AIGR · Institutional AI governance

AIGR Finance

EvidenceReviewDecision

AIGR Finance

Governance ratings for AI inside financial risk and control environments.

AIGR Finance is designed for banks, insurers and financial institutions that need to connect AI use to model-risk discipline, explainability, fairness, data controls, supplier oversight and accountable change management.

ContextBanks + insurers
Core riskModel + conduct
EvidenceValidation + controls
MonitoringDrift + change
01

Model risk

Inventory, validation, limitations, performance thresholds and approval.

02

Explainability

Decision rationale appropriate to the use case, stakeholder and regulatory context.

03

Fairness

Testing, outcome monitoring, prohibited-factor controls and remediation.

04

Data governance

Lineage, quality, access, retention and permitted-use evidence.

05

Suppliers

Vendor due diligence, change notifications, contractual rights and monitoring.

06

Operational resilience

Incident response, fallback, business continuity and change governance.

Decision principle

Model performance and governance maturity are different questions.

A model can be accurate and still lack the ownership, evidence, explainability, controls or monitoring required for responsible enterprise use.

Enterprise assessment

Establish governance readiness for a defined AI system.

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