As markets splinter, rules multiply, and customers expect everything now, financial services are finally growing the muscle to match the moment. What’s happening goes beyond software layered onto old systems; the transformation runs deeper than digital lipstick on analog bones. Back-office pipes got brains. Front-office screens got bots. And regulators are clearing the runway while infrastructure vendors keep building for an AI decade.
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).
