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
Trust is becoming a competitive advantage in AI-first banking because model capability is commoditizing faster than governance capability, meaning institutions that can deploy AI quickly and defensibly will out-execute competitors with access to the same underlying... View
Data governance in banking is the discipline of ensuring data is accurate, complete, and reliable enough for every system, human or AI, that depends on it to make a decision, aggregate risk, or generate a regulatory report More than a decade after the Basel Committee... View
The AI trust gap in banking is the space between what a bank’s AI systems are technically capable of and what the institution is actually willing to let them do unsupervised, and for the large majority of banks today, it is a bigger barrier to scaling AI than... View
Regional banks are not behind on AI in software delivery. They are ahead. The real question is whether test automation moved with them. Most bank technology leaders have this backwards. The assumption, understandably, is that regional banks and credit unions are playing catch-up on AI, that megabanks with larger technology budgets are further along. The […]
Development moved faster this year. Most quality engineering functions did not move with it. Here is how to tell if yours is one of them. Ask most CIOs, CTOs, and Heads of Engineering at regional and challenger banks whether their development teams got faster this year, and the answer comes quickly: yes, obviously. Ask the […]
Banks increasingly consume AI through vendor APIs, SaaS platforms, cloud services, and embedded features. Proprietary architecture can limit visibility, but it does not transfer accountability. SR 26-2 states that model risk principles remain applicable to vendor products and calls for institution-specific understanding, validation, monitoring, and outcome analysis. A vendor model card is an input to […]