Community. This system serves over 5 million queries per second across 5,000+ collections and 2,000 shards. Optimized for performance and availability, Stripe’s infrastructure resembles an in-memory DBaaS in behavior, with petabyte-scale data distribution and sub-millisecond lookups. These decisions ensure that authorization, fraud checks, and ledger updates complete reliably and in real time, despite the complexity of Stripe’s global ecosystem.

Core data engineering challenge #2: Building developer-friendly infrastructure with accurate and accessible data

Modern platforms like Stripe are known for their seamless developer experience, but delivering accurate, developer-facing financial data at scale is an engineering feat in itself. For platform-centric payment providers, the challenge lies in creating an infrastructure that is not only robust and scalable but also easy for developers to integrate with. This involves handling diverse transaction types, supporting recurring payments, and ensuring impeccable data quality for internal analytics and detailed merchant reporting, given the varied data inputs from different integrations.

What makes this data problem uniquely difficult

• Businesses demand trustworthy, queryable data for billing, accounting, and forecasting
• Internal and external stakeholders expect API-level access to near-real-time reporting
• Financial data must comply with global regulatory standards and be audit-friendly

Proven engineering strategies

Scalable and reliable ETL/ELT pipelines

Scalable ETL/ELT pipelines using Spark and Airflow with strong validation and schema enforcement. These pipelines are engineered with strict data contracts to enforce schema consistency and robust error handling across a multitude of merchant integrations. Stripe, for example, uses Airflow extensively to orchestrate Spark-based pipelines at petabyte scale, powering data products across 500 teams. Their scalability is critical for accommodating the growing volume of transactions and data transformations in real time.

Robust internal data models and APIs

Robust internal data models and APIs that mirror public APIs for consistent internal analytics access. This alignment ensures internal teams (data scientists, finance, operations) can query payment data with the same structure and expectations as external developers, minimizing translation errors and accelerating product iteration.

Automated financial reconciliation systems 

Automated financial reconciliation systems to ensure accuracy across currencies and payment methods. These systems reconcile payment flows, bank settlements, and merchant balances automatically, offering granular transparency and reducing manual overhead.

End-to-end governance and observability

End-to-end governance and observability: lineage, validation, and proactive issue alerts. Tools such as lineage tracking and alerting dashboards enable engineers to pinpoint and resolve discrepancies early, ensuring reporting systems stay reliable and audit-ready.

Real case: Stripe’s platform-driven approach to accessible financial data

Stripe’s developer-first reputation isn’t just a product of clean APIs; it’s underpinned by a deeply engineered data infrastructure designed to provide real-time, accurate, and accessible financial data at scale. A flagship example of this is the Stripe Data Pipeline, which allows users to sync Stripe’s payment, billing, and financial records directly into Amazon Redshift with no custom ETL work or data duplication.

This pipeline is built as a scalable, fully managed system that automatically shares near-real-time data updates across accounts using RA3-powered Redshift data sharing, eliminating the latency and complexity typically involved in financial data replication. The architecture below illustrates how this works in practice: Stripe’s Redshift environment shares data directly with a customer’s Redshift instance, which can then run federated queries across additional sources like Amazon S3, RDS, or Aurora, and visualize results through Amazon QuickSight.

This setup enables internal teams (like finance or ops) and external developers to access the same structured, queryable data, complete with schema guarantees and low-latency updates, for tasks ranging from cash flow forecasting to customer cohort analysis.

Core data engineering challenge #3: Supporting small business ecosystems with unified views across products

From POS to payroll to loans, SMB-focused platforms like Square serve as operating systems for small businesses. This creates complex, cross-product data relationships that must be unified to deliver actionable insights.

What makes it difficult is that data is often fragmented across systems: POS, ecommerce, lending, and more all operate on distinct schemas and timelines. Stitching these streams together is complicated further by product-specific data models, siloed metadata, and non-uniform integration standards. On top of that, SMB customers demand real-time visibility, self-serve dashboards that deliver holistic insights into sales, cash flow, employee performance, and customer loyalty.

