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.
At Moody’s Analytics, we are leveraging our credit risk expertise and long history of data and modeling to address the ever-growing demand for more insightful analytics. We are currently evaluating the predictive outcome of these alternative datasets as they are applied to various stages of the credit process. Given the promising results so far, we believe this is the future of more powerful decisioning capabilities within the financial industry.
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
- Text analytics for credit risk
COVID-19 has impacted several industries. We examine qualitative overlays to CRE loans that can be made no matter your CRE model.
Moody’s Analytics analyzed a range of plausible outcomes of quantitative expected losses under CECL, incorporating COVID-19 impacts across commercial and industrial (C&I), commercial real estate (CRE), and retail loans.
CECL was scheduled to go into effect at the beginning of 2020 until COVID-19 disrupted businesses. Moody's Analytics analyzed a range of plausible outcomes of quantitative expected losses under CECL, incorporating COVID-19 impacts across commercial and industrial (C&I), commercial real estate (CRE), and retail loans.
This study takes a scenario analysis approach and dissects the credit risk impact on financial institutions' commercial real estate (CRE) loan portfolios under various COVID-19 scenarios.
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.
Learn to differentiate C&I, CRE, retail, and securities. Choose approaches at the right level of flexibility and sophistication. Apply model-free solutions based on historical internal or industry data.
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.