Detecting accounting fraud in companies reporting under US GAAP through data mining
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DOI: 10.1016/j.accinf.2022.100559
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References listed on IDEAS
- Jianrong Yao & Yanqin Pan & Shuiqing Yang & Yuangao Chen & Yixiao Li, 2019. "Detecting Fraudulent Financial Statements for the Sustainable Development of the Socio-Economy in China: A Multi-Analytic Approach," Sustainability, MDPI, vol. 11(6), pages 1-17, March.
- Beneish, Messod D., 1997. "Detecting GAAP violation: implications for assessing earnings management among firms with extreme financial performance," Journal of Accounting and Public Policy, Elsevier, vol. 16(3), pages 271-309.
- Kurt M. Fanning & Kenneth O. Cogger, 1998. "Neural network detection of management fraud using published financial data," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 7(1), pages 21-41, March.
- Chrysovalantis Gaganis, 2009. "Classification techniques for the identification of falsified financial statements: a comparative analysis," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(3), pages 207-229, July.
- Chen, Yuh-Jen & Wu, Chun-Han & Chen, Yuh-Min & Li, Hsin-Ying & Chen, Huei-Kuen, 2017. "Enhancement of fraud detection for narratives in annual reports," International Journal of Accounting Information Systems, Elsevier, vol. 26(C), pages 32-45.
- Lucia Svabova & Katarina Kramarova & Jan Chutka & Lenka Strakova, 2020. "Detecting earnings manipulation and fraudulent financial reporting in Slovakia," Oeconomia Copernicana, Institute of Economic Research, vol. 11(3), pages 485-508, September.
- Chyan-long Jan, 2018. "An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan," Sustainability, MDPI, vol. 10(2), pages 1-14, February.
- Xiaobo Tang & Shixuan Li & Mingliang Tan & Wenxuan Shi, 2020. "Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 769-787, August.
- Ozili, Peterson K, 2015. "Forensic Accounting and Fraud: A Review of Literature and Policy Implications," MPRA Paper 77236, University Library of Munich, Germany.
- Feng Xu & Zinan Zhu, 2014. "A Bayesian approach for predicting material accounting misstatements," Asia-Pacific Journal of Accounting & Economics, Taylor & Francis Journals, vol. 21(4), pages 349-367, December.
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Cited by:
- Zhou, Ying & Xiao, Zhi & Gao, Ruize & Wang, Chang, 2024. "Using data-driven methods to detect financial statement fraud in the real scenario," International Journal of Accounting Information Systems, Elsevier, vol. 54(C).
- Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
- Ludivia Hernandez Aros & Luisa Ximena Bustamante Molano & Fernando Gutierrez-Portela & John Johver Moreno Hernandez & Mario Samuel Rodríguez Barrero, 2024. "Financial fraud detection through the application of machine learning techniques: a literature review," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-22, December.
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More about this item
Keywords
Accounting fraud; Data mining; US GAAP; Machine learning; Fraud prediction; Financial statement; Beneish model;All these keywords.
JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
- M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
- M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing
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