Almost every AI governance framework in banking today – including the regulatory instruments written to enforce them – assumes the same basic shape of decision: a model produces an output, a human reviews it, and the human is the accountable party if... View
Ask a CIO who owns AI governance at their bank, and the honest answer is often some version of “it depends which model you mean.” The customer-service copilot sits with the digital team. The fraud model sits with the risk function. The credit-decisioning model has... View
Ask most banking CIOs where their institution sits on AI governance maturity, and you’ll generally get a confident answer. Ask them to prove it with production evidence rather than a policy document, and the confidence tends to thin out considerably. That gap –... View
If you’ve been tracking AI regulation in banking one headline at a time, this year has been genuinely difficult to keep straight – a Federal Reserve guidance update, a European deferral that has been widely misread, and a CFPB circular that closes a door some... View
Every AI governance conversation in banking starts from the same regulatory baseline. The Federal Reserve’s SR 26-2, the EU AI Act, the UK’s PRA SS1/23 – none of these instruments carve out a lighter standard for a $30 billion regional bank than they do for a... View
Regression testing in banking often presents an uncomfortable tradeoff. Run the full suite and feedback may arrive too late. Run a reduced suite and teams may worry that the wrong scenario was excluded. The root problem is not always execution capacity. It is weak selection intelligence. Static regression packs were built for a more predictable […]
Release velocity is visible. Release confidence is harder to measure. A bank can reduce build time, automate deployment, and execute thousands of tests, yet still hesitate at the final release decision. The remaining uncertainty often concerns what the metrics do not reveal: untested dependencies, incomplete business scenarios, recurrent defect hotspots, or critical knowledge that never […]
Many banks have spent years expanding test automation. Yet release decisions can still involve lengthy reviews, oversized regression packs, manual impact analysis, and calls to the same subject matter experts. That is not evidence that automation failed. It is evidence that the problem changed. AI-first banking introduces faster change across increasingly connected systems. The quality […]