Banks

Banks are financial institutions subject to some of the most stringent AI governance requirements globally, including regulations from banking authorities like the Federal Reserve and the European Central Bank. Their use of AI in lending, fraud detection, risk assessment, and customer service creates substantial compliance obligations around algorithmic transparency, bias mitigation, and consumer protection. Bank-specific AI governance frameworks are critical because algorithmic errors in credit decisions or automated systems can have widespread economic consequences and expose institutions to significant regulatory penalties.

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ResearchUS2026-09-01

SR 26-2 Forces Banks to Rethink Model Governance From Inventory to Board Oversight

The OCC's revised model risk management guidance, SR 26-2, resets supervisory expectations for U.S. banks by shifting to a materiality-based approach that covers both traditional statistical models and AI systems. CRA's practitioner analysis identifies four practical redesign areas: inventory scope, model tiering, validation, and governance alignment. Banks that operate AI governance and model risk management as separate programs face immediate pressure to harmonize them.

model risk management SR 26-2 banks OCC model validation inventory financial services AI governance