Models are subject to a regular validation cycle, with emphasis given to a thorough model backtesting and benchmarking. Validation testing also requires review of conceptual soundness and is often informed by threshold breaches from Model monitoring. Benchmarking to challenger Models and external data provides additional crucial insights when validating Model adequacy. Auriscon specializes in Validation of Credit Risk Models with challenger Models and indpendent replication.
Credit Models used for IRB Basel and IFRS 9 are often observed to deteriorate when economic condtions or business strategies undergo any significant change. A case in point is a deteriorating Model performance due to failure of addressing emerging risks in time. Consequently, both Basel IV and IFRS 9 raised the standards for Model validations. Credit Models under stress have to demonstrate robustness and to ensure that accurate calibrations are in place. After all, calibration accuracy is a pre-condition for efficient capital and IFRS9 calculations. Click the links below and read about details of Credit Validations and how Auriscon supports.
→ The Elements of Credit Risk Validation (under construction selected examples)
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Our Aproach
is to support or to independently perform validation of Credit Risk Models. We assist in defining and performing statistical testing and analysis covering validation of Basel and IFRS9 risk parameter PD, LGD, EAD, Risk and Economic Capital Models, and effectiveness of monitoring metrics.
Planning & Objectives
We support validations covering, but not limited to, Data Quality and Integrity, Statistical Testing, Conceptual Soundness, and Benchmarking. We support in devising validation frameworks and in automating validation testing and report generation based on the R programming language.
Specialisation
As a specialist provider with expertise in Credit and Model Risk, we can suport based on a tailored approach suitable for Credit Wholesale and Retail portfolios.
Risk-Based Approach to Validation Testing
Identifying latent and emerging Risks is a key aspect every validation should aim to capture. With our support, additional view points on prevailing credit and economic positions are added. With a thorough statistical testing applied, suitably underpinned by benchmarking and backtesting, information is gather critical information about Model performance and Model risk transparently.
Contact us to request further details on our support.
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Validation Levels and Elements
Elements of PD Validation (example)
- Statistical Calibration Testing
- Backtesting and Benchmarking
- Number of Overrides
- Discriminatory Power
- Portfolio Stability
- Challenger Models
Validation Levels
Validation Testing proceeds from Data (Level 1) to Model (Level 2) to Calibration Level (Level 3). Different validation tests are performed and assigned as illustrated in the table shown below:
The Elements of Validation
The following few sections display elements of validations illustrated for a PD Model.
Validation Testing at Calibration Level
Traffic-Light-Dashboard Level 2
Level-2 for PD (example)
Traffic-Light indications for calibration accuracy
Validation Testing at Model Level
Traffic-Light-Dashboard Level 1
Conceptual soundness and Model assumptions.
Limitations pertaining to the Model.
Discriminatory power of a rating (scoring) Model.
Variable selection process and plausibility thereof.
Overrides frequency and strength for indicator of deteriorating performance.
Validation Testing at Data Level
Level 0
- Data Quality, e.g. metrics such as Currentness of Data should be integrated into validation testing at Level-0..
- Representativeness of data and data stability of the portfolio's obligor population in terms of characteristics can be tested using metrics suchs as Population Stabiliy Index, t-test, histogram and percentiles.
- Integrity of data, i.e. is the credibiliy of data sources confirmed?