Last week, Federal Reserve Chair Powell and Treasury Secretary Bessent did something unusual: they called an unscheduled, closed-door meeting with the CEOs of America’s largest banks. The topic wasn’t interest rates, capital requirements, or liquidity stress tests. It was AI. Specifically, it was about what Anthropic’s new Mythos model revealed about the fragility hiding inside your technology stack.

If your first reaction is “this is a cybersecurity issue,” you’re thinking about the wrong problem. And you’re not alone—most bank executives are framing this as a perimeter defense story. It’s not. It’s an architecture exposure story, and the institutions that don’t understand the difference are about to get a very expensive education from their regulators.

AI Didn’t Create Your Problems. It Made Them Visible.

Anthropic’s Mythos model is designed for autonomous coding and agentic tasks. It can act independently, map system dependencies, identify vulnerabilities, and understand complex environments at scale. What spooked regulators wasn’t the model’s capabilities in isolation—it was the speed at which it could reverse-engineer decades of accumulated technical debt across interconnected banking systems.

Here’s what that means in practice: AI can now identify where your systems are tightly coupled, where your controls are inconsistent, where your business logic is scattered across platforms, and where your data definitions conflict across domains. It can trace dependencies faster than your architecture teams can document them. It can find the places where a failure in one system will cascade into twelve others. And it can do all of this in minutes, not months.

Banks have always had this complexity. Legacy cores, integration layers stacked on integration layers, data copied and transformed across business lines, inconsistent definitions of basic concepts like “customer” or “household” across divisions. The difference now is that AI can see it all at once—and so can the people who regulate you.

The questions your board should be asking are not technology questions. They’re business and risk questions:

• Do we actually understand our system dependencies?

• Can we trace data across domains with confidence?

• Are our interfaces consistent and governed?

• Can we isolate failures before they cascade?

• Do we have shared, enforceable definitions of core business entities across the enterprise?

If you can’t answer those questions clearly and quickly, you have an architecture problem. And the Fed just made it clear they’re paying attention.

The Talent War You’re Losing

While regulators were calling emergency meetings, the market was sending its own signals. HSBC appointed its first Chief AI Officer—a C-suite role, not a VP buried in IT. Intuit’s CTO announced publicly that they’re prioritizing early-career developers who grew up using AI, because they understand it better than mid-career workers who learned it later.

That’s not a feel-good diversity statement. It’s a market signal. The people who will build the next decade of financial services don’t want to work at traditional banks. They want to work at fintechs, Big Tech companies, and startups where they can ship code weekly, not quarterly. Where architecture decisions are made by engineers, not compliance committees. Where AI is a product capability, not a risk management problem.

If your talent strategy is still built around hiring “digital transformation consultants” instead of engineers who can ship production-grade AI systems, you’re not competing. You’re just watching from the sidelines while your competitors build the future.

What Happens Next

Anthropic’s model didn’t create your architecture problems. It made them visible to the people who matter: your regulators, your board, and increasingly, your customers. The institutions that have been doing the hard, unglamorous work of architecture governance—standardizing data definitions, documenting dependencies, building consistent interfaces, investing in observability—are the ones that can answer the Fed’s questions today.

The ones that can’t are about to face consent orders, not innovation awards.

The next ninety days will separate the institutions that have been serious about architecture from the ones that have been serious about press releases. If your board isn’t asking the five questions from the Fed meeting, they’re behind. And if your executive team can’t answer them, you’re not ready for what’s coming.

For CSP’s full analysis of what the Fed and Treasury are actually concerned about—and a framework for what AI-ready architecture requires—visit Core System Partners.

Don’t blink—the banking news you can't afford to miss

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Read the companion news of the week post behind this episode: https://coresystempartners.com/core-insider/whats-happening-in-core-banking-this-week-39

What an emergency AI briefing reveals about the architectural risks hiding inside today’s banking platforms

Last week, the Treasury and the Federal Reserve called an unscheduled, closed-door meeting with the CEOs of the largest banks in the country.

The topic wasn’t rates.
It wasn’t liquidity.
It wasn’t capital.

It was AI.

Let that sink in for a second.

The two institutions responsible for the stability of the financial system pulled bank CEOs into a room to talk about AI.

That’s not a trend. That’s a signal.

Our readers already know that we are halfway through our Architecture and AI series.

That series didn’t start last week. It’s the culmination of two years of conversations, client work, and a simple message we’ve been repeating over and over:

If your architecture isn’t ready, AI isn’t going to work the way you think it will.

Last week just reinforced that point in a way the entire industry can’t ignore.

This Is Not About Innovation. It’s About Exposure.

The trigger for the meeting was a new class of AI capabilities that can identify and exploit vulnerabilities across complex systems at a scale we haven’t seen before.

Not incremental improvement. Not better tools.

A step change.

Systems that can:
  •  uncover unknown vulnerabilities
  •  map dependencies across environments
  •  chain together exploit paths
  •  operate continuously

That matters for one reason.

Banks run on complexity.

Decades of systems layered on top of each other.
Legacy cores.
Integration after integration.
Data copied, transformed, and reused across domains.
Business logic scattered across platforms.

That complexity has always been there.

What’s changed is that it can now be understood at scale.

Most People Are Calling This a Cyber Story

It’s not.

Cybersecurity has traditionally been about defense.

Perimeters.
Access controls.
Monitoring.
Response.

What these new AI capabilities expose is something deeper.

They expose structure.

They show:
  •  where systems are tightly coupled
  •  where dependencies are hidden
  •  where data flows break down
  •  where controls are inconsistent

That’s architecture.

AI doesn’t create these issues. It reveals them.

And it does it faster than most organizations can react.

Take a Hard Look at the Typical Bank Stack

If you look at most banks today, the pattern is familiar.

A core system that has been in place for years.
An API layer added over time.
Multiple data stores supporting different functions.
Integrations built to solve specific problems at specific points in time.
Business rules embedded across systems instead of defined centrally.

None of this is unusual.

It’s how the industry evolved.

And for a long time, it worked.

Because complexity was hard to see.

That Assumption Is Gone

The operating model most banks rely on assumes:
  •  system behavior is partially opaque
  •  dependencies are not fully mapped
  •  vulnerabilities take time to uncover

Those assumptions don’t hold anymore.

