Govern AI across banking, insurance, and asset management. Meet APRA CPS 234, MAS TRM, PRA SS1/23, and Basel Committee requirements while enabling AI adoption safely.
Financial institutions face unique AI governance challenges across trading, lending, operations, and customer service that traditional controls cannot address.
Traders, analysts, and operations staff are using AI tools without approval -- pasting market-sensitive data, customer records, and proprietary models into ChatGPT and AI coding assistants. Every unsanctioned AI interaction is an untracked data flow and a potential regulatory breach.
Shadow AI exposes trading strategies, customer PII, and proprietary algorithms to uncontrolled third-party services.
AI models driving credit scoring, loan approvals, and risk assessments introduce algorithmic bias risks and explainability challenges. Regulators demand that firms demonstrate fairness, transparency, and human oversight in AI-driven financial decisions affecting consumers.
Unexplainable AI credit decisions can trigger fair lending violations and regulatory enforcement actions.
Customer account data, transaction histories, KYC documents, and financial records are being pasted into AI tools daily. For regulated financial institutions, this creates immediate compliance exposure under data protection and financial services regulations across multiple jurisdictions.
A single prompt containing customer financial data can trigger breach notification obligations across APRA, MAS, and PRA regimes.
Financial services AI governance spans multiple jurisdictions and regulatory bodies. Here are the frameworks your institution needs to address.
APRA-regulated entities -- banks, insurers, superannuation funds -- must maintain information security capabilities proportionate to threats. AI tools introduce new attack surfaces and data exposure pathways that CPS 234 compliance programs must address, including third-party AI service risk assessments.
The Monetary Authority of Singapore's Technology Risk Management Guidelines require financial institutions to establish governance frameworks for technology risks, including AI and machine learning systems. MAS expects firms to manage model risk, ensure data integrity, and maintain oversight of AI-driven processes in banking and capital markets.
The UK Prudential Regulation Authority's supervisory statement requires firms to maintain comprehensive model inventories including AI and ML models, implement model validation and performance monitoring, and establish clear accountability. AI models in trading, pricing, credit risk, and fraud detection all fall within scope.
The Basel Committee on Banking Supervision has published guidance emphasising governance, risk management, and supervisory expectations for AI in banking. Requirements include board-level AI oversight, model validation frameworks, data governance for AI training data, and transparency in AI-driven decisions affecting customers.
Purpose-built AI governance that addresses the specific challenges of multi-jurisdictional financial services compliance.
Get a complete, real-time inventory of every AI tool used across your financial institution -- sanctioned and unsanctioned. Aona detects Shadow AI across trading desks, operations teams, risk functions, and support staff within minutes of deployment.
Full AI visibility across all business linesApply AI-native DLP controls that prevent customer PII, account data, trading strategies, and proprietary models from leaking into AI tools. Policies enforce automatically across all endpoints -- no manual intervention required.
Protect customer data and proprietary trading informationProduce board-ready compliance reports mapped to APRA CPS 234, MAS TRM, PRA SS1/23, Basel Committee guidance, and FCA requirements. Audit trails capture every AI interaction for regulatory examination across all applicable jurisdictions.
One-click compliance reports for APRA, MAS, PRA, and BaselDon't block AI -- govern it. Give traders, analysts, and operations teams access to approved AI tools while protecting sensitive financial data and maintaining compliance. Enable productivity gains without regulatory risk.
Faster AI adoption with reduced compliance exposureGet full AI visibility, enforce multi-jurisdictional compliance, and enable AI adoption safely across every business line.