Client Needs & Objectives
Context:
- Backtesting of banking models is time-consuming and extremely resource-intensive
- Targeting the causes of poor performance allows to automate actions to take
Objectives:
Automation of model backtesting, calibration of alerts thresholds, identification of poor performances and actions to take
Scope:
- Credit Risk
- Asset management
Techniques:
- Scoring
- Classification
- Logistic regression
Our approach
- A first layer of tests is implemented (Gini, Stability, Conservatism etc.).
- According to the results, several layers of tests are built in order to identify the causes of model’s failures
- Actions to take are automatically established
- Thresholds are dynamically calibrated
