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Data mining journal entries for fraud detection: An exploratory study

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

Abstract

Fraud detection has become a critical component of financial audits and audit standards have heightened emphasis on journal entries as part of fraud detection. This paper canvasses perspectives on applying data mining techniques to journal entries. In the past, the impediment to researching journal entry data mining is getting access to journal entry data sets, which may explain why the published research in this area is a null set. For this project, we had access to journal entry data sets for 29 different organizations. Our initial exploratory test of the data sets had interesting preliminary findings. (1) For all 29 entities, the distribution of first digits of journal dollar amounts differed from that expected by Benford's Law. (2) Regarding last digits, unlike first digits, which are expected to have a logarithmic distribution, the last digits would be expected to have a uniform distribution. Our test found that the distribution was not uniform for many of the entities. In fact, eight entities had one number whose frequency was three times more than expected. (3) We compared the number of accounts related to the top five most frequently occurring three last digit combinations. Four entities had a very high occurrences of the most frequent three digit combinations that involved only a small set of accounts, one entity had a low occurrences of the most frequent three digit combination that involved a large set of accounts and 24 had a low occurrences of the most frequent three digit combinations that involved a small set of accounts. In general, the first four entities would probably pose the highest risk of fraud because it could indicate that the fraudster is covering up or falsifying a particular class of transactions. In the future, we will apply more data mining techniques to discover other patterns and relationships in the data sets. We also want to seed the dataset with fraud indicators (e.g., pairs of accounts that would not be expected in a journal entry) and compare the sensitivity of the different data mining techniques to find these seeded indicators.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ijoais:v:11:y:2010:i:3:p:157-181
    DOI: 10.1016/j.accinf.2010.08.001
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    3. Marco Schreyer & Timur Sattarov & Christian Schulze & Bernd Reimer & Damian Borth, 2019. "Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks," Papers 1908.00734, arXiv.org.
    4. Cano-Rodríguez, Manuel & Núñez-Nickel, Manuel & Licerán-Gutiérrez, Ana, 2025. "Divergence from Benford’s law fails to measure financial statement accuracy," International Journal of Accounting Information Systems, Elsevier, vol. 56(C).
    5. 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.
    6. Pizzi, Simone & Venturelli, Andrea & Variale, Michele & Macario, Giuseppe Pio, 2021. "Assessing the impacts of digital transformation on internal auditing: A bibliometric analysis," Technology in Society, Elsevier, vol. 67(C).
    7. Stratopoulos, Theophanis C. & Vance, Tom W. & Zou, Xiorong, 2013. "Incentive effects of enterprise systems on the magnitude and detectability of reporting manipulations," International Journal of Accounting Information Systems, Elsevier, vol. 14(1), pages 39-57.
    8. 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.
    9. Werner, Michael, 2017. "Financial process mining - Accounting data structure dependent control flow inference," International Journal of Accounting Information Systems, Elsevier, vol. 25(C), pages 57-80.
    10. Fay, Rebecca & Negangard, Eric M., 2017. "Manual journal entry testing: Data analytics and the risk of fraud," Journal of Accounting Education, Elsevier, vol. 38(C), pages 37-49.
    11. de Araújo Silva, Archibald & Aparecida Gouvêa, Maria, 2023. "Study on the effect of sample size on type I error, in the first, second and first-two digits excessmad tests," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    12. 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.
    13. 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.
    14. Montag, Josef, 2015. "Identifying Odometer Fraud: Evidence from the Used Car Market in the Czech Republic," MPRA Paper 65182, University Library of Munich, Germany.
    15. Ricardo Sartori Cella & Ercilio Zanolla, 2018. "Benford’s Law and transparency: an analysis of municipal expenditure," Brazilian Business Review, Fucape Business School, vol. 15(4), pages 331-347, July.
    16. Montag, Josef, 2017. "Identifying odometer fraud in used car market data," Transport Policy, Elsevier, vol. 60(C), pages 10-23.
    17. Fogarty, Timothy J. & Campbell, Cory, 2024. "The big data crossroads: Accounting education and the challenge of 21st century technology," Journal of Accounting Education, Elsevier, vol. 68(C).
    18. Pall Rikhardsson & Kishore Singh & Peter Best, 2019. "Exploring Continuous Auditing Solutions and Internal Auditing: A Research Note," Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 18(4), pages 614-639, December.
    19. Kishore Singh & Peter Best, 2020. "Implementing Benford’s Law in Continuous Monitoring Applications," Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 19(2), pages 379-404, June.
    20. Ensminger, Jean & Leder-Luis, Jetson, 2025. "Detecting Corruption: Evidence from a World Bank project in Kenya," World Development, Elsevier, vol. 188(C).
    21. 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.
    22. Meryem Ayad & Said El Mezouari, 2022. "Research on the implication of artificial intelligence in accounting subfields: current research trends from bibliometric analysis, and research directions," Post-Print hal-05138659, HAL.

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