In the third webinar in our CECL quantification webinars series, our experts discussed which commercial and industrial (C&I) models and methodologies can be leveraged to fulfill CECL requirements, and key considerations in transitioning these models.
Implementation of the new financial instruments impairment standard (CECL), may take between twelve months to two years and over 62% of banks surveyed by Moody’s Analytics expect CECL compliance to increase their overall provisions.
Successful implementation requires understanding the impact of the accounting standard on provisions and identification of appropriate methodologies to incorporate the forward-looking information and life-of-loan horizon required for CECL.
Moody’s Analytics has designed a series of CECL Methodology webinars to help firms of all sizes with the tactical and strategic considerations when selecting the best modeling approach.
In this third webinar of our series, we cover:
How to leverage these existing capabilities in the transition to CECL
Key methodologies (approaches, segmentation, data requirements) and challenges for C&I Considerations for Lifetime Expected Credit Losses vs. Incurred Loss Model
The overall quantitative impact of CECL and how to be prepared
Originating, scoring, and maintaining proactive knowledge of your lending book can be an overwhelming task.
This document presents an approach that converts Through-the-Cycle (TTC) Probability of Default (PD) measures to Point-in-Time (PIT) measures and produces a lifetime term structure.
Is a financial statement decision useful? Is it informative enough to make a loan, acquire a company, increase a limit or move a borrower to work out? The quality of financial statements is a concern for all firms, especially as the demand for faster and more accurate due diligence grows.
Using a unique data set pooled from multiple U.S. ﬁnancial institutions, we empirically study the credit line usage of middle-market corporate borrowers.
RiskCalc™ EDF™ (Expected Default Frequency) values and agency ratings are widely used credit risk measures. RiskCalc EDF values typically measure default risk for private companies, while agency ratings are only available for rated companies. A RiskCalc EDF value measures a company's standalone credit risk based on financial statement information, while an agency rating considers qualitative factors such as Business Profile, Financial Policy, external support, and country-related risks. Moody's Analytics new Sovereign & Size-Adjusted EDF-Implied Rating Template combines RiskCalc EDF values with additional factors to provide a rating comparable to agency ratings for private companies. The new template applies to RiskCalc EDF values across numerous geographies and regulatory environments. With the new template, users can generate a rating more comparable to an agency rating than RiskCalc EDF values or EDF-implied ratings. Analyzing data from 3,900+ companies in 60+ countries, we find that sovereign rating and total asset size, in addition to EDF value, have a statistically significant impact on an agency rating — our quantitative template incorporating these three variables reliably estimates agency ratings in a robust fashion.
This report outlines a practical approach for using RiskCalc EDF credit measures to effectively monitor large portfolios of private firms and to proactively identify at-risk names. The RiskCalc Early Warning Toolkit Excel add-in is an easy to use, yet comprehensive tool that allows users to focus costly and scarce resources on a highly targeted selection of the most at-risk names in their portfolios. This research for private firms compliments previous research on Early Warning Toolkit for public firms. The Early Warning Toolkit identifies at-risk names within a private firm portfolio well before default, using a number of different EDF-related risk metrics.
With the new CECL and IFRS 9 requirements, this document formally investigates and summarizes the term structure properties consistently seen across public, private, and rated firms.
In this webinar, a panel of research and data scientist experts across Moody's Analytics discuss social data in probability of default modeling, closed and open data for location scoring, and text analytics for credit risk.
With an immense amount of available data generated worldwide within the last two years, the next evolution of banking analytics will include information from a variety of open and closed sources.
The primary objective of FASB’s CECL standard is to provide investors with more meaningful and timely information regarding credit risk, but it also presents a unique opportunity for financial institutions to advance credit risk practices, break down silos and strengthen business decisions.