AI Compliance in Financial Services

Financial services is one of the most heavily regulated industries in the world — and it's also one of the fastest adopters of AI. From fraud detection and credit scoring to customer service chatbots and algorithmic trading, AI is embedded across banking, insurance, and wealth management. The compliance implications are enormous.

Regulators are paying attention. The OCC, FFIEC, SEC, and international bodies are issuing guidance on AI model risk management, algorithmic fairness, and explainability requirements. Financial institutions that don't build robust AI compliance programmes face regulatory action, enforcement fines, and loss of consumer trust.

This guide maps the regulatory landscape for AI in financial services, identifies the key compliance risks, and provides a practical framework for building an AI compliance programme that satisfies regulators while enabling innovation.

The Regulatory Landscape for AI in Financial Services

There's no single "AI regulation" for financial services. Instead, AI compliance sits at the intersection of existing regulations and emerging AI-specific guidance:

Existing Regulations That Apply to AI

• SOX (Sarbanes-Oxley): Requires internal controls over financial reporting. AI systems that generate, process, or summarise financial data fall under SOX controls. Audit trails and model documentation are essential.
• PCI DSS: If AI systems process, store, or transmit cardholder data, they're in scope for PCI DSS compliance. This includes AI-powered fraud detection systems and payment processing chatbots.
• GLBA (Gramm-Leach-Bliley Act): Mandates financial institutions protect consumer financial information. AI tools that access customer data — including CRM assistants and analytics platforms — must comply with GLBA's safeguards rule.
• ECOA and Fair Lending Laws: The Equal Credit Opportunity Act prohibits discrimination in lending. AI credit scoring models must be demonstrably fair, unbiased, and explainable — a significant challenge for complex ML models.
• GDPR and Privacy Regulations: For institutions operating in the EU or handling EU citizen data, GDPR's right to explanation (Article 22) directly impacts automated decision-making systems.

AI-Specific Guidance

• FFIEC Guidance: The Federal Financial Institutions Examination Council has published guidance on model risk management (SR 11-7) that applies directly to AI and ML models. Examiners are increasingly scrutinising AI systems during regulatory exams.
• OCC AI Guidance: The Office of the Comptroller of the Currency expects banks to manage AI risks through existing risk management frameworks, with enhanced requirements for model validation and governance.
• EU AI Act: Classifies credit scoring and insurance pricing AI as "high risk," requiring conformity assessments, transparency obligations, and human oversight.

For a comprehensive glossary of these regulatory terms, visit the Aona AI Glossary.

Key AI Compliance Risks in Financial Services

1. Model bias and fairness: AI credit scoring, underwriting, and pricing models can embed or amplify bias against protected classes. Regulators are actively testing for disparate impact.
2. Explainability gaps: Consumers have a right to understand why they were denied credit or charged a specific insurance premium. Black-box AI models make this explanation difficult or impossible.
3. Data leakage through AI tools: Employees using ChatGPT, Copilot, or other AI assistants may inadvertently send customer financial data, account numbers, or trading strategies to third-party AI providers.
4. Third-party AI risk: Financial institutions increasingly consume AI through vendor APIs. OCC and FFIEC expect the same rigor in third-party AI oversight as with any critical vendor.
5. Audit trail deficiencies: AI systems that make or inform decisions about customers need comprehensive logging. Without audit trails, you cannot satisfy examiner requests or respond to consumer complaints.

Building an AI Compliance Framework for Financial Services

1. AI Inventory and Classification

Start by cataloguing every AI system — both officially deployed and shadow AI adopted by business units. Classify each by:

• Risk tier (high/medium/low based on data sensitivity and decision impact)
• Regulatory scope (which regulations apply — SOX, PCI, GLBA, ECOA, etc.)
• Consumer impact (does it directly affect customer outcomes like credit decisions?)

2. Model Risk Management (SR 11-7 Alignment)

For AI models that drive business decisions, implement a model risk management programme aligned with SR 11-7:

• Model development documentation — training data, methodology, assumptions, limitations
• Independent model validation — separate team reviews model performance, bias, and stability
• Ongoing monitoring — track model drift, performance degradation, and emerging bias
• Model inventory — centralised register of all models with risk ratings and review schedules

3. Data Governance Controls

Financial data used in AI systems requires strict governance:

• Data classification and labelling for all AI training and input data
• Data lineage tracking — know where data came from and how it was transformed
• Cross-border data transfer controls for global institutions
• Retention and disposal policies aligned with regulatory requirements

4. Vendor AI Due Diligence

Third-party AI services require enhanced due diligence. For each AI vendor, assess:

• How is customer data used — is it used to train models? Can you opt out?
• Where is data processed and stored? Does it meet your data residency requirements?
• What security certifications does the vendor hold (SOC 2, ISO 27001, etc.)?
• What happens to your data if the vendor relationship ends?

For a structured vendor assessment approach, see our AI governance templates.

Practical Steps for Different Financial Sub-Sectors

Banking

• Prioritise explainability for credit decisioning models — regulators will ask for adverse action reasons
• Implement robust controls around AI access to core banking systems and customer accounts
• Ensure BSA/AML AI systems have documented validation and human oversight protocols

Insurance

• Document AI underwriting model fairness testing — state regulators are increasingly focused on algorithmic discrimination
• Maintain explainability for claims adjudication AI — consumers must understand why claims are denied
• Track NAIC Model Bulletin on AI/ML — state adoption of this guidance is accelerating

Wealth Management

• AI-generated investment advice must comply with fiduciary duty requirements
• Robo-advisor AI systems require SEC/FINRA registration and compliance
• Ensure AI does not create suitability issues by recommending inappropriate products based on algorithmic patterns

For sector-specific guidance, explore our financial services industry guides.

Preparing for Regulatory Examinations

Financial institution examiners are increasingly asking about AI. Be ready for these questions:

1. Provide a complete inventory of all AI and ML models in production
2. Demonstrate model validation procedures and results for high-risk AI systems
3. Show fairness testing results for any AI involved in consumer-facing decisions
4. Provide documentation of human oversight mechanisms for AI-driven decisions
5. Demonstrate third-party AI vendor risk assessments and ongoing monitoring
6. Show your AI governance policy, including roles, responsibilities, and escalation procedures

Pro tip: Don't wait for the examination. Run a mock AI examination using examiner guidance documents. Identify gaps before regulators do. Our compliance templates include examination readiness checklists.

How Aona AI Supports Financial Services Compliance

Financial institutions need AI governance that operates at the speed and rigour regulators expect. Aona AI's platform is built for this:

• Complete AI inventory with regulatory classification — know exactly which regulations apply to each AI system
• Automated risk assessments aligned with SR 11-7, NIST AI RMF, and EU AI Act requirements
• Examination-ready reports that satisfy OCC, FFIEC, and state regulatory requirements
• Continuous monitoring for new AI tool adoption, policy violations, and emerging compliance gaps

Explore our comparison guides to see how Aona compares to other AI governance solutions, or get started with our free financial services compliance templates.