AI collapses that gap.

What used to take months can now happen continuously.
What used to be hidden can now be mapped.
What used to be manageable becomes visible.

Two Forces Are Now Colliding

On one side, you have the upside of AI:
  •  automation
  •  efficiency
  •  improved decisioning
  •  new revenue opportunities

On the other side, you have the downside:
  •  increased visibility into system weaknesses
  •  faster discovery of vulnerabilities
  •  expanded attack surface
  •  rising regulatory scrutiny

Both forces are pointing to the same place.

Your architecture.

This Is the Conversation We’ve Been Having

This is exactly why we launched this series.

Not to talk about AI in isolation.
Not to speculate about use cases.

To focus on what sits underneath.

Because AI doesn’t sit on top of your bank.

It runs through it.

And when it does, it tests everything:
  •  your data
  •  your integrations
  •  your definitions
  •  your controls
  •  your operating model

That’s the part most conversations miss.

Structure Is Only Half the Problem

Most banks have spent years working on data structure.

Data models. Warehouses. Pipelines.

That’s necessary.

It’s not sufficient.

Because AI doesn’t just need data.

It needs context.

It needs to understand what that data means across the enterprise.

What is a customer across different lines of business.
What is an exposure across lending and treasury.
What defines a relationship versus a household versus a legal entity.

Most institutions don’t have a consistent answer to those questions.

They have multiple versions of the truth.

That’s where things start to break down.

We’ll go deeper on this in an upcoming article, because it’s one of the least understood and most important gaps we’re seeing.

Regulators Just Elevated the Conversation

An unscheduled meeting at this level is not about long-term strategy.

It’s about current risk.

No mandates were issued.

None were needed.

The message is implicit:

Understand your exposure.
Understand your systems.
Understand how AI changes the equation.

That’s not a technology exercise.

That’s a business and risk conversation.

The Questions That Matter Now

This is where leadership teams need to focus.
  •  Do we actually understand our system dependencies
  •  Can we trace data across domains
  •  Are our interfaces consistent and governed
  •  Can we isolate failures quickly
  •  Do we have shared definitions across the enterprise

If those answers are unclear, that’s not unusual.

But it is now a problem.

The Bottom Line

This is not a future scenario.

It’s not a pilot.
It’s not something you get to later.

AI is already changing how systems are analyzed, understood, and potentially exploited.

That shifts the conversation.

From:

What can we build

To:

What does our architecture look like under pressure

Because that’s where we are now.

Where to Go From Here

We’ll continue breaking this down in the remaining articles in the series.

If you want a clear view of where your institution stands today, explore the CSP Transformation Readiness Scorecard or reach out for a working session.

The gap between AI ambition and architectural reality is measurable.

And now, it matters more than ever.

Read it here:

https://coresystempartners.com/core-insider/the-ai-wake-up-call-why-the-fed-and-treasury-summoned-bank-ceos/

- HSBC appointed its first Chief AI Officer to centralize enterprise AI execution.
- The White House released a national AI policy framework that raises the governance bar for financial services.
- Mastercard advanced its digital assets strategy with an acquisition to strengthen stablecoin-era rails.
- Banks continued core modernization moves by selecting major vendors for platform upgrades.
- Jack Henry and Finastra logged new core wins as institutions prioritize cloud and API readiness.

The pattern is consistent: AI and modern architecture are becoming inseparable, and governance is turning into a competitive advantage, not a constraint. If you cannot move fast with controls built in, you will not move at all.

The full analysis is in this week’s Don’t Blink episode — link above.

FIS is quietly transferring hundreds of employees to Cognizant. People with 10, 15, 25 years of institutional knowledge — the ones who know your configuration, your integrations, your workarounds. An internal petition was filed. Whether your bank will be notified is unclear.

Do not assume it will be. Call your relationship manager today. Ask who is servicing your account, whether they are staying, and what your contract says about notification.

This week also saw HSBC, JPMorgan Chase, and Royal Bank of Canada all make AI a C-suite priority — not a pilot, not a committee. Leadership roles with budgets and mandates. When the largest banks in the world move like this, the institutions still running experiments are already behind.

And the OCC just removed the recovery plan requirement for banks over billion. The forcing function is gone. The question your board needs to answer is whether your systems are genuinely resilient — or whether you were only resilient enough to pass the exam.

Full breakdown of all this week’s stories is in the news post — link in the description.

Don’t blink, because in banking transformation, if you look away for a moment, the world moves without you.

And remember — transformation starts with insight.

So stay sharp and stay core.

FIS Is Quietly Transferring Hundreds of Long-Tenured Employees to Cognizant — And May Not Tell Your Bank

Industry sources report that FIS is rebadging hundreds of employees with 10 to 25 years of institutional knowledge over to Cognizant. These are the people who know your specific configuration, your custom integrations, your undocumented workarounds. An internal employee petition was filed calling on FIS to pause and reassess, citing critical knowledge concentration loss and near-certain service disruption risk. Whether your bank will be notified of these changes is unclear. Do not assume it will be. If your bank runs FIS, ask your relationship manager three questions today: Who is servicing my account? Are they staying? What is our contract’s notification obligation for material service team changes?

Sources: Industry sources

HSBC, JPMorgan Chase, and RBC Are Making AI a Core Leadership Priority — Not a Pilot

Three of the world’s largest financial institutions moved beyond AI experimentation this week. HSBC created the role of Chief AI Officer, making AI a formal C-suite mandate. JPMorgan Chase posted an Executive Director of Agentic AI Strategy, a leadership role with a budget and a mandate. Royal Bank of Canada is hiring to build AI directly into its core banking architecture. These are not press releases. These are organizational decisions. When the largest banks in the world are naming AI at the leadership level, the institutions still running pilot programs are already behind.

Sources: Lucidate

Two More Community Banks Select Jack Henry for Core Modernization

FM BANK and Quoin Financial Bank, both part of Independent Bancshares Inc., selected Jack Henry this week for core processing and digital banking modernization. Together they manage close to $1 billion in combined assets serving agricultural, small business, and commercial clients across Minnesota and South Dakota. Jack Henry continues to win in the community and regional bank space as legacy systems hit their replacement ceiling. The banks that feel the urgency first are the ones acting first.

