The Federal Reserve and OCC's model risk management guidance applies to AI systems as much as traditional models. Most financial institutions don't have the documentation, validation workflows, or governance infrastructure that examined AI requires.
Deep learning models used for credit decisions, fraud detection, or trading are subject to SR 11-7. Without independent model validation, ongoing monitoring procedures, and challenger models, you're creating examined risk.
Any AI system that touches financial reporting or internal controls falls under SOX. These systems need complete decision audit trails, change management controls, and documented human oversight procedures.
AI-driven underwriting, pricing, and credit decisions carry ECOA and fair lending liability if disparate impact analysis isn't built into the model development and monitoring lifecycle from day one.
Financial services AI sits at the intersection of banking regulation, securities law, and emerging AI-specific guidance. Praxient's readiness work covers the regulatory requirements that matter to your examiners.
The foundational guidance for model governance. Covers model inventory, independent validation, ongoing monitoring, and the escalation procedures examiners check during safety and soundness reviews.
AI systems integrated into financial close processes, reporting pipelines, or internal control monitoring require documented change management, audit trails, and human-in-the-loop oversight procedures.
SEC's evolving AI guidance for registered investment advisers and broker-dealers. Covers conflicts of interest in AI-driven recommendations, disclosure requirements, and supervisory obligations.
Disparate impact testing, adverse action notice requirements for AI-driven credit decisions, and the explainability obligations that apply when AI influences consumer credit outcomes.
Map every AI and ML model against SR 11-7 requirements. Identify data quality gaps in model training sets, feature engineering processes, and model monitoring pipelines that examiners will flag.
Rank AI opportunities by regulatory complexity and risk tier — separating high-scrutiny use cases (credit decisions, trading) from lower-risk applications so you sequence the hard work correctly.
Build SR 11-7-aligned governance: model cards, independent validation procedures, challenger model requirements, ongoing performance monitoring, and fair lending disparity testing protocols.
A phased deployment plan structured around your exam cycle and regulatory obligations — ensuring AI systems are documentable, auditable, and defensible before they go into production.
Our 17-question assessment covers data quality, infrastructure, governance, and team readiness — calibrated for regulated financial institutions. Get a scored report with prioritized recommendations in under 5 minutes.
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