Yale School of Management Professor X. Frank Zhang, along with Chenchen Li of Nanjing University and Ningzhong Li of the University of Texas at Dallas, have introduced a unique approach to detecting revenue fraud at supplier firms. By analyzing customer information provided by supplier firms and disclosed in customer firms’ accounting data, the researchers aim to predict two types of revenue fraud: "cooking the books" and "channel stuffing."
Zhang explains the significance of their work, stating, "This paper is unique because it uses information from the supplier-customer relationship to identify accounting fraud." Their method focuses on discrepancies between supplier sales growth and customer purchase growth numbers, as well as year-over-year jumps in customers’ inventory and accounts payable.
The study conducted by Zhang and his coauthors spans almost half a century and includes nearly 30,000 supplier-year observations. They found that firms with large discrepancies between supplier sales growth and customer purchase growth are more likely to have engaged in revenue fraud. Zhang notes that the average likelihood of fraud in their study is only 0.4%, making these findings significant.
Zhang emphasizes the efficiency of their model compared to existing fraud detection methods, stating, "Our single variable is half as informative as a combination of all 11 variables used in the Dechow model." He also highlights the interest in their research from various parties, including Wall Street investors and representatives of the U.S. Securities and Exchange Commission.
The study not only sheds light on the prevalence of revenue fraud in supplier firms but also underscores the limited effectiveness of auditors in uncovering such fraudulent activities. Zhang points out that auditors often maintain good relationships with their clients and may overlook signs of fraud. Similarly, customers are more focused on product quality and timely delivery, rather than detecting fraud within their supplier firms.
Overall, the findings presented in the paper hold significance for regulators, investors, and accounting scholars. Zhang's research has already attracted attention from entities seeking to leverage the model to identify potential fraud and gain a competitive edge in trading, particularly in relation to companies that have committed fraud.
