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Creating economic scenarios through the COVID-19 crisis continues to be a challenge. In the last week we had new economic data reporting on conditions three weeks ago heralding the creation of 4.8 million new jobs. At the same time, reports of skyrocketing coronavirus cases are causing businesses to re-close ...
St. Paul, MN (January 11, 2020) – Minnesota Credit Union Network (MnCUN) and Deep Future Analytics (DFA) announced their partnership today. MnCUN will make available DFA’s Prescient Manager™ software to its membership. Prescient Manager™ is an easy-to-use, web-based credit risk forecasting and stress testing ...
The proposed rule provides helpful comments on how the new Current Expected Credit Loss (CECL) standard should be managed, validated, and monitored. One seemingly simple statement, however, has significant implications.
In the sections “Analyzing and Validating the Overall Measurement of ACLs”, “Responsibilities ...
Credit Unions can expect at least a 22% increase and Community Banks a 59% decrease in reserves as a result of CECL?
Deep Future Analytics (DFA) and Prescient Models (PM) recently conducted a joint study across 103 CECL clients to determine how much their loss reserves could change if CECL were adopted ...
All publications below were authored by DFA's own, Dr. Joseph Breeden
CECL (Current Expected Credit Loss) is the new accounting standard for estimating loss reserves on loan portfolios. The CECL guidance provides a great amount of flexibility in which models are used and a range of other choices that may impact the calculations. This book provides details of a study on how to apply CECL to US mortgage data. It seeks to disclose as many modeling details, results, and validation tests as possible so as to provide a reference for comparison and best practices. Because CECL is so similar to IFRS 9 Stage 2, this can also serve as a benchmark for implementing the new international account standards. The book is organized into three parts. Part I: Study Summary provides an overview of CECL, the design of the mortgage study, and the key comparative results across the models tested. Part II: Model Details provides in-depth discussions of how the models were designed and estimated, the coefficients, and the validation. Part III: Background provides additional conceptual material. Chapters 11 and 12 may be particularly useful to those new to modeling, and Chapter 13 puts CECL modeling in the context of lending analytics overall.
Building on the solid foundation of the previous bestselling first impression, this extended updated impression walks through the various issues of retail lending and develops approaches to address the interaction between economic cycles and retail lending. The complexity of time is extensively explored: vintages, current time and maturity. Reinventing Retail Lending Analytics, Second Impression covers complex issues such as scenario based forecasting, stress testing, volatility analysis, economic capital and portfolio optimisation, credit scoring and last, but not least, model risk.
The book ends by providing examples of the application of nonlinear decomposition. These examples will provide you with rich data sets for exploring portfolio dynamics and improving portfolio management using nonlinear decomposition techniques.
The new loan loss accounting rules for CECL and IFRS 9 require thousands of organizations to learn about modeling. Likewise, accountants and others in finance are now required to learn about statistical modeling concepts. This book is intended to define terms in a manner consistent with decades of academic literature on statistical modeling and hopefully reduce some of the noise and confusion just around definition of terms. It may also serve as a useful guide to analysts new to the field tasked with IFRS 9 compliance, the international loss accounting rules, and credit risk modeling in general.
Each chapter of this book is a term that one might encounter when discussing creating lifetime loss forecasting models for CECL or IFRS 9. Not every term is a model, and some models listed are being mentioned only to explain why they are not likely to be used for loss forecasting. The CECL guidelines and subsequent FAQs have given examples of modeling techniques. Some people new to loss forecasting have assumed that those are all the available or applicable methods. This book is meant in
part to dispel that misconception.
The definitions and descriptions provided here are meant to provide an intuitive understanding across a range of modeling techniques. Mathematical derivations are kept to a minimum. The references listed will provide all the necessary details for an eager analyst.