Sources: Jack Henry / PR Newswire | IBS Intelligence

The OCC Just Removed the Rule That Forced Banks to Ask Hard Questions About Resilience

The OCC finalized a rule on March 31 to rescind its recovery planning guidelines for banks with at least $100 billion in assets. That requirement existed for a reason: it was a forcing function that made institutions honestly assess whether their systems could survive a stress event. The OCC’s Comptroller stated the guidelines “do little to improve banks’ ability to manage through stress.” That may be true. But the question your board should be asking is not whether you still have to comply. The question is whether your systems are genuinely resilient, or whether you were only resilient enough to pass the exam.

Sources: OCC.gov | American Banker

Don’t blink—the banking Singularity is accelerating.

If you’re finding these breakdowns valuable, give it a Like and Subscribe so we can keep the real conversations going.

Read the companion news of the week post behind this episode: https://coresystempartners.com/core-insider/whats-happening-in-core-banking-this-week-37

RBC is a good example. After years of heavy investment, they’re now projecting up to $1 billion in value from AI by 2027. And in capital markets, analysts are already expanding coverage using AI — from about 1,500 companies a couple of years ago to 1,700 today, with more to come. At some point, it stops being innovation and just becomes how work gets done.

At the same time, regulators are changing their tone.

The U.S. Treasury is now openly saying that not adopting AI can be a risk — to efficiency, to resilience, to competitiveness. That’s a subtle shift, but an important one. We’ve gone from “be careful with AI” to “you probably need to be using this.”

And they’re backing that up with action. AI-driven decisions — onboarding, fraud detection, payments — are increasingly being treated like real banking decisions, which means they need to be explainable, auditable, and regulator-ready.

Meanwhile, startups are moving fast into the gaps.

Companies like Obin are automating large parts of compliance workflows with agent-based systems — delivering real efficiency gains, but also raising the usual questions around governance.

And then there’s agentic commerce — where most players say they’re ready… but integration, identity, and liability still haven’t caught up. So the takeaway this week is simple: AI is no longer optional — but neither is getting it right.

If you’re finding these breakdowns helpful, go ahead and Like and Subscribe so we can keep the conversation going.

Don’t blink — the banking singularity is accelerating.

And remember: Transformation starts with insight, so Stay Sharp and Stay Core.

RBC Thinks a Decade of AI Spending Might Finally Start Looking Like Strategy Instead of Expensive Optimism

Fresh coverage says RBC expects AI to generate CAD $700M to CAD $1B in revenue and cost savings by 2027, after already spending more than CAD $5B a year on technology and holding a top-three spot on the Evident AI Index for four straight years. Inside capital markets, AI-assisted analysts now cover about 1,700 companies, up from roughly 1,500 in 2023, with a goal of 2,500—translation: the productivity story is getting real enough that even bankers can’t keep calling it a lab experiment.

Banks are earning massive underwriting fees structuring these deals. Bridgewater Associates called this a “more dangerous phase” — the same banks writing the checks are financing companies whose AI capabilities may ultimately displace them.

Sources: American Banker

U.S. Treasury Just Opened an AI Innovation Series, Which Is Government for “Fine, We Should Probably Stop Pretending This Is Optional”

In a March 23 announcement, the Treasury Department and FSOC launched an AI Innovation Serieswith four roundtables bringing together financial institutions, tech firms, regulators and specialists to focus on practical AI deployment across fraud detection, underwriting, cybersecurity and operational risk. The bigger signal is the tone: Treasury explicitly said failure to adopt productivity-enhancing AI can itself become a risk to efficiency and resilience, while its AI Transformation Office is now pushing governance that evolves alongside deployment — basically, here’s the thing: regulators are no longer just asking whether AI is dangerous; they’re starting to ask whether not using it is its own kind of incompetence.

Sources: US Treasury

Regulators Are Starting to Treat AI Outputs Like Real Banking Decisions—Because, Awkwardly, That’s What They Are

An nalysis says nearly three-quarters of finance leaders were already using AI in 2024, 68% of institutions have increased fraud-detection spending, and 71% of fraud incidents and losses now come from unauthorized-party fraud. The important shift is strategic, not poetic: banks’ AI-driven onboarding, fraud and payment decisions are increasingly being judged under existing financial rules rather than some magical “tech exception,” which means models now need to be explainable, auditable and regulator-proof—whether that actually happens or not.

Sources: PYMNTS

Obin AI Raised $7 Million by Promising Banks an Agent Workforce That Knows What Compliance Is

Seed-funding roundup, Obin AI was reported to have raised $7M, with backing led by Motive Partners plus strategic angels including Fei-Fei Li and Lukasz Kaiser. The company says its founding team already has the trust of institutions representing more than $1T in AUM, and one deployment automated 50,000+ annual loan notices for a $500B private-credit fund, cutting processing time by 70%–90%—which is the kind of number that gets every ops executive interested right up until someone says “model governance.”

Sources: Qubit Capital

Agentic Commerce Looks “Ready” on Paper, Which Usually Means the Liability Questions Haven’t Started Yet

PYMNTS/Visa Acceptance Solutions study found 80% of acquirers say they’re ready for agentic commerce, based on a survey of 75 acquirers across Brazil, the UAE and the U.S., fielded from Jan. 16 to Jan. 30. But the same report says merchant integration costs, legacy systems, identity checks and liability rules are still slowing adoption—so yes, the rails may be ready, but “the rails are ready” and “the ecosystem is ready” are not the same sentence, no matter how confidently someone says them onstage.

Sources: PYMNTS

Don’t blink—the banking Singularity is accelerating.

If you’re finding these breakdowns valuable, give it a Like and Subscribe so we can keep the real conversations going.

Read the companion news of the week post behind this episode: https://coresystempartners.com/core-insider/whats-happening-in-core-banking-this-week-36

Mastercard made one of the more interesting moves — building a proprietary AI model trained not on text, but on billions of payment transactions. Not an LLM — a tabular model designed to understand commerce itself. Early results show better fraud detection, especially in edge cases that typically trigger false positives.

That’s a signal: domain-specific AI is starting to outperform general-purpose tools.

At the same time, Veriff is pointing out something the industry doesn’t want to admit — we’re solving the wrong fraud problem. Deepfakes get the headlines, but the real issue is fragmented identity systems. When one in 25 identity checks involves fraud, the problem isn’t just the model — it’s the architecture.

