Detecting accounting fraud in companies reporting under US GAAP through data mining |
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| Institution: | 1. Department of Economics and Finance, Faculty of Management, Comenius University, Address: Odbojárov 10, P.O. Box 95, 820 05 Bratislava, Slovakia;2. Department of Economics and Finance, Faculty of Management, Comenius University, Address: Odbojárov 10, P.O. Box 95, 820 05 Bratislava, Slovakia;1. TU Dortmund University, Faculty of Business and Economics, Germany;2. The University of Tampa, Sykes College of Business, Department of Accounting, United States;1. College of Business Administration, University of Seoul, Seoulsiripdaero 163, Dongdaemun-gu, Seoul 02504, South Korea;2. Shidler College of Business, University of Hawaii at Manoa, 2404 Maile Way, Honolulu, HI 96822, United States;3. School of Management, Clark University, 950 Main Street, Worcester, MA 01610, United States;1. School of Accounting, Nanjing Audit University, China;2. School of Accounting, Zhongnan University of Finance and law, China;3. School of Accounting, Zhongnan University of Finance and law School of Management & West Yunnan University of Applied Sciences, Dali, China;1. Marist College, United States;2. Ramapo College, United States;3. Rutgers University, United States |
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| Abstract: | The accounting fraud detection models developed on financial data prepared under US Generally Accepted Accounting Principles (GAAP) in the current literature achieve significantly weaker performance than models based on financial data prepared under different accounting standards. This study contributes to the US GAAP accounting fraud data mining literature through the attainment of higher model performance than that reported in the prior literature. Financial data from the 10-K forms of 320 fraudulent financial statements (80 fraudulent companies) and 1,200 nonfraudulent financial statements (240 nonfraudulent companies) were collected from the US Security and Exchange Commission. The eight most commonly used data mining techniques were applied to develop prediction models. The results were cross-validated on a testing dataset and then compared according to parameters of accuracy, F-measure, and type I and II errors with existing studies from the US, China, Greece, and Taiwan. As a result, the developed predictive models for accounting fraud achieved performance comparable to those achieved by models built on data from other accounting standards. Moreover, the developed models also significantly outperformed (accuracy 10.5%, F-measure 16.1%, type I error 12.2% and type II error 15.2%) existing studies based on US GAAP financial data. Furthermore, this study provides an extensive literature review encompassing recent accounting fraud theory. It enhances the existing US fraud data mining literature with a performance comparison of studies based on other accounting standards. |
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| Keywords: | Accounting fraud Data mining US GAAP Machine learning Fraud prediction Financial statement Beneish model |
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