Original research, curated industry news, and practical tools for bank and credit union executives — grounded in regulatory data, not vendor pitches.
Insights
Short-form analysis drawn from our ongoing research program — written for decision-makers, sourced from regulators and primary data, updated as the landscape shifts.
Why fraud detection is banking's highest-ROI AI use case — and why the same technology is now arming the fraudsters on the other side of the transaction.
Read the insight →The Massachusetts AI underwriting settlement, the March 2026 DOJ fair-lending settlement, and what both mean for any institution using algorithmic underwriting.
Read the insight →The OCC/Fed/FDIC's revised model risk guidance explicitly excludes generative and agentic AI. Here's what that gap means for your institution right now.
Read the insight →A 2026 scorecard ranking Fiserv, FIS, Jack Henry, Nymbus, Temenos, and more on the five capabilities that actually determine AI readiness for community banks.
Read the insight →Why AI-driven personalization only works when customers trust how their data is being used — and how leading institutions are earning that trust.
Coming soonHow autonomous AI agents are restructuring back-office workflows — and the human-oversight questions every institution should be asking before deploying them.
Coming soonTools
Free, self-service estimates built from the same data behind our research. Nothing you enter here is transmitted anywhere — the math runs entirely in your browser.
Institutions deploying AI-driven fraud detection report a 25–40% reduction in realized fraud losses. Estimate what that range could mean for your institution.
Directional estimate only, not a guarantee of results. Actual performance depends on your model, data quality, and existing fraud program.
Six questions examiners are already asking in the field. Check the ones that are true today for a directional read on where your institution stands.
Industry News
A running, hand-curated list of the developments we think matter most — updated periodically, with our take on why each one is worth your attention.
The ESRB and Bank of England both published reports on AI's ability to accelerate cyberattacks against financial institutions; the ECB has given 110 EU banks until the end of October 2026 to submit AI-cybersecurity action plans.
Why it matters: expect a similar supervisory push in the U.S. even before a formal rule requires it.
PYMNTSThe OCC and Federal Reserve have made AI governance a permanent agenda item in periodic exams — asking about technical constraints, human oversight, and "kill switch" mechanisms.
Why it matters: this is already happening in the field, regardless of what formal AI-specific rules eventually say.
IndexBoxThe revised guidance (OCC Bulletin 2026-13) applies to institutions over $30B in assets and covers traditional and machine-learning models. Generative and agentic AI are named as out of scope, with a request for information still to come.
Why it matters: full breakdown in our own analysis, linked above.
OCC Bulletin 2026-13AI agents are beginning to automate commercial lending, insurance claims, and underwriting — Moody's reportedly cut credit-memo preparation from 40 hours to two minutes using AI agents.
Why it matters: the efficiency gain is real, but so is the oversight gap the FSB is flagging.
PYMNTSA reminder that federal fair-lending enforcement remains active even as some agencies have pulled back from disparate-impact theories more broadly.
Why it matters: don't read reduced federal appetite in one area as reduced enforcement risk overall — see our credit decisioning insight above.
nContractsA reported internal Treasury analysis warns that AI firms are now more deeply embedded in the U.S. economy than dotcom-era companies were, posing financial-stability risk if productivity gains don't materialize.
Why it matters: worth watching if your institution has direct exposure to AI-sector lending or investment.
NOTUSCornerstone Advisors' 2026 "What's Going On in Banking" research finds lending is now credit unions' third most common AI use case (46%), behind contact centers and fraud management.
Why it matters: AI adoption in lending is now mainstream, not experimental — governance needs to keep pace.
Cornerstone Advisors / PR NewswireThe Flagship Report
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