Current Expected Credit Loss (CECL) regulations (FASB 825-15) that change the loan loss reserve estimation to lifetime Expected Loss (EL) pose challenges to optimizing loan loss reserves—a critical objective for financial institutions. A key component in calculating EL is Loss Given Default (LGD), the estimation of the probable recovery of a realizable asset if a default occurs. Our Absolute Expected Loss model provides powerful, consistent, and objective LGD estimates at a contract level.
Look Around Corners
The U.S. Financial Accounting Standards Board (FASB) standards focusing on Current Expected Credit Losses (CECL) shift the way financial institutions view and analyze risk of future losses. The standards emphasize a forward-thinking model forecasting loss over the life of a loan using historical and current information rather than occurred and observable evidence of credit loss. Our Absolute Expected Loss Model is an innovative approach for estimating Expected Loss (EL) to meet CECL standards.
How We Help You Meet New Guidelines
A structured decisioning framework
Improve accuracy and effectiveness of forecasting and credit decisioning.
Spot troubled accounts
Perform bottoms up (contract level) calculation of expected loss.
Tackle risk earlier
Guide loss mitigation to take action earlier in the default cycle and lower losses.
Complement your collections efforts
Leverage Absolute Expected Loss's accurate default analysis and tracking.
Know where your risk is
Understand loss risk by collateral type, geography, business unit, transaction size.
Quantitative precision for CECL
Equifax’s Absolute Expected Loss Model provides insight for optimal loss reserve along with valuable information about institutional portfolios. Because estimates are given at the obligation level, the model output can accurately rank order individual borrowers and aid in portfolio risk management. Download our white paper to learn more about how our Absolute Expected Loss Model can help you determine prudent and defensible reserves for losses.