Proven engineering strategies

Unified data platforms 

Unified data platforms (data lakes/lakehouses) integrating structured and semi-structured data across services. These centralized systems, hosted on cloud platforms, ingest and store raw and processed data from all product lines, such as POS systems, e-commerce platforms, lending products, and payroll services. The result is a scalable repository that enables holistic analysis of SMB behavior, financial health, and engagement across all service touchpoints.

Flexible schema evolution 

Flexible schema evolution and federated identity systems to connect user records across domains. Designing adaptable data models is essential when managing input from disparate product lines. Schema flexibility allows the platform to incorporate new data types quickly, ensuring smooth product rollouts without re-engineering data infrastructure. Federated identity solutions ensure that a single customer’s footprint, spanning purchases, employee payments, and loan applications, can be unified across previously siloed systems.

Real-time streaming pipelines 

Real-time streaming pipelines for live sales, inventory, and cash flow insights. These pipelines power dashboards for merchants and internal teams alike. They aggregate transactional and behavioral data to enable merchants to act on trends as they emerge, spotting low inventory, tracking peak sales hours, or adjusting staffing. For internal product teams, these same pipelines surface usage insights, powering A/B testing and prioritization decisions across feature sets.

Cross-product risk scoring pipelines

Cross-product risk scoring pipelines for SMB-specific loan underwriting and fraud detection. By linking behavioral signals across tools, like how consistently a merchant processes payroll, how quickly they replenish inventory, and how seasonal their sales are, data teams can craft underwriting models tailored to the volatility and opportunity inherent in SMB ecosystems. Fraud detection systems, in turn, benefit from a broader understanding of merchant behavior beyond transactions alone.

Case in point: Block’s unified data platform for SMB analytics and risk scoring

Block, a company serving small and medium-sized businesses (SMBs) with platforms like Square, faced significant data challenges due to the fragmented nature of data from its various products (POS, e-commerce, lending, payroll, etc.). These diverse product lines resulted in different data models and schemas, making it difficult to achieve a unified view of SMB health.

To address this, Block leveraged Databricks’ Unity Catalog and implemented engineering solutions centered around unified data platforms, specifically data lakes/lakehouses. These platforms integrated both structured and semi-structured data from across their services, enabling a comprehensive understanding of their SMB ecosystem. As part of this transformation, the company managed over 12PB of data and reduced compute costs 12× while improving governance.

Key to this unification were flexible schema evolution and federated identity systems, which allowed Block to connect user records across different product domains. Furthermore, real-time streaming pipelines were established to provide SMBs with live sales, inventory, and cash flow insights through self-serve dashboards. Block also developed cross-product risk scoring pipelines, crucial for SMB-specific loan underwriting and fraud detection, leveraging the unified data to mitigate risks and better serve their clients.

Core data engineering challenge #3: Enhancing customer experience through data-driven personalization at scale

Beyond payments and fraud, platforms are mining their massive historical datasets to personalize experiences, predict support needs, and recommend financial products.

What makes this challenging is the sheer depth and breadth of data involved. Payment platforms often maintain years’ worth of historical customer interactions, spanning behavior logs, transaction history, and customer support records. This massive dataset must be queried in real time, with latencies low enough to serve machine learning models and personalization systems on the fly. On top of that, the infrastructure must support continuous experimentation and frequent model retraining, often across millions of users, without disrupting performance or data integrity.

Proven engineering strategies

Large-scale warehousing

Large-scale warehousing  (e.g., Snowflake, BigQuery, or Hadoop) plays a foundational role in modern customer analytics. These systems store petabytes of structured and semi-structured data, including years of transaction histories, device metadata, and behavioral records. This long-term repository enables teams to run deep cohort analyses, train complex ML models, and generate holistic user profiles that drive strategic decision-making across product and marketing.

Real-time behavioral pipelines 

Real-time behavioral pipelines (often built on Kafka) continuously stream customer interaction data, from clicks and scrolls to payment selections and device logins, into personalization engines. These pipelines are engineered to process high-velocity signals with millisecond latency, enabling dynamic content delivery, fraud mitigation, and behavior-triggered alerts in real time. The ability to act on live data significantly improves responsiveness and user engagement across platforms.

