Industries · Financial Services
Financial Services AI Assurance
Credit decisioning, fraud detection, model risk management, and fair lending compliance — financial services AI operates under SR 11-7, ECOA, and Fair Housing Act obligations that make independent model validation and bias evaluation a regulatory necessity, not a best practice.
AI risk in financial services
AI systems in financial services make consequential decisions at scale: credit approvals, fraud flags, risk scores, and customer triage. The regulatory framework is dense: SR 11-7 requires independent model validation and ongoing monitoring for models used in decision-making at regulated institutions; ECOA and the Fair Housing Act prohibit lending discrimination, including disparate impact from algorithmic decision systems; and CFPB guidance increasingly addresses AI-specific issues in consumer finance.
Bias in financial AI is a distinct technical and legal problem. AI models can produce disparate outcomes across protected classes without using protected characteristics as inputs, through proxy variables that encode demographic correlates. Independent evaluation against fair lending standards requires testing across protected characteristics and their proxies — a different methodology than conventional model validation.
Financial institutions also face emerging risks from LLM deployment: customer-facing AI systems that can be manipulated to produce advice outside regulatory scope, or that expose customer financial data through adversarial prompting. These require adversarial testing frameworks specific to financial services contexts.
Services most relevant to financial services
AI Model Evaluation →
SR 11-7 aligned independent validation, fair lending bias evaluation, and ongoing performance monitoring.
LLM Red-Teaming →
Adversarial testing for customer-facing financial AI against data extraction, manipulation, and out-of-scope advice risks.
NIST AI RMF Assessment →
AI risk framework alignment for institutions subject to federal oversight.
AI Governance Advisory →
Model risk governance structure and AI oversight framework design.
Regulatory context
Financial services AI operates under model risk management guidance (SR 11-7, OCC 2011-12), fair lending law (ECOA, Fair Housing Act, with CFPB enforcement), and emerging AI-specific regulatory guidance from the CFPB, OCC, and federal banking agencies. State-level AI regulation in financial services is also developing, with requirements varying by jurisdiction. We track the current state of this landscape and calibrate our work accordingly.
Frequently asked questions
What is model risk management in financial services?
Model risk management (MRM) in financial services is the discipline of identifying, measuring, and controlling the risks that arise from using quantitative models to make decisions. SR 11-7, issued by the Federal Reserve and OCC, is the primary framework. It requires independent model validation, ongoing monitoring, and documentation of model limitations. AI systems used in credit decisioning, fraud detection, and risk assessment fall under MRM obligations for regulated financial institutions.
What fair lending risks do AI systems create?
AI systems used in credit decisions can create disparate impact — systematically disadvantaging protected classes — even when they do not use protected characteristics directly. This occurs because AI models can learn proxy variables that correlate with race, sex, age, or national origin. Under the Equal Credit Opportunity Act and Fair Housing Act, disparate impact is actionable regardless of intent. Bias evaluation for financial AI requires testing across protected characteristics and proxy variables, not just feature selection review.
How does LLM red-teaming apply to financial services AI?
Financial institutions deploying LLM-backed applications — customer service assistants, document analysis tools, advisory interfaces — face adversarial risks specific to their context. These include extraction of customer financial data through prompt injection, manipulation of the model to produce advice outside regulatory scope, and system prompt extraction that exposes proprietary business logic. Red-teaming for financial AI is scoped to these specific risk categories rather than generic attack taxonomies.