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Identification of fraudulent financial statements through a multi‐label classification approach

Author

Listed:
  • Maria Tragouda
  • Michalis Doumpos
  • Constantin Zopounidis

Abstract

Although the financial audit controls in companies have advanced over the years, the number of corporate fraud instances is growing, thus raising the need for investigating the factors that can be used as early warning signals and developing effective systems for identifying financial fraud. In this paper, financial statements from 133 Greek companies listed in the Athens Stock Exchange over the period 2014 to 2019 are investigated, based on the fraud diamond theory. Financial data and corporate governance variables are used as inputs to data mining techniques to develop models that can identify patterns of irregularities in a company's financial reports. To this end, popular machine learning classification algorithms are employed in a novel multi‐label classification setting that not only identifies fraudulent cases but also considers the nature of the auditors' comments. The results indicate that the proposed multi‐label approach provides enhanced results compared to binary classification algorithms, avoiding inconsistent outputs with respect to the existence of different forms of manipulation of financial statements.

Suggested Citation

  • Maria Tragouda & Michalis Doumpos & Constantin Zopounidis, 2024. "Identification of fraudulent financial statements through a multi‐label classification approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    • Handle: RePEc:wly:isacfm:v:31:y:2024:i:2:n:e1564
    DOI: 10.1002/isaf.1564
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    References listed on IDEAS

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    1. 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.
    2. Yi Wei & Jianguo Chen & Carolyn Wirth, 2017. "Detecting fraud in Chinese listed company balance sheets," Pacific Accounting Review, Emerald Group Publishing Limited, vol. 29(3), pages 356-379, August.
    3. Zhang, Yi & Hu, Ailing & Wang, Jiahua & Zhang, Yaojie, 2022. "Detection of fraud statement based on word vector: Evidence from financial companies in China," Finance Research Letters, Elsevier, vol. 46(PB).
    4. Artem Bequé & Kristof Coussement & Ross Gayler & Stefan Lessmann, 2017. "Approaches for credit scorecard calibration: An empirical analysis," Post-Print hal-01745262, HAL.
    5. Ch. Spathis & M. Doumpos & C. Zopounidis, 2002. "Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques," European Accounting Review, Taylor & Francis Journals, vol. 11(3), pages 509-535.
    6. Kangan Sayal & Gurparkash Singh, 2020. "Investigating the role of theory of planned behavior and Machiavellianism in earnings management intentions," Accounting Research Journal, Emerald Group Publishing Limited, vol. 33(6), pages 653-668, September.
    7. Neda Abdelhamid & Arun Padmavathy & David Peebles & Fadi Thabtah & Daymond Goulder-Horobin, 2020. "Data Imbalance in Autism Pre-Diagnosis Classification Systems: An Experimental Study," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-16, March.
    8. Joseph V. Carcello & Albert L. Nagy, 2004. "Client size, auditor specialization and fraudulent financial reporting," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 19(5), pages 651-668, June.
    9. Dimitrios Kydros & Michail Pazarskis & Athanasia Karakitsiou, 2022. "A framework for identifying the falsified financial statements using network textual analysis: a general model and the Greek example," Annals of Operations Research, Springer, vol. 316(1), pages 513-527, September.
    10. James L. Bierstaker & Richard G. Brody & Carl Pacini, 2006. "Accountants' perceptions regarding fraud detection and prevention methods," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 21(5), pages 520-535, June.
    11. Karpoff, Jonathan M., 2021. "The future of financial fraud," Journal of Corporate Finance, Elsevier, vol. 66(C).
    12. Gray, Glen L. & Debreceny, Roger S., 2014. "A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 15(4), pages 357-380.
    13. Arjan Reurink, 2018. "Financial Fraud: A Literature Review," Journal of Economic Surveys, Wiley Blackwell, vol. 32(5), pages 1292-1325, December.
    14. 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.
    15. Maria Krambia‐Kapardis & Chris Christodoulou & Michalis Agathocleous, 2010. "Neural networks: the panacea in fraud detection?," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 25(7), pages 659-678, July.
    16. 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.
    17. Christian Lohmann & Thorsten Ohliger, 2019. "The total cost of misclassification in credit scoring: A comparison of generalized linear models and generalized additive models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(5), pages 375-389, August.
    18. Chen, Gongmeng & Firth, Michael & Gao, Daniel N. & Rui, Oliver M., 2006. "Ownership structure, corporate governance, and fraud: Evidence from China," Journal of Corporate Finance, Elsevier, vol. 12(3), pages 424-448, June.
    19. Fen‐May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 23(7), pages 650-662, July.
    20. 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.
    21. Firth, Michael & Rui, Oliver M. & Wu, Wenfeng, 2011. "Cooking the books: Recipes and costs of falsified financial statements in China," Journal of Corporate Finance, Elsevier, vol. 17(2), pages 371-390, April.
    22. Allen H. Huang & Reuven Lehavy & Amy Y. Zang & Rong Zheng, 2018. "Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach," Management Science, INFORMS, vol. 64(6), pages 2833-2855, June.
    23. Zhou, Fangzhao & Zhang, Zenan & Yang, Jun & Su, Yunpeng & An, Yunbi, 2018. "Delisting pressure, executive compensation, and corporate fraud: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 48(C), pages 17-34.
    24. Normah Omar & Zulaikha ‘Amirah Johari & Malcolm Smith, 2017. "Predicting fraudulent financial reporting using artificial neural network," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 24(2), pages 362-387, May.
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