MLOps infrastructure 

MLOps infrastructure supports the end-to-end lifecycle of machine learning for customer experience. This includes not only training and deploying models but also robust monitoring, feature store management, and automatic retraining pipelines. Whether it’s recommending tailored offers, setting dynamic prices, or routing support queries, the MLOps layer ensures that personalization engines evolve continuously and operate reliably in production environments.

Robust A/B testing infrastructure

Robust A/B testing infrastructure underpins a culture of experimentation. This infrastructure integrates deeply with frontend delivery and backend analytics systems, enabling granular measurement of customer behavior across variant experiences. By tying experimentation data directly into warehousing and ML feedback loops, teams can validate hypotheses faster, reduce guesswork, and build features that demonstrably improve outcomes.

Graph technology

Graph algorithms are increasingly vital for fraud detection by analyzing complex relationships between entities like users, accounts, transactions, devices, and IP addresses. Unlike traditional relational databases, graph technologies excel at revealing hidden connections and patterns, which is crucial for identifying sophisticated fraud rings and behaviors that might otherwise go unnoticed. They enable financial institutions to understand the context of activities and behaviors, such as tracing money laundering schemes or identifying synthetic identities that combine real and fake information. Graph algorithms can quickly uncover anomalies, predict future fraud, and reduce false positives.

Case study: PayPal and graph technologies for fraud detection

PayPal, a pioneer in online payments, leverages real-time graph databases and graph analysis extensively to combat fraud, saving hundreds of millions of dollars. Their approach moves beyond traditional rule-based systems and isolated data analysis to focus on the interconnectedness of data.

One key application involves asset sharing detection, where PayPal builds an “Asset-Account Graph” to identify unusual sharing patterns. For example, if multiple accounts share the same physical address, phone number, or device, it can indicate a coordinated fraudulent scheme. By linking accounts to shared assets, PayPal can quickly identify abnormal linking behaviors and investigate suspicious clusters.

Furthermore, graph databases allow PayPal to easily extract and analyze complex transaction patterns that are difficult to identify with traditional relational databases. For instance, the “ABABA” pattern, where users A and B repeatedly send money back and forth in a short period, is a common indicator of account takeover (ATO) fraud and can be quickly identified through graph analysis.

Graph features like “connected community” help identify closely linked accounts and their transactional behaviors. This is particularly useful for detecting fraud rings, where a group of fraudsters might exhibit very different transactional connections compared to legitimate users. By understanding the structural characteristics of the graph, PayPal can identify “risky elements” (vertices or edges) and prevent large-scale losses.

Crucially, PayPal’s real-time graph database capabilities enable it to connect different relationships in near real-time, supporting its fraud detection activities. This allows for immediate action against fraudulent activities, such as blocking new accounts created by banned users. While primarily for security, this also contributes to a smoother customer experience by reducing false positives and improving trust. Data engineers build the underlying data platforms that allow for analyzing aggregated, anonymized customer data to drive these personalization efforts, ensuring a more intuitive and rewarding user journey. PayPal utilizes technologies such as Aerospike for underlying data storage and Apache TinkerPop with Gremlin as their graph compute engine and query language. This infrastructure allows them to process massive amounts of interconnected data with millisecond latency, enabling their machine learning models to make swift, informed decisions to protect customers and transactions.

Core data engineering challenge #4: Operating a unified global payment platform with flexible architecture

Building and operating a truly unified global payment platform, supporting hundreds of payment methods across continents, means reconciling billions of data points while maintaining compliance with ever-changing regulations. This level of scale brings unique architectural burdens: systems must not only handle large volumes of heterogeneous data but must also normalize those flows across disparate schemas, currencies, and regulatory formats. Furthermore, accuracy and timeliness of reporting become paramount, especially as platforms span multiple business units and legal entities. Adding to the complexity is the requirement to maintain robust, compliant pipelines for global KYC and AML efforts, ensuring that identity verification and risk scoring adapt to each jurisdiction while still operating under a unified data backbone.

