Banks can automate compliance yet still fail to defend it. Examiners demand proof of why a decision was made and whether the model stayed within approved limits. A Wolters Kluwer survey found 31.8% of banks use AI or ML in production. Only 12.2% rate their AI strategy as well-defined. Deployment is outpacing governance. The Federal Reserve’s SR 26-2 guidance now sets risk-based rules for model use. It applies most to banks over $30 billion in assets. AML, credit compliance and regulatory reporting each face explainability gaps. AI alerts lack the clear logic of rules-based systems. Credit decisions must still meet Regulation B’s specific reason requirements. Regulatory reporting needs traceable data lineage to pass BCBS 239 tests. Maveric Systems proposes Continuous Compliance Assurance as the solution. It requires permanent audit readiness and decision-level explainability.
What changed
Banks relied on annual model validation and quarterly risk assessments for compliance.
What it unlocks
Banks can now maintain audit-ready compliance as a permanent state with continuous monitoring and explainability.
- 31.8% had AI or ML in production
- 12.2% called AI strategy well-defined
- 148 financial institutions surveyed
- $18.3 trillion unmonitored by TD Bank
What you need to act on it
- AI or ML models in production
- governance frameworks aligned with SR 26-2
- continuous monitoring infrastructure
Sources