Model validation requires theoretical, qualitative and a solid mathematical finance background with a good sense for the business domain. It focuses on whether a model or methodology is adequate for the purpose it is used for or not

In a broader definition, the role involves the validation not only of the models or methodologies at hand, but also of their implementations. This involves reviewing the code, running simulation stress tests, and developing alternative implementations as a cross-check. The validation team must also deal with the varied technologies (Excel/VBA, Matlab, C++) used for validation. Current validation process requires not only a very strong Quant culture, but an equally strong IT discipline.

Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses. Effective validation helps ensure that models are sound. It also identifies potential limitations and assumptions, and assesses their possible impact.

Role Definition:

- Take the leadership role in building, developing and marketing the Organisation model validation practice across multiple risk domains, e.g., Credit risk, Market Risk, ALLL, Stress Testing models, and across several areas within Risk & Regulations.

- Understand model validation requirements in details for each risk domain (credit risk, market risk, etc.)

- Liaise with front-end teams to understand demand for this expertise. Work with cross-functional team to ensure that the proper business context and data requirements are met

- Design validation reports for different types of models in consultation with operations teams working in the area and with a view to both growing and marketing the practice.

- Help with the development and/or diffusion of related modeling practice, to enable comparison across approaches, etc.

- Drive knowledge initiatives within the organization. Author white papers, drive applied research

A still wider interpretation of the role is to validate not only the model implementation, but the whole process where the model comes into play:

- Purpose & the model use

- The market data and other data sources on which the strategy depends

- Horizon to use the model which have been calibrated

- Frequency of model re-calibration

- Model performance for specific market or business conditions

- Empirical tests of model robustness

Required Competencies:

- Conceptual understanding of the data and methodology used for top regulatory models (e.g., PD/EAD/LGD models for credit risk, Value-at-Risk models for market risk, ALM & behavioral models for interest rate risk)

- Knowledge of domain, products and related software to help with the development and diffusion of the required knowledge and expertise

- Experience with risk policy and analytics in areas of loss mitigation, model development/validation, loss forecasting, Basel compliance, creating and executing stress testing scenarios, ALLL reporting, CCAR submissions

- 6 - 12 years of hands-on experience in risk modeling/segmentation, customer/prospect databases, customer behavior data and segmentation

- Exposure to Moody's Risk Analyst (MRA) / RiskCalc, LossCalc, KMV models

- Expertise or a good knowledge of the credit risk rating for various Asset Classes: Retail, Wholesale, Equity, Bank, Sovereign

- Understanding of BCBS & SR 11/7 requirement in risk quantification & risk model validation & governance.

- Proficiency working with large databases or data warehouses using SQL or OLAP query tools.

- Good communication, stakeholder, team and project management skills to enable working with multiple teams if required

- Ability to work independently on projects and present effectively to senior stakeholders

Education:

- MBA (Finance, Financial Engineering); MS in Statistics, Economics, Finance and other quantitative discipline. Certifications such as FRM, CFA preferred

Desired Competencies;

- Background in validation of risk model & complete understanding of principles of risk model validation as defined the regulatory agencies. For ex SR 11/7

Experience in:

a) Reviewing conceptual soundness of models: Conceptual understanding of requirements, business background, approach used, knowledge of alternative methods, identifying model deficiencies

b) Generation of model validation metrics & other results: Data requirements for generating metrics and exposure to relevant software to generate results

c) Interpreting and making recommendation for alternative methodologies plus generating right reports to interpret results of validation process

- Expertise or exposure to reviewing validation coding, running simulation stress tests, and developing alternative implementations as a cross-check.

- Exposure to varied validation sofware's used in validation. (Excel/VBA, Matlab, C++, R, SAS, etc) used for validation

- Detailed knowledge of, or 5+ years of experience with, relevant statistical tests and methodologies, related software, domain and product knowledge

- Detailed knowledge of relevant regulatory rules and guidelines from one of the top markets (US, EMEA, APAC)

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