Proven engineering strategies

Centralized data hubs with universal connectors

Centralized data hubs with universal connectors serve as the backbone of global payment data platforms. Data engineers are tasked with designing systems that can ingest massive volumes of payment data from diverse sources, ranging from local PSPs to acquirers and digital wallets, and normalize it into a consistent, analyzable format. To achieve this, teams often build a comprehensive library of universal connectors that are capable of integrating with a multitude of proprietary APIs, legacy banking systems, and country-specific payment protocols.

Canonical data models

Canonical data models are developed to provide a unified view across all transactions, regardless of their origin. These models abstract away regional nuances, such as differing settlement timelines, field schemas, or local regulations, enabling downstream systems to reason about payments uniformly. Flexibility is key; these models must continuously evolve to accommodate new payment types, emerging markets, and regulatory mandates, while still offering backward compatibility.

Multi-currency and time-zone aware reporting engines

Multi-currency and time-zone aware reporting engines are critical for supporting the financial needs of global merchants. These systems must handle real-time currency conversion, daylight saving adjustments, and regional tax considerations, all while producing accurate and auditable reconciliation data. Timeliness is essential, especially when financial reports need to be generated on a daily or hourly cadence across multiple business units or jurisdictions.

Dynamic routing and real-time analytics

Dynamic routing and real-time analytics allow platforms to intelligently direct transactions to the optimal acquiring bank or payment rail, maximizing authorization success rates and minimizing fraud. Data engineering teams build streaming analytics systems capable of scoring transactions in-flight, leveraging historical success data, real-time risk signals, and contextual merchant behavior. This dynamic decision-making infrastructure requires ultra-low-latency pipelines and the ability to update routing logic in real time as new patterns emerge.

Cloud-native and microservices-based architecture 

Cloud-native and microservices-based architecture underpins the entire global payment infrastructure. Engineering teams leverage managed services for stream processing, serverless compute, and horizontally scalable storage to handle spiky workloads and regional deployment challenges. This modular approach ensures resilience, rapid iteration, and adaptability to local market demands and compliance regimes.

Case in point: How Adyen uses real-time data and graph models to power global payment intelligence

One platform that exemplifies these engineering principles in practice is Adyen, a global payment company operating in over 30 countries and supporting hundreds of payment methods. At the scale Adyen operates at, every decision about data infrastructure directly impacts authorization rates, fraud risk, and merchant trust. To meet these demands, Adyen has built a deeply integrated, intelligence-driven architecture that transforms raw transaction data into real-time insight and action.

At the core of Adyen’s system is a centralized data hub that ingests and processes thousands of events per second. Payment attempts, device fingerprints, shopper metadata, and merchant context all flow into a unified pipeline, orchestrated using Apache Airflow and transformed via PySpark. The goal isn’t just storage, it’s clean, validated, and canonical datasets that can be used across teams: risk, finance, machine learning, and compliance. By enforcing consistency early in the data lifecycle, Adyen reduces redundancy and ensures downstream systems speak the same language, whether they’re running an AML check or producing a reconciliation report.

But normalization is just the beginning. Adyen’s ability to act on data in real time is what sets it apart. To fight fraud and enable instant compliance decisions, Adyen developed an internal graph database system, engineered on top of PostgreSQL and Java, that maps relationships between transactions, devices, users, and behavioral signals. This graph layer enables the platform to detect fraud rings, trace suspicious onboarding flows, and score risk in milliseconds. When a payment attempt comes through, it’s not evaluated in isolation; it’s contextualized against a dynamic web of global interactions.

This real-time intelligence feeds directly into Adyen’s machine learning layer. Every transaction benefits from the platform’s AI-driven products: Adyen Protect, which blocks fraudulent activity in-flight, and Adyen RevenueAccelerate, which uses ML models to optimize authorization routing and retry logic. These models are trained on global payment flows and are constantly updated using fresh data from the centralized hub. According to Adyen, these optimizations have delivered measurable results up to 6% uplift in conversion and significantly lower fraud losses for merchants.

