Using Economic Links between Firms to Detect Accounting Fraud

The Accounting Review, forthcoming

49 Pages Posted: 2 Mar 2021 Last revised: 9 May 2022

See all articles by Chenchen Li

Chenchen Li

University of Texas at Dallas

Ningzhong Li

University of Texas at Dallas

Frank Zhang

Yale School of Management

Date Written: May 6, 2022

Abstract

We explore whether accounting fraud can be detected using the information of firms economically linked to a focal firm. Specifically, we examine whether customer information disclosed by a supplier firm, combined with customers’ accounting information, helps to detect the supplier’s revenue fraud. We first confirm the economic link between the supplier and customers by showing a strong positive correlation between the supplier’s sales growth and the growth rate of total customer purchases. We then introduce two variables based on customer accounting information—the discrepancy between supplier sales growth and customer purchase growth and customer excess purchases—and show that they are predictive of supplier revenue fraud. We conduct a battery of cross-sectional tests to further examine the two fraud predictors and generally find results to vary cross-sectionally in a predictable way. Finally, the out-of-sample tests indicate adding the two variables to Dechow et al.’s (2011) model increases fraud prediction accuracy.

Keywords: Fraud, economic link, revenue, accounting information, customer

JEL Classification: G14, M40, M41, M42

Suggested Citation

Li, Chenchen and Li, Ningzhong and Zhang, Frank, Using Economic Links between Firms to Detect Accounting Fraud (May 6, 2022). The Accounting Review, forthcoming, Available at SSRN: https://ssrn.com/abstract=3780047 or http://dx.doi.org/10.2139/ssrn.3780047

Chenchen Li

University of Texas at Dallas ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

Ningzhong Li (Contact Author)

University of Texas at Dallas ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

Frank Zhang

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

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