Then you’ve got Bank of America doubling down on a “build once, reuse everywhere” strategy. One shared AI platform across 60,000 employees. They tested decentralization — and found teams were just rebuilding the same thing. So they pulled it back to control and scale.

Meanwhile, private fintech valuations are… getting stretched. Up over 160% year-over-year, while public comps barely moved. AI is doing a lot of narrative work there.

And underneath it all, Capgemini is reporting that 80% of bank executives still see no revenue lift from AI, largely because legacy systems are eating the budget.

So the takeaway this week is simple: building AI is easy — capturing value from it is not.

If you’re finding these breakdowns helpful, go ahead and Like and Subscribe so we can keep the conversation going.

Don’t blink — the banking singularity is accelerating.

And remember: Transformation starts with insight, so Stay Sharp and Stay Core.

Mastercard Just Built a Payments AI Brain — And It’s Not an LLM

Mastercard dropped a pretty significant announcement yesterday: it’s building a proprietary generative AI foundation model trained not on text, but on billions of anonymized payment transactions — a Large Tabular Model (LTM), not an LLM — with plans to scale to hundreds of billions of transactions using Nvidia and Databricks infrastructure. Here’s the thing — most banks and networks are just bolting ChatGPT wrappers onto their existing pipelines; Mastercard is going a completely different direction, training a domain-specific model that learns to predict transaction characteristics the same way an LLM predicts the next word in a sentence. The first use case is cybersecurity, where early testing already showed the LTM outperforming standard ML techniques — specifically on high-value, low-frequency purchases like wedding rings that trigger false positives constantly. Translation: a model that understands commerce at the structural level, not just the text level. That’s a different game entirely.

Sources: Mastercard

Veriff: The Real Identity Threat Goes Beyond Deepfakes

Veriff’s CTO Hubert Behaghel made a pointed argument in PYMNTS’ “What’s Next in Payments” series yesterday that the entire industry is misdiagnosing AI fraud risk — focusing obsessively on deepfakes while ignoring that deepfakes only attack one layer (computer vision) of a much deeper identity problem. According to Veriff’s 2026 Identity Fraud Report, roughly 1-in-25 online identity verification attempts now involves fraud. The real vulnerability, per Behaghel, is siloed identity architectures that check each signal independently rather than cross-correlating thousands of data points — device intelligence, network behavior, media inputs — together. And here’s the kicker he dropped: KYC data providers themselves are becoming high-value attack targets, because fraudsters know that owning a verification provider’s dataset unlocks a master key to synthetic identity fraud at scale. Basically, the identity infrastructure we built for humans is not ready for the age of AI agents making purchases autonomously — and most banks haven’t started building the accountability chain that agentic commerce will require.

Sources: PYMNTS

Bank of America’s “Build Once” AI Strategy: Discipline or Moat?

Fortune ran a detailed inside look at BofA’s AI architecture yesterday, and the headline is this: while the rest of the industry was running 17 parallel vendor pilots and rebuilding the same LLM plumbing in every division, BofA CTO Hari Gopalkrishnan has been running a “build once, reuse forever” model since 2018 — and just rebuilt it for the gen AI era in 2025. The bank put that discipline to the test, running 15 separate proofs of concept to see whether letting individual units “go rogue” with their own AI stacks would outperform the shared Erica platform — and found they were mostly just reinventing the same infrastructure. The shared platform now supports 60,000 tech staff across consumer, wealth, capital markets, and private banking, all on the same model-agnostic architecture. The tradeoff, as Gopalkrishnan admits, is that you need “institutional patience” — which is a polite way of saying the bank has to say no to a lot of vendors with flashy demos on a regular basis. Whether that plays out as a competitive advantage or an innovation bottleneck is the multi-billion dollar question.

Sources: Fortune

The Fintech Bubble Keeps Growing—Fueled by AI Hype

Forbes came out swinging yesterday with a data-heavy takedown of private fintech valuations — and the numbers are wild. Stripe, which brought in $6.9 billion in net revenue in 2025 (+30% YoY) and processed $1.9 trillion in payments, is currently valued at $159 billion in private markets — nearly 5x its closest public competitor Adyen, which processed similar volumes. Ramp claimed $1 billion in “gross revenue” and fetched a $32 billion valuation — but that gross figure doesn’t subtract the interchange fees and rewards returned to banks and customers, meaning actual net revenue is likely 40%+ lower and the real multiple is closer to 50x, “reminiscent of fintech’s 2021 bubble days” per Forbes. Meanwhile, the top-10 private fintechs’ collective market value jumped 164% over the past 12 months, versus a 2% rise for the top-10 public fintechs. Translation: private fintech is a luxury-goods market right now, where prestige and scarcity are driving prices in ways that have almost nothing to do with underlying economics — and the AI narrative is doing the heavy lifting. The reckoning, when it comes, will be cinematic.

Sources: Forbes

Capgemini: 80% of Bank Execs See No AI Revenue Gains

A Capgemini Research Institute report released Monday — based on 150 senior leaders at corporate and investment banks plus 600 non-bank financial execs — landed with a thud: more than 4-in-5 CIB executives said new product implementations aren’t boosting revenue, and 51% admitted new products didn’t deliver expected cost savings either. Here’s the structural problem: only 29% of annual IT budgets are allocated to transformative technologies, while 43% goes to maintaining legacy systems — which is basically the definition of running to stand still. The customer demand mismatch makes it worse: more than half of CIB customers want real-time responsiveness, 49% want personalized engagement, and 40% want innovative products — but fewer than 20% say banks are actually meeting those expectations. Revenue growth is projected to decelerate from 6.5% (2022–2024) to 5.4% over the next five years as a result. Basically, banks are spending billions on AI while their foundational infrastructure quietly cannibalizes the return. The pilots look great in the boardroom deck; the P&L doesn’t agree.

Sources: Yahoo Finance

Don’t blink—the banking Singularity is accelerating.

If you’re finding these breakdowns valuable, give it a Like and Subscribe so we can keep the real conversations going.

