AI Agent Governance for Financial Services
Control, audit, and enforce compliance for every AI agent in your banking, trading, and wealth management operations.
The Challenge: AI Agents in Banking
Financial institutions face unprecedented governance challenges with autonomous agents:
⚠ Regulatory Scrutiny
Regulators like the Fed, APRA, and MAS demand visibility into AI agent decisions. Without proper audit trails and governance frameworks, you risk enforcement action and fines.
🔐 Customer Data at Risk
AI agents processing financial data—account numbers, SSNs, transaction histories—face constant risk of leaking sensitive information. One breach can trigger compliance violations and customer liability.
📊 Model Risk Opacity
Trading and risk agents can drift or behave unexpectedly, exposing your firm to market risk, model risk, and operational losses. Detecting these issues quickly is critical.
🚫 AML Compliance Gaps
Anti-money laundering agents must detect suspicious patterns reliably. Without outcome quality assessment, you cannot verify that AML agents are catching real threats.
How Prefactor Solves Financial Services AI Governance
Prefactor's Agent Runtime Control Plane delivers the visibility and enforcement financial institutions need:
📋 Agent Lifecycle Governance
Manage your agent registry with manual enrollment and full lifecycle tracking. Document who owns each agent, what data they access, and their intended use—essential for regulatory reporting and compliance audits.
🛡 Runtime Policy Enforcement
Define and enforce behavioral policies on agents in real-time. Block non-compliant outputs, throttle operations, sandbox risky agents, or escalate to humans—all without code changes. Inline enforcement prevents policy violations before they happen.
🔒 PII Detection & Blocking
Detect and block account numbers, routing numbers, SSNs, and other financial PII in agent outputs before they leave your system. Inline blocking prevents data leakage in real-time, protecting customer privacy.
🔍 Immutable Audit Trails
Every agent interaction—input, reasoning, output, policy decisions—is recorded in an immutable, tamper-proof audit trail. Provide regulators with complete visibility into agent behavior and decision-making for full compliance.
⚖ Composite Risk Scoring (Coming Soon)
Aggregate agent behavior, model drift, cost anomalies, and policy violations into a single risk metric. Coming soon: identify trading and risk agents exhibiting dangerous patterns before they impact your portfolio.
✅ Outcome Quality Assessment Coming soon
Continuously evaluate agent outputs against your success metrics. For AML agents, ensure detection accuracy is maintained. For customer service agents, verify response quality meets standards and SLAs.
Compliance Frameworks Prefactor Supports
Prefactor helps you align with the regulatory frameworks that matter most to your institution:
APRA CPS 234 (Australia)
Manage AI/ML governance, model risk, and documentation requirements. Prefactor's audit trails and lifecycle governance ensure compliance with APRA's heightened expectations for AI oversight.
MAS TRM (Singapore)
Meet the Monetary Authority of Singapore's Technology Risk Management guidance. Prefactor's outcome quality assessment and runtime policy enforcement align with MAS expectations for AI testing and validation.
Fed SR 11-7 & AI Guidance
Align with Federal Reserve expectations for model validation, risk management, and governance. Prefactor provides the visibility and control the Fed demands for AI/ML systems in banking.
Basel III & Model Risk Management
Ensure trading and risk management agents comply with Basel III model risk capital requirements. Runtime policy enforcement, outcome quality assessment, and immutable audit trails support your model validation framework.
Frequently Asked Questions
How does Prefactor help with regulatory compliance for AI agents in banking?
Prefactor provides immutable audit trails, real-time PII detection, and runtime policy enforcement that align with APRA CPS 234, MAS TRM, Fed SR 11-7, and Basel III requirements. Our control plane ensures every agent decision is documented and validated against your compliance frameworks.
Can Prefactor detect sensitive financial information in agent outputs?
Yes. Prefactor's inline PII detection identifies account numbers, routing numbers, customer identifiers, and other sensitive financial data in real-time, allowing you to block or sanitize outputs before they reach end users or external systems.
What compliance frameworks does Prefactor support?
Prefactor aligns with APRA CPS 234, MAS TRM, Fed SR 11-7, and Basel III. Our immutable audit trails, lifecycle governance, and outcome quality assessment help you meet regulatory expectations across all major financial regulatory regimes.
How does Prefactor support anti-money laundering (AML) agent oversight?
Prefactor's lifecycle governance capabilities ensure AML agents operate within defined behavioral boundaries. Our immutable audit trail and outcome quality assessment let compliance teams validate that AML detection agents are functioning correctly and not missing suspicious transactions.
Continue your research
Ready to Govern Your Financial AI?
See how Prefactor brings visibility, control, and compliance to your AI agent infrastructure.
Book a Demo