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Model validation
2 months ago
1. Backtest & re-calibrate each model designed and developed by the business, hence a thorough
understanding of model development under Basel & IFRS norms is critical.
2. Choose adequate model outcome analysis techniques such as:
a. Model estimates vs realized values (e.g. back-testing for some models);
b. Stability of model outcomes;
c. Benchmarking: model output vs output generated by comparable models or applications
d. Sensitivity analysis to test robustness
3. Analyze the model output and the related components (if applicable);
4. Model assumptions and limitations validity;
5. Results of benchmarking and sensitivity analysis;
6. Accuracy of model’s characteristics;(ROC/AUC, KS statistics, accuracy ratio, Gini coefficient etc.)
7. Monitor over time in order to follow up trends and detect deviations;
8. Establish thresholds and action plan for major deviations;
9. Report this analysis to the different model stakeholders.
10. Implement a governance to monitor the corrective actions
Furthermore, as part of the model ongoing monitoring phase, the model monitor should abide by the group standards on ongoing monitoring that establish guidelines on performance assessment processes including type, scope and range of tests and appropriateness of responses to any problems that may appear.
Technical Skills:
Regulatory risk model (IRB, IFRS9) model validation, monitoring, development (good to have) using SAS,
R. Initiation to machine learning model validation.
Functional Skills:
• Knowledge of Global regulatory Topics BASEL II/III & IFRS 9
• Understanding of risk management and risk quantification processes
• Understanding of forms of risk, viz. credit, market, operational, model etc.
Experience : 2-8
Location: Bangalore
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