Read the companion news of the week post behind this episode: https://coresystempartners.com/core-insider/whats-happening-in-core-banking-this-week-35

Amazon is borrowing $37 billion to build AI infrastructure. Not spending cash — borrowing. The bond sale attracted $126 billion in demand in a single day. Salesforce is raising $25 billion. Oracle is lining up $45 to $50 billion. Total Big Tech AI spending hits $600 billion this year — up 46% from last year.

Who’s underwriting these deals? Banks.

The same banks that are paying Wall Street fees to finance companies whose AI will eventually come for their revenue. Bridgewater called it a more dangerous phase. I’d call it irony at scale.

Meanwhile, the FDIC just removed the last excuse.

Chairman Travis Hill told banks directly: stop letting fear of regulators block AI adoption for compliance. He said AI catches suspicious activity faster, reduces false positives, and the FDIC is not going to penalize you for deploying it. Two regulatory clarifications followed.

If your bank is still running rule-based AML in 2026, that’s a choice — not a constraint.

And then there’s Santander.

$12.2 billion for Webster Bank. Capital One grabbing Brex for $5 billion. NatWest, community banks — everyone is consolidating, right now, while AI is accelerating.

Banks that haven’t modernized their cores are about to be absorbed by banks that have. Size is no longer protection. Architecture is.

If these breakdowns are useful, hit Like and Subscribe — the signal is only getting louder.

Don’t blink — the banking singularity is accelerating.

Stay sharp and stay core.

Amazon Is Borrowing $37 Billion to Build AI — And Banks Are Cashing the Checks

The world’s largest tech companies have stopped using their own cash to fund AI and started borrowing at scale. Amazon is targeting a $37 billion, 11-part bond sale that attracted $126 billion in peak demand. Salesforce is raising $25 billion. Oracle plans $45–50 billion in combined debt and equity. Meta already raised $30 billion in bonds. Total Big Tech AI capital expenditure is expected to hit $600 billion in 2026, up 46% from $410 billion in 2025.

Banks are earning massive underwriting fees structuring these deals. Bridgewater Associates called this a “more dangerous phase” — the same banks writing the checks are financing companies whose AI capabilities may ultimately displace them.

Sources: Reuters

The FDIC Just Told Banks: Stop Being Afraid to Deploy AI for AML — We’ve Got Your Back

FDIC Chairman Travis Hill delivered an unusually direct speech: stop letting fear of regulators block AI adoption for Bank Secrecy Act and AML compliance. He explicitly addressed the concern that examiners would “play gotcha” when AI tools surface past failures or require costly parallel technology runs.

Hill’s message: AI identifies suspicious activity faster and more precisely than legacy rule-based systems, reducing false positives. The FDIC also issued two regulatory clarifications — allowing pre-population of customer identity data and accepting last-four-digit Social Security Number verification via trusted third parties.

Sources: FDIC

Santander Acquires Webster Bank for $12.2 Billion — The Consolidation Wave Is Here

Santander announced the acquisition of Webster Financial (Webster Bank) for $12.2 billion, expected to close in the second half of 2026. The deal positions Santander as a top-10 US retail and commercial bank and marks a significant Northeast expansion of its US footprint.

This is not an isolated event. Capital One is acquiring Brex for $5.15B. NatWest is acquiring Evelyn Partners for £2.7B. Community banks are acquiring fintech partners outright. The consolidation wave that banking observers have been predicting for a decade is accelerating — and it is accelerating at the same time as AI transformation.

Sources: Investing

Don’t blink—the banking Singularity is accelerating.

If you’re finding these breakdowns valuable, give it a Like and Subscribe so we can keep the real conversations going.

Read the companion news of the week post behind this episode: https://coresystempartners.com/core-insider/whats-happening-in-core-banking-this-week-34

This week, AI in banking moved from experimentation to something much more structural.

Bank of America held its first investor day in nearly 15 years, and AI was right at the center of the strategy. CEO Brian Moynihan is projecting 12% annual EPS growth, with AI driving major productivity gains. Coding workflows alone have been cut by 30%, saving the equivalent of about 2,000 employees. And Erica — their consumer AI assistant — now delivers capacity equal to roughly 11,000 full-time workers.

That’s not a tech demo. That’s operating leverage.

At the same time, Wall Street CEOs are starting to speak more honestly about the workforce impact. Productivity is rising fast — but headcount in operations and support roles is quietly trending down across several major banks. The official message is redeployment. The operational reality is simply doing more with fewer people.

McKinsey also issued a warning this week about what they’re calling “pilot purgatory.” Banks are running AI pilots everywhere, but many still haven’t scaled them into real transformation. The institutions that actually rewire operations around AI are pulling ahead.

Meanwhile, fintech infrastructure players like Plaid are expanding rapidly — with fraud and payments tools growing hundreds of percent — showing how the ecosystem around banks is evolving just as quickly.

And while AI spending continues to surge, credit investors are beginning to ask whether the hype cycle might be running a little hot.

So the story right now isn’t just innovation — it’s execution, governance, and scale.

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Don’t blink — the banking singularity is accelerating.

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BofA Eyes 12% Growth with AI Push

Bank of America held its first investor day in nearly 15 years today, and CEO Brian Moynihan didn’t hold back, forecasting 12% annual earnings-per-share growth over the next three to five years, a ROTCE target of 16-18%, and the consumer bank gunning for $20 billion in profits. AI isn’t a side note here; it’s load-bearing infrastructure for the whole strategy. Moynihan has previously disclosed that BofA used AI techniques to cut 30% out of coding workflows, effectively saving 2,000 people, and that Erica, their consumer AI assistant, is the equivalent of 11,000 full-time employees in saved capacity. The markets business is projecting nearly double revenue growth by 2030, and the investment bank wants to add 50-100 basis points of global dealmaking market share. Whether BofA can actually close the performance gap on its big bank peers, its stock has lagged all five closest rivals over five years, is the real test, but the message today was clear: responsible growth has a new co-pilot, and it runs on AI.

