The Challenge

A financial-services institution needed an independent assessment of how effectively and accurately manual overlays were being used within its credit rating process. The central question was whether Risk Committee judgement enhanced the model’s predictive and discriminatory performance, weakened it, or produced a neutral outcome when tested against actual performance

Key Areas of Assessment

• Overlay Efficiency
• Granular Diagnostics
• Model Output vs Actual Performance
• Overlaid Output vs Actual Performance

Our Solution

• Reviewed overlay concentration across rating grades and outcome performance.
• Assessed final ratings including overlays against actual performance.
• Compared original model ratings and final overlaid ratings for overridden cases.
• Identified records where final ratings differed from model ratings.
• Assessed model ratings against actual performance before manual judgement.
• Prepared performance-testing views for model-generated and final ratings.
• Compared performance across framework, models and segments.
• Synthesized evidence on whether overlays enhanced or detracted from predictive power.Analyzed overlay frequency, direction, concentration and magnitude.

Core Analytical Lens

Model ratings vs actual performance | Final ratings including overlays vs actual performance | Overlay cases before vs after intervention | Framework-level and rating-grade-level assessment

The Impact

• Focused Management Review
• Objective View of Overlay Value
• Multi-Level Effectiveness Testing
• Visibility Into Judgement Patterns

Conclusion

ICRA Analytics’ approach converted manual rating overlays from an opaque judgement layer into a testable component of the model framework. By backtesting model-generated ratings and final assigned ratings against the same actual-performance indicator, and by separately examining overridden cases, the analysis was designed to determine whether overlays added measurable value and where their use warranted closer management attention

FAQ

1. How can banks validate whether manual rating overlays improve credit risk model performance?

Banks can assess the effectiveness of manual rating overlays by comparing model-generated ratings and final assigned ratings against actual performance outcomes. This helps determine whether expert judgement improves, weakens, or preserves predictive accuracy.

2. What is the best approach to measure the impact of rating overrides on credit risk model outcomes?

A robust approach involves backtesting both model-only ratings and final overlaid ratings against the same default indicator, allowing institutions to quantify the value added by manual intervention and identify ineffective overrides.

3. Why is rating overlay validation important for model risk management and regulatory compliance?

Rating overlay validation provides evidence-based insights into how expert judgement influences rating decisions, strengthening model governance, transparency, audit readiness, and model risk management frameworks.

4. How can financial institutions identify whether Risk Committee judgement is enhancing rating accuracy?

By isolating overridden cases and comparing performance before and after intervention, institutions can evaluate whether Risk Committee decisions improve risk differentiation, predictive performance, and rating consistency.

5. What are the key metrics used to assess the effectiveness of credit rating overlays?

Organisations typically evaluate overlay frequency, upgrade versus downgrade patterns, notch movement, rating-grade concentration, and comparative performance of the model-generated and the final assigned ratings with actual borrower performance to understand the effectiveness of manual judgement.