Objective

• To help a large US investment bank speed model validation and monitoring time, reduce model risk and eliminate costs by automating model testing of pricing and market risk models.
  

CRISIL's Solution

• Built a suite of tests for different models and respective use cases on the client proprietary platform to reduce the overall model validation timelines
  
• Set up a nine-member team (a healthy mix of quants and programmers), led by an onsite project manager
  
• Leverage team's strong experience in pricing models with programming in C++, Python and R
  
• Sample tests implemented:
  
  • MC convergence
  • Hedge backtesting
  • Assumptions
  • Stress scenarios
  • Model stability
  • VaR grid granularity
    
• Other features:
  
  • Flexible test interface that could be used for CCAR and other mandates
  • Allowed definition of further tests
  • Interactive results on a dashboard
    
    

Client Impact

• Helped automate tests across models to reduce model validation time by ~40% for low-risk models and ~25% for high-risk models
  
• Increased efficiencies in the model monitoring process, enabling faster results for high-risk models on a monthly basis, and for lower risk models on a semi-annual basis
  
• Achieved reductions in model risk; exceptions from the scheduled batch immediately flag any potential model risk issues
  
• Helped the bank reduce timelines and the number of contractor personnel required for model validation, minimizing costs
  
• CRISIL solution covered quantitative tests for MC convergence, hedge backtesting, stability testing, assumptions and limitation testing, stress scenarios, VaR grid granularity, etc.

Questions

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