FinTech & Banking

Operate AI compliance for lending, fraud, payments, and credit models with continuous controls and audit-ready evidence.

Representative outcomes

  • Maintain a living inventory of AI models and vendors used in credit and fraud workflows.
  • Continuously score compliance posture and flag drift, control gaps, and high-risk systems.
  • Produce audit-ready evidence for approvals, testing, and governance decisions.

Representative examples illustrating how financial institutions operationalize AI compliance using TrustRail.

Governing Shadow AI in Credit & Fraud Operations

FinTech & Banking • Shadow AI Discovery

Situation

A digital lending platform discovered that multiple teams were using unapproved third‑party AI tools for credit risk analysis and fraud detection, creating unmanaged regulatory and model risk exposure.

Goal (SMART)

  • Specific: Identify and govern all AI tools used across lending and fraud workflows.
  • Measurable: Achieve ≥95% AI inventory coverage with assigned owners.
  • Achievable: Use automated discovery plus compliance ownership workflows.
  • Relevant: Reduce regulatory and audit risk from undocumented AI usage.
  • Time‑bound: Operational within 4–6 weeks.

Approach

TrustRail’s Shadow AI Discovery scanned systems, vendors, and internal workflows to identify AI usage. Each AI system was assigned an owner, risk tier, and mapped to required governance controls.

Results

• 97% AI inventory coverage achieved
• All AI systems assigned accountable owners
• Audit inquiries resolved using centralized evidence

Timeline

Week 1–2: Discovery & inventory • Week 3: Ownership & risk tiering • Week 4–6: Evidence and reporting

Continuous Bias & Compliance Monitoring for Credit Models

FinTech & Banking • AI Compliance Scorecard & Bias Testing

Situation

A bank faced increasing scrutiny on fairness and explainability of AI‑driven credit decisioning models, with compliance reviews occurring only at annual audit cycles.

Goal (SMART)

  • Specific: Monitor compliance and bias posture of credit models continuously.
  • Measurable: Maintain ≥90% compliance score across all active models.
  • Achievable: Use automated scorecards and scheduled bias testing.
  • Relevant: Demonstrate defensible, fair lending practices.
  • Time‑bound: Live dashboards within 6 weeks.

Approach

TrustRail’s AI Compliance Scorecard continuously evaluated controls, documentation, and testing coverage. Automated bias and fairness tests were scheduled at model changes and defined review intervals.

Results

• Real‑time visibility into compliance posture
• Bias risks identified before audit reviews
• Reduced audit preparation time by ~40%

Timeline

Week 1–2: Control mapping • Week 3–4: Scorecard configuration • Week 5–6: Bias testing & dashboards