Challenges in Validation: Taking the Study Findings Forward A Corporate Perspective Lyn McGowan RBC Financial Group Advanced IRB Forum New York, June 19, 2003

2 The Challenge of Validation for Corporate and Mid-Market Portfolios Internal rating validation approaches, methods, issues vary, depending on the types of rating models used Rating system design and validation go hand in hand Type of Rating Model CORPORATE MID-MARKET Statistical Models 7 4 External Vendor Models 7 2 Expert Judgement Models 1511 Hybrid Models 10 7

3 The Data Challenge Insufficient data to rely on purely statistical means of validation  must rely on other means The Basel Research Task Force recognizes that quantitative statistical techniques should be performed, however should not drive the pass/fail decision for IRB validation Supervisor will need to understand and be satisfied with: 1.The logic of the risk assessment process 2.The rating system’s design and operation 3.How the rating system has been calibrated 4.The internal validation process 5.The “feedback loop”

4 Logic of the Risk Assessment Process Conceptual clarity  Well-defined drivers/factors  Dynamic properties, significance of factors Transparency  Explicitly demonstrates reasoning  Constraints (such as stipulated factor weightings)  Assessment horizon Replicability  “Gut feel” won’t do  Criteria or thresholds for factors Well-documented  Process/procedures manual

5 Rating System Design and Operation Conceptual clarity  Understandable output Transparency  Not a Black Box Replicability  Well-defined framework and/or methodology Consistency  Application across industry, geography Documentation  Rationale for design  Conceptual meaning, definition of grades  Frequency of review  Override authority, reporting

6 Calibration of the Rating System Conceptual clarity  Techniques have been combined rationally Transparency  Availability of data across quality spectrum  Method of mitigating scarcity of data  Basis for numerator and denominator Consistency  Potential sources of bias  Relevance of external data Documentation  Specific techniques used

7 Internal Validation Process Conceptual clarity  Discriminative power vs accuracy of calibration  Factor relevance vs factor weights vs model strength  Rationale for Triangulation Transparency  Scope / frequency of work  Mapping processes  Objective metrics Consistency  Actual vs predicted  Own to external loss experience  Role of loan review unit Documentation  Clear, comprehensive, precise

8 The Critical Feedback Loop Calibration Validation Continuous Improvement Cycle