Behind these tools is a sophisticated ML infrastructure. Adyen has built a scalable feature platform, ingesting structured data from Kafka, Hive, and Spark, and serving features to models with sub-100ms latency. Their research teams also explore advanced techniques like off-policy evaluation, allowing them to test and iterate on recommendation algorithms without live traffic exposure, shortening time-to-insight and reducing the cost of experimentation.

In short, Adyen’s data architecture doesn’t just scale, it learns, adapts, and informs every facet of its global payments engine. It’s a striking example of how a modern payment company can transform the chaos of real-time, multi-jurisdictional transactions into a coherent, intelligent system. Through canonical modeling, centralized ingestion, and embedded ML, Adyen shows what it truly means to operate a unified global platform with flexible, resilient, and forward-looking architecture.

Final thoughts

From fraud to reconciliation to global scale, modern payments are a data engineering challenge disguised as a financial service. The winners in this space, from Stripe to PayPal to Adyen, aren’t just good at money. They’re masters of infrastructure.

And if you want to compete, you need to be too. Xenoss can help you get there.

How Xenoss can help

Building this kind of system internally takes years. Xenoss specializes in helping fintech companies engineer:

• Real-time streaming architectures for fraud detection and authentication
• Resilient, schema-flexible ETL pipelines across global payment rails
• Cloud-native ML infrastructure with online/offline feature stores
• Auditable, compliant data lakes and warehouse strategies
  

The post How Stripe, PayPal, Visa, and Adyen solve the toughest data engineering challenges in payments appeared first on Xenoss - AI and Data Software Development Company.

On one hand, the potential for automating repetitive finance operations is high: They are not inherently creative and would benefit from eliminating human error. 

Analysts estimate that AI adoption can add up to $170 billion to the US banking sector by 2028. For major banks, AI excellence is becoming a crucial strategic objective; as Paul J. Davies put it for Bloomberg, “Every bank fears its competitors getting good at AI before they do.” 

On the other hand, finance is a heavily regulated domain, and global government agencies are cautious about “unleashing” autonomous systems without human guidance and approval. 

In this landscape, financial organization leaders need to understand which automation goals are too ambitious (e.g., a fully automated AI bank), which lack disruptive potential (disparate AI applications in banking like chatbots that are not embedded into the customer journey), and which offer the most transformative power without the risk of losing compliance. 

This article examines four high-impact areas where AI in banking is already driving results:

• Customer service
• Cybersecurity, fraud detection, and prevention
• Loan underwriting
• Operational efficiency and automation

#1: Customer service

Amazon has raised the bar for flexibility, speed, transparency, and seamless delivery beyond e-commerce.

In banking, convenience and personalization are no longer differentiators—they’re requirements. McKinsey data shows that banks that prioritize customer satisfaction see faster deposit growth.

Additionally, banks are currently under the looming threat of disintermediation. The services that financial institutions alone used to handle (payment, budgeting, even loan underwriting) are now chipped at by non-banking innovators. 

In China, Tencent-owned WeChat offers users a wide range of financial products. Google also has a budgeting and financial management tools suite. FinTech startups like Cropin use AI and advanced analytics to simplify the underwriting process. 

In a landscape where financial institutions no longer monopolize their core capabilities, improving the customer experience with innovative technologies can help maintain the status quo. 

How AI is changing the face of customer engagement in banking

An AI-first customer engagement strategy involves constant aggregation and analysis of user data, including needs, on-site behavior, context (such as type of employment, sources of income, family situation, etc.), and preferences. 

Machine learning models offer a deeper understanding of these signals compared to traditional statistics-based algorithms like logistic regression.  

Financial institutions can channel newly discovered insights into three high-impact areas.

Intelligent product suggestions

Companies outside of finance, like Netflix, were able to attract and continuously expand their customer base with intelligent content recommendation engines. 

Retailers also use AI-enabled product recommendations engines to recommend products that meet their needs. 

For financial institutions, intelligent recommendation engines can also make a significant difference. 

Personetics, an AI-driven personalization provider for banks, allows banks to embed product recommendations into customer journeys based on user behavior signals. These hyper-personalized suggestions helped financial institutions like Synovus, the 8th-largest bank headquartered in the US, improve their deposit growth rate and achieve a 19% conversion rate. 

