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Data analytics-based auditing: a case study of fraud detection in the banking context

Author

Listed:
  • Jean Robert Kala Kamdjoug

    (ESSCA School of Management)

  • Hyacinthe Djanan Sando

    (Catholic University of Central Africa)

  • Jules Raymond Kala

    (CIE-Centre de Recherche en Intelligence Artificielle)

  • Arielle Ornela Ndassi Teutio

    (Catholic University of Central Africa)

  • Sunil Tiwari

    (University of Bristol Business School, University of Bristol)

  • Samuel Fosso Wamba

    (TBS Business School)

Abstract

For a long time, decision-making in auditing was limited to a risk-oriented recommendation and consisted of the rigorous analysis of a sample of data. The new trend in the audit decision process focuses on the use of decision support systems (DSSs) founded on data analytics (DA) to better concentrate on in-depth analysis. This study examines how DA can improve the audit decision-making approach in the banking sector. We show that DA techniques can improve the quality of audit decision-making within banks and highlight the advantages associated with mastering these techniques, which results in a more effective and efficient audit of digital banking transactions. We propose an artifact-based data analytics-driven decision support system (DA-DSS) for an automatic fraud detection system supported by DA. The proposed DA-DSS artifact with a random forest classifier at its core is a promising innovation in the field of electronic transaction fraud detection. The results show that the random forest classifier can accurately classify the data generated by this artifact with an accuracy varying from 88 to 93% using transaction data collected from 2021 to 2022. Other classifiers including k-nearest neighbors (KNN) are also used, with a classification rate ranging from 66 to 88% for the same transaction datasets. These results show that the proposed DA-DSS with random forest can significantly improve auditing by reducing the time required for data analysis and increasing the results’ accuracy. Management can use the proposed artifact to enhance and speed up the decision-making process within their organization.

Suggested Citation

  • Jean Robert Kala Kamdjoug & Hyacinthe Djanan Sando & Jules Raymond Kala & Arielle Ornela Ndassi Teutio & Sunil Tiwari & Samuel Fosso Wamba, 2024. "Data analytics-based auditing: a case study of fraud detection in the banking context," Annals of Operations Research, Springer, vol. 340(2), pages 1161-1188, September.
    • Handle: RePEc:spr:annopr:v:340:y:2024:i:2:d:10.1007_s10479-024-06129-8
    DOI: 10.1007/s10479-024-06129-8
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    1. Jan vom Brocke & Alan Hevner & Alexander Maedche, 2020. "Introduction to Design Science Research," Progress in IS, in: Jan vom Brocke & Alan Hevner & Alexander Maedche (ed.), Design Science Research. Cases, pages 1-13, Springer.
    2. George Salijeni & Anna Samsonova-Taddei & Stuart Turley, 2019. "Big Data and changes in audit technology: contemplating a research agenda," Accounting and Business Research, Taylor & Francis Journals, vol. 49(1), pages 95-119, January.
    3. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    4. Fengchun Tang & Carolyn Strand Norman & Valaria P. Vendrzyk, 2017. "Exploring perceptions of data analytics in the internal audit function," Behaviour and Information Technology, Taylor & Francis Journals, vol. 36(11), pages 1125-1136, November.
    5. Oluwatoyin Esther Akinbowale & Polly Mashigo & Mulatu Fekadu Zerihun, 2023. "The integration of forensic accounting and big data technology frameworks for internal fraud mitigation in the banking industry," Cogent Business & Management, Taylor & Francis Journals, vol. 10(1), pages 2163560-216, December.
    6. Yoon, Kyunghee & Liu, Yue & Chiu, Tiffany & Vasarhelyi, Miklos A., 2021. "Design and evaluation of an advanced continuous data level auditing system: A three-layer structure," International Journal of Accounting Information Systems, Elsevier, vol. 42(C).
    7. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    8. Margaret H. Christ & Scott A. Emett & Scott L. Summers & David A. Wood, 2021. "Prepare for takeoff: improving asset measurement and audit quality with drone-enabled inventory audit procedures," Review of Accounting Studies, Springer, vol. 26(4), pages 1323-1343, December.
    9. Earley, Christine E., 2015. "Data analytics in auditing: Opportunities and challenges," Business Horizons, Elsevier, vol. 58(5), pages 493-500.
    10. Federica De Santis & Giuseppe D’Onza, 2021. "Big data and data analytics in auditing: in search of legitimacy," Meditari Accountancy Research, Emerald Group Publishing Limited, vol. 29(5), pages 1088-1112, February.
    11. Adrian Gepp & Martina K. Linnenluecke & Terrence J. O’Neill & Tom Smith, 2018. "Big data techniques in auditing research and practice: Current trends and future opportunities," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 40(1), pages 102-115, February.
    12. Robert L. Braun & Harold E. Davis, 2003. "Computer‐assisted audit tools and techniques: analysis and perspectives," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 18(9), pages 725-731, December.
    13. Ken Peffers & Tuure Tuunanen & Björn Niehaves, 2018. "Design science research genres: introduction to the special issue on exemplars and criteria for applicable design science research," European Journal of Information Systems, Taylor & Francis Journals, vol. 27(2), pages 129-139, March.
    14. repec:eme:maj000:maj-01-2018-1785 is not listed on IDEAS
    15. Geerts, Guido L., 2011. "A design science research methodology and its application to accounting information systems research," International Journal of Accounting Information Systems, Elsevier, vol. 12(2), pages 142-151.
    16. Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
    17. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1579-1580, October.
    18. 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.
    19. 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).
    20. 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).
    21. Aderemi O. Adewumi & Andronicus A. Akinyelu, 2017. "A survey of machine-learning and nature-inspired based credit card fraud detection techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 937-953, November.
    22. Al-Akra, Mahmoud & Abdel-Qader, Waleed & Billah, Mamun, 2016. "Internal auditing in the Middle East and North Africa: A literature review," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 26(C), pages 13-27.
    23. Rong-Ruey Duh & