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Modeling the Risk of Material Misstatement of Current Expected Credit Losses

Modeling the Risk of Material Misstatement of Current Expected Credit Losses

Presented at the 10th Annual BayesiaLab Conference on Thursday, October 27, 2022.

Abstract

Recent changes to U.S. accounting rules require estimation, in a continuous-variable sense, of current expected credit losses (CECL) on "financial instruments held at the reporting date, based on historical experience, current conditions, and reasonable and supportable forecasts." This new, continuous treatment of credit losses contrasts with the prior binary accounting rule under which losses were recorded only if "probable." At the same time, the standard for auditing such estimates, known as SAS 143, has also changed, placing emphasis on the effects of uncertainty, subjectivity and judgment, negligent or intentional management bias, complexity, and change. Both the accounting and auditing rules require probabilistic and causal reasoning, for which Bayesian networks are an effective tool. This presentation explores the application of Bayesian networks to the audit of current expected credit losses under the new standards, treating as a target variable the risk of material misstatement (RMM) of the continuous CECL estimate.

Presentation Video

Presentation Slides

About the Presenter

Kurt Schulzke, JD, CPA, CFE, teaches accounting information systems, auditing, forensic accounting, risk management, and leadership at the University of North Georgia. His teaching, research, and consulting integrate data science, accounting, and law. He has published on business valuation, economic damages, and Bayesian networks in accounting and auditing in the Columbia Journal of Transnational Law, Vanderbilt Journal of Transnational Law, Tennessee Journal of Business Law, Journal of Forensic Accounting Research, and The Value Examiner. As an attorney, Kurt focuses on business entities, estates, and trusts. MAcc (Brigham Young University), J.D. (Georgia State University), M.S. Applied Statistics (Kennesaw State University).


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