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Accounting fraud detection using contextual language learning

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  • Bhattacharya, Indranil
  • Mickovic, Ana

Abstract

Accounting fraud is a widespread problem that causes significant damage in the economic market. Detection and investigation of fraudulent firms require a large amount of time, money, and effort for corporate monitors and regulators. In this study, we explore how textual contents from financial reports help in detecting accounting fraud. Pre-trained contextual language learning models, such as BERT, have significantly advanced natural language processing in recent years. We fine-tune the BERT model on Management Discussion and Analysis (MD&A) sections of annual 10-K reports from the Securities and Exchange Commission (SEC) database. Our final model outperforms the textual benchmark model and the quantitative benchmark model from the previous literature by 15% and 12%, respectively. Further, our model identifies five times more fraudulent firm-year observations than the textual benchmark by investigating the same number of firms, and three times more than the quantitative benchmark. Optimizing this investigation process, where more fraudulent observations are detected in the same size of the investigation sample, would be of great economic significance for regulators, investors, financial analysts, and auditors.

Suggested Citation

  • Bhattacharya, Indranil & Mickovic, Ana, 2024. "Accounting fraud detection using contextual language learning," International Journal of Accounting Information Systems, Elsevier, vol. 53(C).
    • Handle: RePEc:eee:ijoais:v:53:y:2024:i:c:s1467089524000150
    DOI: 10.1016/j.accinf.2024.100682
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    1. David F. Larcker & Anastasia A. Zakolyukina, 2012. "Detecting Deceptive Discussions in Conference Calls," Journal of Accounting Research, Wiley Blackwell, vol. 50(2), pages 495-540, May.
    2. Durnev, Art & Mangen, Claudine, 2020. "The spillover effects of MD&A disclosures for real investment: The role of industry competition," Journal of Accounting and Economics, Elsevier, vol. 70(1).
    3. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    4. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    5. Sunita Goel & Jagdish Gangolly, 2012. "Beyond The Numbers: Mining The Annual Reports For Hidden Cues Indicative Of Financial Statement Fraud," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 75-89, April.
    6. Lynnette Purda & David Skillicorn, 2015. "Accounting Variables, Deception, and a Bag of Words: Assessing the Tools of Fraud Detection," Contemporary Accounting Research, John Wiley & Sons, vol. 32(3), pages 1193-1223, September.
    7. Hoberg, Gerard & Lewis, Craig, 2017. "Do fraudulent firms produce abnormal disclosure?," Journal of Corporate Finance, Elsevier, vol. 43(C), pages 58-85.
    8. Gill A. Pratt, 2015. "Is a Cambrian Explosion Coming for Robotics?," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 51-60, Summer.
    9. Patricia M. Dechow & Weili Ge & Chad R. Larson & Richard G. Sloan, 2011. "Predicting Material Accounting Misstatements," Contemporary Accounting Research, John Wiley & Sons, vol. 28(1), pages 17-82, March.
    10. Alexander Dyck & Adair Morse & Luigi Zingales, 2010. "Who Blows the Whistle on Corporate Fraud?," Journal of Finance, American Finance Association, vol. 65(6), pages 2213-2253, December.
    11. Berkin, Anil & Aerts, Walter & Van Caneghem, Tom, 2023. "Feasibility analysis of machine learning for performance-related attributional statements," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    12. John Berns & Patty Bick & Ryan Flugum & Reza Houston, 2022. "Do changes in MD&A section tone predict investment behavior?," The Financial Review, Eastern Finance Association, vol. 57(1), pages 129-153, February.
    13. Brian J. Bushee & Ian D. Gow & Daniel J. Taylor, 2018. "Linguistic Complexity in Firm Disclosures: Obfuscation or Information?," Journal of Accounting Research, Wiley Blackwell, vol. 56(1), pages 85-121, March.
    14. Lori Holder-Webb & Jaffrey Cohen, 2007. "The Association between Disclosure, Distress, and Failure," Journal of Business Ethics, Springer, vol. 75(3), pages 301-314, October.
    15. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
    16. Volkan Muslu & Suresh Radhakrishnan & K. R. Subramanyam & Dongkuk Lim, 2015. "Forward-Looking MD&A Disclosures and the Information Environment," Management Science, INFORMS, vol. 61(5), pages 931-948, May.
    17. Nerissa C. Brown & Richard M. Crowley & W. Brooke Elliott, 2020. "What Are You Saying? Using topic to Detect Financial Misreporting," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 237-291, March.
    18. Li, Feng, 2008. "Annual report readability, current earnings, and earnings persistence," Journal of Accounting and Economics, Elsevier, vol. 45(2-3), pages 221-247, August.
    19. Craja, Patricia & Kim, Alisa & Lessmann, Stefan, 2020. "Deep Learning application for fraud detection in financial statements," IRTG 1792 Discussion Papers 2020-007, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
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    1. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.

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