Case Study

Accelerated CECL Journey with Robust Loss Forecast Model Methodology

Business Objective

Our client is a leading mid-sized regional bank in the US with a diversified consumer lending portfolio.

Our objective was to build a robust loan-level loss forecasting model for their credit card portfolio, along with detailed documentation of the process that would successfully meet the guidance and expectations on Current Expected Credit Loss (CECL).


  • Constantly evolving guidelines – FASB and the banking regulatory bodies such as FRB, OCC or FDIC has not specified loss calculation methodology
  • Substantial data preparation – the need to summarize defaults, credit and fraud losses, and to create monthly trend information
  • In-depth analysis of economic risk factors at a granular level – the macroeconomic forecasts could not simply be based on deviations in the past.
  • Extensive documentation needed to keep the model audit-ready

Solution Methodology 

  • Leveraged both bottom-up (loan-level) and top-down (segment-level) approaches
  • Created segmentation based on delinquency, payment activity, and tenure at observation points separately for each of the dependent events (credit charge-off, bankruptcy and prepay)
  • Developed a comprehensive loss forecasting framework with justifiable assumptions for calculating PD (Probability of Default), EAD (Exposure at Default) and LGD (Loss Given Default)
  • Developed an ensemble of models alongside business context to forecast potential losses over the lifetime of the loans
  • Back-tested the models along with testing for different stress scenarios
  • Worked with the bank’s finance group to simulate ACL calculations and comparisons on incurred loss methodology and CECL methodology
  • Established detailed and comprehensive documentation that described the process end-to-end and also included quantified justifications for all the decisions and assumptions

Business Impact


  • Ensured the client had a robust loss forecasting model that met regulatory guidance and expectations on CECL
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