Dynamic Risk Scoring vs Model Stability
Govern ML risk model updates through a staged validation and approval framework before any production deployment.
CyberTRIZ analysis · Banking contradiction AML026 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Business Context
Dynamic customer risk scoring improves AML effectiveness by continuously adjusting customer risk according to behaviour, transactions, external events, and regulatory intelligence. Frequent model changes, however, may reduce consistency and complicate governance.
Banking TRIZ Resolution
Maintain stable core risk models while updating risk indicators, thresholds, and external intelligence through configurable parameters supported by model governance.
Recommended Banking TRIZ Principles
Principle 15 - Dynamics
Principle 23 - Feedback
Principle 35 - Parameter Changes
Principle 40 - Composite Materials
Expected Outcome
Better risk assessment
Stable model performance
Faster regulatory adaptation
Improved governance