Consultant - Model Risk Management - Bank

We are hiring for one of our reputed banking client :-

MRM- Consultant Expert(delivery manager)

- IC role

- Exp req - 8 years relevant 4 yrs is must have

- Location -Bangalore

- Model validation and calibration role

- Credit risk modelling is mandate (PD ,LGD, EAD Model)

- IFRS9 Mandate

- SAS or R any coding (Hands on experience required)

Profile Required:

- Ideal candidate should be well versed in credit risk model development, validation and maintenance of models (PD, LGD and EAD) for wholesale and retail credit portfolio of the bank as per regulatory guidelines.

- Exposure to banking book and understanding of trading book products and knowledge on BASEL/IFRS guidelines is highly desirable. Candidate should have excellent business communication skills.

Educational Requirements:

- Post-graduation degree in quantitative discipline (Statistics, Economics, Mathematics & engineering) from Tier I/II colleges. Additional certification in machine learning techniques or estimation of credit risk parameters will be preferred.

Role & Responsibility:

- The ongoing monitoring of the model is a task that must be done in all phases of the model lifecycle (development, implementation, use). In order to track and measure the efficiency and adequacy of models, the model monitor conducts continuous analysis and controls as an early warning both initially at implementation (for new models) and regularly as a part of the model's ongoing monitoring.

For the purpose of these tests, the model monitor is responsible to:

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