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A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits

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  • Gray, Glen L.
  • Debreceny, Roger S.

Abstract

This paper explores the application of data mining techniques to fraud detection in the audit of financial statements and proposes a taxonomy to support and guide future research. Currently, the application of data mining to auditing is at an early stage of development and researchers take a scatter-shot approach, investigating patterns in financial statement disclosures, text in annual reports and MD&As, and the nature of journal entries without appropriate guidance being drawn from lessons in known fraud patterns. To develop structure to research in data mining, we create a taxonomy that combines research on patterns of observed fraud schemes with an appreciation of areas that benefit from productive application of data mining. We encapsulate traditional views of data mining that operates primarily on quantitative data, such as financial statement and journal entry data. In addition, we draw on other forms of data mining, notably text and email mining.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ijoais:v:15:y:2014:i:4:p:357-380
    DOI: 10.1016/j.accinf.2014.05.006
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    1. Jans, Mieke & Lybaert, Nadine & Vanhoof, Koen, 2010. "Internal fraud risk reduction: Results of a data mining case study," International Journal of Accounting Information Systems, Elsevier, vol. 11(1), pages 17-41.
    2. Elsas, Ph.I., 2008. "X-raying Segregation of Duties: Support to illuminate an enterprise's immunity to solo-fraud," International Journal of Accounting Information Systems, Elsevier, vol. 9(2), pages 82-93.
    3. Worrell, James & Wasko, Molly & Johnston, Allen, 2013. "Social network analysis in accounting information systems research," International Journal of Accounting Information Systems, Elsevier, vol. 14(2), pages 127-137.
    4. Jans, Mieke & Alles, Michael & Vasarhelyi, Miklos, 2013. "The case for process mining in auditing: Sources of value added and areas of application," International Journal of Accounting Information Systems, Elsevier, vol. 14(1), pages 1-20.
    5. Debreceny, Roger S. & Gray, Glen L., 2010. "Data mining journal entries for fraud detection: An exploratory study," International Journal of Accounting Information Systems, Elsevier, vol. 11(3), pages 157-181.
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    Cited by:

    1. Rabeea SADAF, 2016. "Benford’S Law In The Case Of Hungarian Whole-Sale Trade Sector," SEA - Practical Application of Science, Romanian Foundation for Business Intelligence, Editorial Department, issue 12, pages 561-566, December.
    2. Chen, Yuh-Jen & Liou, Wan-Ching & Chen, Yuh-Min & Wu, Jyun-Han, 2019. "Fraud detection for financial statements of business groups," International Journal of Accounting Information Systems, Elsevier, vol. 32(C), pages 1-23.
    3. Alles, Michael & Gray, Glen L., 2016. "Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors," International Journal of Accounting Information Systems, Elsevier, vol. 22(C), pages 44-59.
    4. Yuan Song & Hongwei Wang & Maoran Zhu, 2018. "Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-14, December.
    5. Fábio Albuquerque & Paula Gomes Dos Santos, 2023. "Recent Trends in Accounting and Information System Research: A Literature Review Using Textual Analysis Tools," FinTech, MDPI, vol. 2(2), pages 1-27, April.
    6. Laskai András, 2019. "AI foundations of the international business planning and the AI consciousness model," International Journal of Science and Business, IJSAB International, vol. 3(1), pages 17-28.
    7. Jacqueline Birt & Maryam Safari & Vincent Bicudo de Castro, 2023. "Critical analysis of integration of ICT and data analytics into the accounting curriculum: A multidimensional perspective," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 4037-4063, December.
    8. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    9. Earley, Christine E., 2015. "Data analytics in auditing: Opportunities and challenges," Business Horizons, Elsevier, vol. 58(5), pages 493-500.
    10. Mushang Lee & Yu-Lan Huang, 2020. "Corporate Social Responsibility and Corporate Performance: A Hybrid Text Mining Algorithm," Sustainability, MDPI, vol. 12(8), pages 1-19, April.
    11. Federica De Santis, 2018. "Big Data e revisione contabile: uno studio esplorativo nel contesto italiano," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(2), pages 129-154.
    12. Andrea Cardoni & Evgeniia Kiseleva & Francesco De Luca, 2020. "Continuous auditing and data mining for strategic risk control and anticorruption: Creating “fair” value in the digital age," Business Strategy and the Environment, Wiley Blackwell, vol. 29(8), pages 3072-3085, December.
    13. Anastassia Fedyk & James Hodson & Natalya Khimich & Tatiana Fedyk, 2022. "Is artificial intelligence improving the audit process?," Review of Accounting Studies, Springer, vol. 27(3), pages 938-985, September.
    14. Saxton, Gregory D. & Guo, Chao, 2020. "Social media capital: Conceptualizing the nature, acquisition, and expenditure of social media-based organizational resources," International Journal of Accounting Information Systems, Elsevier, vol. 36(C).
    15. Abdullah Albizri & Deniz Appelbaum & Nicholas Rizzotto, 2019. "Evaluation of financial statements fraud detection research: a multi-disciplinary analysis," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 16(4), pages 206-241, December.
    16. Mirjana Pejić Bach & Živko Krstić & Sanja Seljan & Lejla Turulja, 2019. "Text Mining for Big Data Analysis in Financial Sector: A Literature Review," Sustainability, MDPI, vol. 11(5), pages 1-27, February.
    17. Ruhnke, Klaus, 2023. "Empirical research frameworks in a changing world: The case of audit data analytics," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 51(C).
    18. Freiman, Jamie W. & Kim, Yongbum & Vasarhelyi, Miklos A., 2022. "Full population testing: Applying multidimensional audit data sampling (MADS) to general ledger data auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    19. Dilla, William N. & Raschke, Robyn L., 2015. "Data visualization for fraud detection: Practice implications and a call for future research," International Journal of Accounting Information Systems, Elsevier, vol. 16(C), pages 1-22.
    20. Huidong Sun & Mustafa Raza Rabbani & Muhammad Safdar Sial & Siming Yu & José António Filipe & Jacob Cherian, 2020. "Identifying Big Data’s Opportunities, Challenges, and Implications in Finance," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    21. 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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    Keywords

    Auditing; Fraud; Data mining;
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