Sources: Yahoo Finance

Wall Street CEOs Get Real on AI and Jobs

Business Insider compiled the most candid CEO statements on record about AI and jobs, and the picture is more nuanced — and more ominous — than the talking points suggest. EY surveyed 240 financial services CEOs and found 60% expect AI to maintain or increase headcount in 2026, but the actual numbers inside the banks tell a different story: JPMorgan operations staff down 4%, Wells Fargo engineers 30-35% more productive with no offsetting hiring, Goldman explicitly constraining headcount growth, and Citi’s CFO expecting headcount to “continue trending down” as AI improves productivity. BofA’s Moynihan put it plainly: coding efficiency gains “saves us about 2,000 people.” Wells Fargo’s Scharf was the bluntest of all — “anyone who says they don’t think they’ll have less head count because of AI either doesn’t know what they’re talking about or is just not being totally honest about it.” Translation: the official line is “we’re redeploying people,” and the actual line is “we’re doing more with fewer people,” and both are true at the same time.

Sources: Business Insider

McKinsey Warns Banks Are Heading Straight for “Pilot Purgatory”

McKinsey dropped a new report this week arguing that banks are among the businesses best positioned to benefit from agentic AI, and simultaneously at most risk of squandering that advantage. The logic: 50-60% of a typical bank’s full-time equivalents are tied to service operations, making banking a prime target for AI-driven transformation second only to technology and engineering itself. But McKinsey’s warning is sharp, too many institutions are experimenting with narrow use cases without scaling, and they’re falling into what the firm calls “pilot purgatory,” where the pilots run forever and the ROI never materializes. To actually generate meaningful value, McKinsey says banks need to rewire entire domains. operations, frontline distribution, technology, data science, risk management. and embed AI at the core rather than bolting it onto processes designed for a different era. Here’s the thing: this isn’t new advice, but coming from McKinsey in early March 2026, it’s a signal that the gap between leaders and laggards is starting to look permanent.

Sources: Banking Exchange

Plaid Hits $8 Billion Valuation as Its Fraud Tools Grow 400%

Plaid quietly closed a new funding round last week that values the company at $8 billion, up from $6.1 billion just ten months ago — and while the primary purpose was employee liquidity rather than capital expansion, the numbers underneath tell the real story. Anti-fraud services grew roughly 400% annually, payments facilitation grew 250%, and together those newer product lines now represent more than 20% of annual recurring revenue, growing at nearly 93% year-over-year. The company connects over 8,000 applications to more than 12,000 financial institutions and has quietly evolved from “just” being banking infrastructure into a full-stack fraud detection, risk management, and credit underwriting platform. Basically, Plaid started as the plumbing that made fintech possible, and it’s now becoming the security system, the payment processor, and the credit engine — and with those growth rates and an IPO path becoming more visible, the $8 billion valuation looks conservative rather than ambitious.

Sources: TechFunding News

Survey: Banks Are Deploying AI Faster Than They’re Governing It

Wolters Kluwer published its Q1 2026 Banking Compliance AI Trend Report this week based on 148 financial institutions, and the findings are a useful reality check on how the industry’s AI ambitions are actually translating into practice. Only 31.8% of institutions surveyed have deployed AI or ML technologies into production — and of those, only 12.2% describe their AI strategy as “well-defined and resourced.” Meanwhile, 46.6% cite operational efficiency as their primary AI goal, but only 35.8% have established internal policies for ethical AI use, and just 26.4% are confident their AI initiatives comply with regulatory requirements. The governance gap is real: 58.8% of respondents said what would most help their AI strategy is clearer regulatory guidance — meaning over half of banks are essentially waiting for regulators to tell them the rules before they’ll commit to a governance framework. That’s not exactly a posture that inspires confidence when the same institutions are simultaneously telling investors how aggressively they’re deploying AI.

Sources: Wolters Kluwer

Credit Investors Sound AI Bubble Alarm

A new Bank of America survey of institutional credit investors found that 23% now say the threat of an AI bubble is their single biggest concern — up sharply from just 9% in December 2025, a near-tripling in three months. These aren’t retail investors momentum-trading AI stocks; these are insurance companies, hedge funds, and pension managers who buy investment-grade corporate bonds and track how much debt companies are taking on to fund AI infrastructure. The context: the four largest AI hyperscalers — Alphabet, Microsoft, Meta, and Amazon — are collectively on track to spend $700 billion on AI capital expenditures in 2026 alone, and all four stocks are down year-to-date. For banks, this matters in two directions: as heavy AI investors themselves, they’re exposed to questions about ROI timelines, and as lenders to the tech sector, they’re watching whether all that AI infrastructure debt starts to look shaky. Whether the bubble concern is prescient or just anxiety is genuinely unclear — but when bond market professionals triple their worry level in three months, that’s worth paying attention to.

Sources: Motley Fool

Don’t blink—the banking Singularity is accelerating.

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Jamie Dimon openly admitted that AI is already displacing workers at JPMorgan — and that they now have formal redeployment plans in place. Headcount looks flat on paper, but operations and support roles are shrinking while client-facing roles are growing. Fraud costs are down. Engineer productivity is up. Generative AI use cases have doubled.

That’s not experimentation. That’s restructuring.

Santander also put a hard number on its AI ambitions — targeting more than €1 billion in annual value by 2028, while closing branches and leaning into digital scale. For a bank of that size, that’s not a headline — it’s a transformation roadmap.

Meanwhile, Deutsche Bank and Goldman are deploying agentic AI to catch rogue traders — shifting from rule-based alerts to autonomous investigative agents. Given the fines banks have paid over the past decade, that’s less innovation… and more risk management.

Even the Fed is embedding AI into performance metrics. This isn’t theoretical anymore. It’s structural.

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Don’t blink — the banking singularity is accelerating.

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JPMorgan Reshuffles Jobs Under Dimon’s Plan

JPMorgan Chase CEO Jamie Dimon confirmed at an investor meeting Monday what most people have been dancing around: AI is already displacing workers at the world’s largest bank by market cap, and the bank now has formal redeployment plans to shift those employees into new roles. The numbers underneath the flat 318,512 headcount tell the real story—operations staff fell 4%, support staff fell 2%, while client-facing and revenue-generating roles grew 4%. Meanwhile, generative AI use cases doubled this year, fraud processing costs dropped 11%, and software engineer productivity is up 10%. Dimon wasn’t just talking about JPMorgan though—he raised a broader societal question using a pointed thought experiment: if autonomous trucks replaced 2 million drivers overnight, and the next available job pays $25,000 stocking shelves, would you still do it? It’s the kind of question most CEOs dodge. Dimon, working with a nearly $20 billion annual tech budget, apparently feels comfortable enough to ask it out loud

Sources: CNBC

Santander Puts a Number on Its AI Bet: €1 Billion by 2028

Santander’s Executive Chair Ana Botin used the bank’s investor day in London today to lay out the math on their AI ambitions—and the math is big. The bank expects AI and data initiatives to generate more than €1 billion in business value annually by 2028, split between cost savings and revenue growth, as part of a broader strategic plan targeting €20 billion in profit and 210 million customers (up 30 million from today’s 180 million). The efficiency angle is equally aggressive: Santander is closing another 44 UK branches—leaving 244 full-service locations—while leaning into becoming a “global digital bank with branches.” Here’s the thing: €1 billion sounds like a headline number, but for a bank with 200,000+ staff across Europe and the Americas, it’s essentially a down payment on a much larger operational transformation. Whether the branch closures and the AI investments combine to actually hit those targets by 2028 remains to be seen, but Botin is clearly betting the strategic plan on it.