Cross-channel experiences across partner ecosystems

Financial institutions can counter the disintermediation threat by collaborating with partner ecosystems and leveraging the data these platforms collect to engage customers through the bank’s services. 

Citibank was among the eight companies Google partnered with in 2021 to offer digital savings capabilities to Google Pay users. The deal gave the tech giant the infrastructure for building a fintech product, while Citibank acquired a new way to reach digital-only customers. 

In October 2024, Citibank expanded its partnership with Google by leveraging its technology for expanding its use cases for artificial intelligence in banking. 

Hyper-personalized wealth management tools

Machine learning helps banks use customer data to build services that keep customers engaged beyond financial operations, like wealth and liquidity management platforms or budgeting tools. 

For instance, these real-world machine learning applications help improve customer engagement: 

• The Royal Bank of Canada deployed an AI-enabled NOMI Forecast, a platform that offers realistic projections of future cash flows based on historical data. Over 900,000 bank clients have used this feature since 2021. 

• Empower is a mobile app that helps reduce recurring expenses, such as subscriptions to rarely used tools or competitive mobile phone fees. 

• Federal Bank built a virtual assistant that uses natural language and learns over time, improving the precision of its responses.

#2: Loan underwriting

Using machine learning models to accurately predict the probability of a lender defaulting on a loan is gaining traction among banks. Underwriters are using artificial intelligence to better use the data they have at their disposal, eliminate bias from decision-making, and get more insight on millions of citizens (45 million Americans, according to the Office of Research report) who don’t have sufficient credit data in their reports. 

How the future of AI in banking impacts credit underwriting

Improving the accuracy of creditworthiness

Traditionally, banks use statistical tools like FICO (Fair Isaac Corporation) for default prediction and credit scoring. These systems consider a lender’s credit history, debt levels, payment history, and other variables to calculate default probabilities. 

Though FICO-like models are still a gold standard in credit scoring, they are static and overly reliant on historical data, which is why these techniques don’t respond well to changes in a lender’s behavior or U-turns of the economy. Besides, traditional tools often overlook underrepresented lender segments, such as younger borrowers or those in emerging markets.

Financial institutions are aware of the reactive nature of statistical creditworthiness assessment,  so they are turning to artificial intelligence. 

Machine models help integrate data from disparate sources into a unified view and make a highly accurate default probability prediction. 

Case study: Improving risk scoring with multimodal AI

Xenoss engineers supported a U.S. bank expanding into the Indian market in improving the accuracy of its credit scoring model using machine learning

Before reaching out to Xenoss, the client used independent statistical models to predict default probability based on the following data sources: 

• Credit card transactions 
• Account transactions
• Credit history

However, prediction accuracy was not high enough to support confident lending decisions, especially in a market with limited historical credit data. Seeking a more robust approach, the bank turned to Xenoss for technical guidance.

The machine learning engineers built a unified multi-modal neural network with embedded input to improve prediction accuracy. 

In the new model, inputs from three data sources were transformed and fed into the unified neural network, which processed all the data at once and provided a 360-degree view of a customer profile based on a combination of credit card transactions, credit history, and account data. 

The shift to a new neural network yielded a 1.8-point Gini uplift—a metric that assesses the predictive accuracy of the model—and allowed the bank to precisely estimate the risk of default without needing to add new data sources. 

#3: Cybersecurity, fraud detection, and prevention

Fraud takes many forms: identity theft, insider fraud, partner deception, customer scams, and payment tampering. This puts a significant strain on the global economy. According to Nasdaq, eliminating fraud could add 0.4% to the US GDP.

How AI improves fraud detection and prevention in banking

AI capabilities can help security teams detect much more granular signals of suspicious intentions than a human brain is capable of picking up. 

Here are several effective real-world use case examples of machine learning applications for successful fraud detection and prevention. 

• Image analytics. Ping An, a Chinese financial services holding company, designed a proprietary computer vision model to analyze 54 involuntary microexpressions that occur before a person can control their facial expression, to spot high-risk customers. 

