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Gamut AI Gamut AI Docs

Financial services

Banks, capital-markets firms and payment providers operate AI under some of the heaviest scrutiny of any sector: prudential regulators, conduct regulators, model-risk functions, internal audit and demanding institutional clients all expect documented, defensible governance. Gamut gives financial institutions one operating layer to govern every AI system from credit models to agentic operations.

AI systems typically in scope

Section titled “AI systems typically in scope”
  • Credit and lending decisioning, affordability and collections models.
  • Fraud detection, transaction monitoring and AML screening.
  • Trading, execution and market-surveillance models.
  • Customer-facing GenAI: assistants, advisers, complaint handling, KYC document processing.
  • Internal copilots and emerging agentic workflows that take action across systems.

Governance drivers

Section titled “Governance drivers”
  • Model risk management. Independent validation, documented assumptions, ongoing monitoring, the discipline supervisors expect of any consequential model.
  • EU AI Act readiness. Credit scoring and some fraud uses fall into higher-risk categories with specific obligations.
  • Conduct and fairness. Demonstrable controls against bias and unfair outcomes in lending and pricing.
  • Operational resilience and audit. Board, internal audit and external assurance expect a traceable record.

Use cases

Section titled “Use cases”

Govern a credit-decisioning model end to end

Section titled “Govern a credit-decisioning model end to end”

The scenario: a lending model influences customer outcomes and sits squarely in regulatory focus.

How Gamut solves it: register it in AI System Records, capture a model card for its technical and fairness characteristics, run intake and risk tiering (it will tier high), and route it to GTSAF for depth plus EU AI Act for regulatory readiness. Evidence validation, monitoring and fairness testing through the Evidence Tracker and Testing Centre, and produce a workpaper pack for model risk and audit.

Demonstrate AML and fraud monitoring controls

Section titled “Demonstrate AML and fraud monitoring controls”

The scenario: a transaction-monitoring model must be shown to operate effectively, not just exist.

How Gamut solves it: control testing records design and operating effectiveness with sampling, exceptions and tester sign-off, findings track any gaps through the Remediation Roadmap, and the audit trail proves the history.

Put agentic operations under control

Section titled “Put agentic operations under control”

The scenario: an agent that moves money, opens tickets or acts in core systems.

How Gamut solves it: register it in Agentic CISO, set its ATF autonomy level, and let Gateway enforce approval gates on every financial or external action while Claw executes only through governed paths. No agent holds credentials; every action becomes runtime evidence.

Quick start

Section titled “Quick start”
  1. Register the AI system in AI System Records with a named owner and a model card.
  2. Run AI Use Case Intake and confirm the risk tier and ACRS band.
  3. Route to GTSAF and EU AI Act; score controls.
  4. Evidence validation, monitoring and fairness testing in the Evidence Tracker and Testing Centre.
  5. Track gaps on the Remediation Roadmap.
  6. Produce a board pack and a workpaper pack for the board, model risk and internal audit.

Frameworks that apply

Section titled “Frameworks that apply”

GTSAF, EU AI Act, NIST AI RMF, ISO/IEC 42001, and for agentic operations ATF, ACRS and MAESTRO.

Next

Section titled “Next”