Sources: Yahoo Finance

Deutsche & Goldman Deploy AI to Catch Rogue Traders

Both Deutsche Bank and Goldman Sachs disclosed today they’re moving beyond rule-based surveillance algorithms and into agentic AI for trading misconduct detection—and the distinction matters. Deutsche Bank is partnering with Google Cloud to build a large language model that identifies anomalies in orders, trades, and market fluctuations, and will extend the same model to monitor communications between traders, sales staff, and client-facing employees later this year. Goldman Sachs, separately, is exploring agent-based AI to analyze trades and flag suspicious signals or unusual market movements. The key difference from traditional systems: these agents autonomously plan and act rather than just surface data for humans to review. Translation—instead of compliance teams wading through flagged alerts generated by static rules, the AI is doing the investigative legwork. Given that trading misconduct fines have cost global banks tens of billions over the past decade, this is less a tech experiment and more a liability management play.

Sources: Bloomberg

Fed’s Waller Details AI Strategy

Federal Reserve Governor Christopher Waller delivered an unusually candid speech yesterday about the Fed’s internal AI deployment—and it reads less like a policy statement and more like a practical implementation guide. The Fed has built a system-wide general-purpose AI platform for all employees, deployed coding assistants that cut some development tasks from two days to two hours, and embedded AI directly into enterprise workflows across legal, risk, procurement, and operations. One project modernizing hundreds of databases cut completion time by 50% while detecting 30% more issues than previous migrations. Waller’s framing was deliberate: AI use at the Fed is not optional, it’s being built into employee performance goals, and training happens on paid time, not nights and weekends. The bigger subtext here: if the central bank itself is mandating AI adoption and tying it to performance metrics, it’s a signal to every institution the Fed regulates about where the bar is heading.

Sources: Federal Reserve

Don’t blink—the banking Singularity is accelerating.

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RBC just announced a dedicated AI Group reporting directly to the CEO, targeting $1 billion in enterprise value by 2027. And this isn’t from a standing start — they’ve built one of Canada’s largest GPU clusters, have 27,000 employees using their internal AI assistant, and hundreds of AI-related patents. When you create a Group Executive seat for AI, that’s not experimentation. That’s institutional commitment.

TD Bank’s head of AI made another important point this week: model performance isn’t what determines success — integration does. TD generated $170 million in AI value last year, but they’re focused on redesigning workflows, not just plugging AI into legacy processes. That’s the difference between demo and deployment.

Meanwhile, compliance startup Sphinx raised funding for browser-native AI agents that work inside existing systems. No rip-and-replace. That’s why adoption is moving fast. On the policy side, the Fed is openly modeling AI-driven labor disruption. Productivity up, potential unemployment spikes — and monetary policy can’t fix structural displacement.

And JPMorgan analysts are warning that AI capital requirements could force smaller banks into mergers.

This isn’t hype. It’s a competitive reshuffle.

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Don’t blink — the banking singularity is accelerating.

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Stay Sharp. Stay Core.

RBC Forms AI Unit Targeting $1B by 2027

Royal Bank of Canada announced today it’s launching a new AI Group reporting directly to the CEO, led by 12-year tech veteran Bruce Ross. The bank’s targeting up to $1 billion in enterprise value from AI benefits by 2027—which sounds ambitious until you look at the infrastructure they’ve already built: one of Canada’s largest GPU clusters, 27,000 employees using their internal AI assistant RBC Assist, and 1,200+ patent filings since 2019 with 635 related to AI. They’re ranked #1 in Canada and #3 globally for AI maturity, so this isn’t a moonshot from a standing start. The real tell here is that they’re creating a dedicated team to “accelerate” rather than experiment, which suggests they’ve moved past the pilot phase and into actual production deployment. Whether they hit that billion-dollar target is one thing, but the organizational commitment, complete with a new Group Executive seat, indicates they’re betting the farm on AI as a core competitive differentiator.

Sources: RBC

TD Bank Says AI Success Is About Integration

Ted Paris, TD’s senior VP of AI, is pushing back on the hype cycle in an interview published today. His take: AI project failure rates aren’t about getting the model wrong, they’re about how well you integrate these tools into business processes to drive actual adoption. TD implemented 75 AI use cases generating $170 million in value last year and expects $200 million in incremental value in 2026, but Paris is focused on rethinking workflows from the ground up rather than just plugging agents into legacy processes that were designed decades ago. The bank’s deploying everything from generative AI virtual assistants in contact centers to agentic AI for more autonomous tasks, but the emphasis is on involving risk, legal, IT, and change management partners from the beginning. Translation: the hard part isn’t the technology, it’s the organizational transformation required to make it work at scale.