• Assessing cybersecurity risk in real-time. Generative AI in banking helps organizations write code for fraud detection rules, streamline “red testing” (simulating attack scenarios to choose the most effective response), or aggregate historical data from security events to pinpoint trends and root causes of vulnerabilities. 

• Fighting financial crime. Banks increasingly use genAI to flag suspicious activity in real-time, based on a customer’s transaction history, and create customer risk ratings based on changes in know-your-customer (KYC) attributes. Oracle, for one, is pioneering the use of AI agents in countering financial crime. Oracle’s Investigation Hub enables autonomous workflows that assist investigators in collecting evidence, prioritizing actions, and documenting cases—automating manual steps without removing human oversight.

#4: Internal operations automation

In a volatile economy, banks focus AI efforts on proven use cases that reduce costs and boost operational efficiency. Rather than betting on experimental ideas, many are turning to ML to streamline everyday processes with minimal investment and fast time-to-value.

Here are the critical areas financial institutions should explore to “do more with less” in day-to-day operations. 

Empowering the workforce

Augmenting employee productivity with machine learning is one of the safest yet impactful machine learning applications in banking. 

Automating manual tasks like data entry and analysis, process documentation, and summarizing large amounts of corporate data can save teams across virtually every department of a large bank hours of productive time. 

Compliance teams, in particular, have benefited from using genAI tools to summarize and extract key takeaways from the latest regulations. 

Transforming technologies

As large-language models become increasingly on par with humans in programming, banking organizations can use these tools to optimize existing code, reducing the time in-house teams need to build proprietary technology. 

A Portugal-based financial organization built an internal banking AI engineering copilot that migrated the company’s core technology from outdated COBOL-based platforms to Oracle. The tool not only handled the code migration but also generated documentation and metadata schemas to support long-term maintainability.

How to foster successful AI adoption 

Xenoss’ engineers noticed that three success determinants help companies achieve maximum returns from AI adoption with minimal risk.  

Build a centralized data hub

Data is the foundation of every AI initiative. Banks need well-governed, real-time access to structured and unstructured data across departments. This requires a strong data governance framework, secure storage, and minimal latency between data generation and consumption.

Optimize every layer of the tech infrastructure

Scaling machine learning is nearly impossible on top of outdated systems. Legacy infrastructure creates bottlenecks that limit performance, agility, and integration.

That’s why reviewing and modernizing foundational technologies should come before deploying AI applications. A strong, flexible core is what allows AI to scale efficiently and deliver sustained value.

The following components are the building blocks of rolling out AI for financial services.  

• APIs strategy. Connecting internal systems via APIs reduces silos, improves data exchange, and enables integration with external partners—unlocking broader AI use cases in banking.

• Infrastructure-as-code. A hybrid architecture with on-premise control and cloud scalability reduces maintenance overhead and accelerates deployment.

• Codebase standardization. Ensuring that the bulk of AI components are reusable across the banking infrastructure promotes standardization and reduces time to market. 

• Security and governance: Adopting zero-trust principles and implementing centralized control centers ensures compliance and strengthens risk management.

Commit to organization-wide transformation

In Xenoss’ experience, banks that succeed with AI are those willing to implement it across entire business domains, not just in isolated, point-based use cases. Broad adoption leads to greater impact and operational consistency.

Crucially, this kind of transformation depends on well-oiled MLOps processes—repeatable, procedural, and decoupled from day-to-day operations, allowing banks to scale AI in banking and finance without disrupting core workflows.

Bottom line

As AI and banking continue to evolve and digital startups raise the bar for customer experience, traditional banks are starting to rethink how they connect with their customers. 

The most forward-thinking institutions aren’t just adopting AI to optimize processes—they’re embedding it at the core of their strategy. From personalization and fraud detection to infrastructure and underwriting, AI is becoming a catalyst for reimagining the entire banking value chain.

Getting there, though, isn’t just about plugging in new tech. It demands bold leadership, new talent, deep partnerships, and a commitment to modernizing core systems.

Banks that treat AI not as an add-on, but as a driver of transformation, will shape the future of finance.

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