Model Validation

  • In 2011 The Office of the Comptroller of the Currency (OCC) adopted the “Supervisory Guidance on Model Risk Management”. This guidance, articulates the elements of a sound program for effective management of risks that arise when using quantitative models in bank decision making. It also provides guidance to examining personnel and national banks on prudent model risk management policies, procedures, practices, and standards.
  • This new supervisory guidance replaces Bulletin OCC 2000-16, “Model Validation,” dated May 30, 2000. The new guidance incorporates the accumulated lessons of supervisory experience and industry practice over the past decade. Model validation remains at the core of the new guidance, but the broader scope of model risk management encompasses model development, implementation, and use, as well as governance and controls related to models.
  • Many financial institutions, including banks, insurance companies and investment houses use complex models in their lines of business. The abovementioned directives have defined the Best Practices for validating these models and defining model risk policies, procedures and measures.
  • HMS (Halperin Consulting Group) has over 20 years’ experience in the banking and financial sector, especially dealing with market, liquidity and credit models.
  • HMS has developed a flexible model validation methodology to ensure maximum benefit for its clients during their model validation processes. This includes specific and quantitative recommendations for improving the models, when relevant.
  • HMS has a dedicated model validation team, comprised of well experienced analysts and banking experts including local and foreign consultants.

The main areas of our expertise include:

  • Liquidity risk models
  • Market risk models (including VaR, etc.)
  • Credit risk models (including mortgages, counterparty credit risk, etc.)
  • Scoring models
  • Concentration risk models

Our basic methodology is based on three components:

  • Methodological validation – validating the core hypothesis of the model, prerequisites, benchmarking, etc.
  • Data validation – confirming the data accuracy, completeness, calculations, etc.
  • Statistical validation – using statistical and mathematical methods, such as backtesting, regression analysis, sensitivity and stress testing, etc. to validate the model’s results.
  • Use-Test validation – validating the model outputs, the processes based on the outputs, managerial decisions, etc.

In our experience, having a clear and holistic approach to performing a model validation project can provide the bank with tremendous value within several areas:

  • Enhancing risk management procedures
  • Incorporating additional accuracy to the models
  • Adding model transparency to all levels of the management
  • Maintaining regulatory requirements
  • Achieving continuous improvement
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