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CECL validations and stress-testing - your questions answered

During our latest CECL Tuesday talk-through we answered submitted questions on CECL validations and stress-testing. This informative article reviews the questions we answered in our session.

Key takeaway:

  • Best practice is a start to finish validation, not just model testing
  • Start with model governance, polices and procedures surrounding the model and process
  • Review and test the data inputs (loan level data) as well as the assumptions being applied
  • Full replication provides value in that the entire portfolio is working accurately to produce final CECL estimate

Key takeaways:

  • Data errors from extract and transfer process 
  • Data assumption variances  
  • User errors 

Key methodology features: 

  • How is the discount rate being applied 
  • Are my loss factors being vectored based on the economic environment 
  • Are my prepayments shortening the life of the loan or re-amortizing the balance each month? 

Key takeaways:

  • A CECL vendor’s own validation is typically a validation of their mathematical formulas and regression models within the model itself
  • An independent validation focus on YOUR data within that model
  • Value is here is does the software combined with your data calculate a reasonable and supportable CECL estimate

Key takeaways:

  • Internally built model validations will focus on the data inputs and assumptions versus “black box” methodology
  • Key here is does my assumptions applied within the calculation make sense for our institution
  • Will also focus on the incorporate of a forecast since it is not as explicit in the basic methods

Key takeaways:

  • Key assumptions for any CECL model whether complex or not are sensitive to changes in the economic landscape
  • It is important to know the volatility of your CECL estimate so there are no surprises
  • Start with your previous volatile time periods (great recession, COVID) to use for scenarios going forward
Ivan Cilik
Matt J. Nitka
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Collaboration and education: necessities to avoid risks related to NIL collectives