Sources: Banking Dive

Sphinx Raises $7.1M for AI Compliance Agents

Compliance fintech Sphinx closed a $7.1 million seed round led by Cherry Ventures yesterday, and the pitch is straightforward: instead of asking banks to adopt yet another system, their AI agents work directly inside the tools institutions already use, case management systems, PDFs, email, third-party portals. The agents handle AML, KYC, and KYB checks, review alerts, draft RFIs, and create audit trails without requiring integrations or system replacements. Teams typically go live in days, and in production the agents have already processed millions of alerts and hundreds of thousands of cases. One customer, Equals Money, saw a 94% reduction in false positives while catching more true positives. The real innovation here isn’t the AI, it’s the browser-native architecture that lets the software function inside institutional complexity without requiring a rip-and-replace overhaul. That’s why banks and fintechs are adopting it so quickly

Sources: Business Wire

Fed’s Barr Flags AI Job Risks

Federal Reserve Vice Chair Michael Barr delivered a speech yesterday acknowledging that while AI will likely boost productivity and living standards long-term, it could cause serious short-term labor market disruptions—and monetary policy isn’t equipped to fix structural unemployment. He laid out three scenarios: gradual adoption with manageable displacement, rapid adoption triggering a “jobless boom” with widespread unemployment, or stalled progress leading to modest gains and potential financial stress from overinvestment. Early evidence shows AI is already hurting some groups—particularly young workers in early-career roles like software developers and customer service reps—though aggregate employment hasn’t been hit yet. Barr’s concern is that if AI causes permanent demand reduction for many job types, society will need policies beyond the Fed’s mandate to address the fallout. The subtext: central bankers are starting to game out worst-case scenarios, and they’re not dismissing the possibility of major labor market upheaval

Sources: Federal Reserve

JPMorgan: AI Costs May Spur Bank Mergers

JPMorgan Chase analysts warned yesterday that the pace of AI investment required to stay competitive may force smaller banks into mergers, according to Bloomberg. The logic is simple: competitive demands are pushing banks of all sizes to invest in AI, but large banks and some regionals are better positioned to afford it, creating a structural advantage that smaller institutions can’t match. Financial services has already seen stock market selloffs driven by investor fears about AI’s impact on corporate profits, and this analysis suggests the pressure isn’t just about revenue disruption, it’s about the sheer capital requirements to build and maintain AI infrastructure. This isn’t a new M&A driver we’ve seen before; it’s a technology arms race that could reshape the competitive landscape by making scale even more critical than it already was.

Sources: PYMNTS

Don’t blink—the banking Singularity is accelerating.

Read the companion news of the week post behind this episode: https://coresystempartners.com/core-insider/whats-happening-in-core-banking-this-week-31/

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Next, Oracle launched a full agentic AI platform for retail banking. Real-time underwriting, auto-summarized compliance calls, modular, intelligent, scalable. This isn’t automation. It’s architecture with intent. OpenAI’s dominance in banking, it’s slipping. New data shows its share dropped from 50% to 33%. Banks are going model-agnostic, bringing in Anthropic and Gemini. Because AI strategy isn’t about the flashiest model. It’s about who shows up and helps build. Embedded finance is getting radically cheaper. AI is automating underwriting, compliance, and collections end-to-end. Vertical SaaS platforms are embedding full-service financial services without traditional banking infrastructure.

Context, not capital, is becoming the real advantage. And yet, more than half of CEOs still report zero return from AI. No revenue growth, no cost reduction, because they’re bolting AI onto old workflows. The banks seeing returns are redesigning how work actually gets done.

Don’t blink. The banking singularity is accelerating. AI isn’t a differentiator anymore. It’s the baseline. If you found this useful, subscribe to the Core Insider.

We’ll keep the real talk coming. And remember, stay sharp and stay core.

Citi just launched collaborative AI workspaces—today

Citigroup CEO Jane Fraser recently delivered a clear message: AI isn’t taking job, but those who know how to use it will outperform those who don’t. Citi has now mandated AI training, including prompt engineering, for all 175,000 employees across 84 countries. The results are already tangible, with 21 million AI tool interactions and 70% tool adoption across the company.

What sets this initiative apart is Citi’s focus on internal mobility, 50% of new roles are filled by long-time employees. This strategy reduces hiring costs while preserving institutional knowledge. Fraser frames the AI shift as an opportunity for reinvention, emphasizing that the bank’s edge lies in empowering its people to grow with the technology.

Sources: Fortune

Oracle Launches Agentic AI for Retail Banking

Yesterday at Oracle Financial Services Summit in New York, Oracle unveiled its enterprise-class agentic AI platform specifically designed for retail banks, a full ecosystem of autonomous AI agents that handle end-to-end workflows from generating product brochures and tracking loan applications to compliance-checking collection calls and analyzing credit decisions in real time.

Sources: Oracle News

Banks Shift Away from OpenAI

A fresh report from Evident shows OpenAI’s market share in top-tier banking dropped from 50% to 33% in just one year, with Anthropic’s Claude and Google’s Gemini picking up the slack as banks mature their AI strategies and demand more than just model access—they want partnership, deployment support, and architectural expertise. Alexandra Mousavizadeh, CEO of Evident, told Computer Weekly this morning: “The competition is really being fought on who can provide the best partnership. Banks are asking suppliers to send deploying engineers into their bank to help them build the architecture.”

Sources: Computer Weekly

AI Is Making Embedded Finance Cheaper

A new analysis published today highlights how AI is collapsing the cost structure of embedded finance, the integration of lending, payments, and banking into non-financial platforms, where historically you needed heavy infrastructure like banking partnerships, compliance frameworks, underwriting teams, and collections management, but now AI is automating the entire stack. Vertical SaaS companies in healthcare, real estate, and professional services are embedding working capital loans and deposit accounts into their software with minimal human intervention, underwriting becomes algorithmic, compliance monitoring auto-adapts, collections strategies adjust dynamically to borrower behavior, and because these platforms already have deep behavioral data like cash flow patterns, invoice timing, and seasonal trends, their AI models can out-underwrite standalone lenders using far richer context.

Sources: FinTech Bloom

The ROI Paradox Continues

Even with all this momentum, the core challenge hasn’t changed: 56% of CEOs still report zero financial return from AI investments, zero revenue growth, zero cost reduction—while the 12% seeing both embedded AI into workflows, not just bolted it onto legacy processes, redesigning how work gets done, not just how fast it gets done. Forbes McKinsey’s estimates remain both carrot and stick: $200B–$340B in annual value creation for banking, but up to $170B in profit erosion if business models don’t adapt, with AI budgets hitting 5% of total spend this year and global AI spending forecast at $2.52 trillion, up 44% year-over-year.

Sources: Forbes | McKinsey Global Banking Review | Finastra | FinTech Magazine | Reuters

Don’t blink—the banking Singularity is accelerating.

Read the companion news of the week post behind this episode: https://coresystempartners.com/core-insider/whats-happening-in-core-banking-this-week-29/

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