AI Policy Enforcement for Financial Services and Lending

Industry context

Lenders and financial institutions use Corules to ensure AI credit decisions comply with ECOA, FCRA, and fair lending regulations. Every decision carries specific adverse action reasons, is replayable for regulatory examination, and is screened for disparate impact.

Corules provides the deterministic policy enforcement layer that Financial Services organizations need to deploy AI agents in production — with audit trails that satisfy regulators and governance teams.

Regulatory requirements

ECOA requires specific adverse action notices on credit denials. FCRA governs use of credit report data. OCC guidance on AI in credit decisions requires explainability and auditability. CFPB has issued guidance requiring lenders to explain AI-generated credit denials with specific reasons.

Corules's immutable audit ledger records every decision with: policy set version, actor identity (from signed claims — never from LLM output), normalized input hash, outcome, and reason. This creates a complete compliance trail for any regulatory examination.

Key decision types

These are the structured decisions most commonly enforced with Corules in Financial Services:

• Credit application approval and adverse action
• Loan modification and hardship program eligibility
• Credit limit increase and decrease decisions
• Fraud alert escalation and account suspension
• Underwriting exception approval

Applicable use cases

These Corules use cases are commonly deployed in Financial Services organizations:

Frequently Asked Questions