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CECL Solver for Moody’s CreditCycle™ solution enables users to forecast lifetime losses through custom and/or industry off-the-shelf econometric models under the CECL standard. Our fully documented, flexible solution enables clients with any data availability to effectively analyze their portfolio under a wide range of reporting dates. Quarter-over-quarter allowance differences are broken down to help clients better understand the source of change.

Leverage models tailored to your unique exposures

  • Custom or industry models incorporating client data.
  • Uses Moody’s CreditCycle platform for visualization, estimation, forecasting and version control.
  • Audit track and user-friendly options to view, adjust, and export results.
  • 30-year forecast horizon, reverting to long-term trends, or explicit mean reversion override.
  • Economic and model forecasts updated monthly under all Moody’s Analytics scenarios and regulatory scenarios.
  • Unlimited, tracked updates and sensitivity analysis on the web.
  • Access to economists and consumer credit analysts for interpretation of results.
  • Integrated with Moody’s Power Tools for convenient Excel download and refresh.
  • Secure web-based environment with integrated national and regional economic data.

Address accounting standards while managing portfolio credit risk

  • Calculate expected lifetime credit losses on aggregate and by segment/cohort.
  • Configure loss given default (LGD), lifetime, mean reversions, single scenario vs. scenario weighting, and other options to compare results.
  • Perform impact analysis incorporating future lending strategies to estimate future CECL allowances.
  • Compare CECL methodology with incurred loss methods using backward-looking calculation.

Assess attributions and relative impacts on the allowances

  • Attribute quarter-over-quarter allowance change to various sources such as portfolio composition, risk profile, economic dynamics, model/methodology changes, and more after-the-fact changes.
  • Track quarter-over-quarter portfolio and assumption differences for ease of comparison.
  • Identify correlations between economic variables and credit risk.

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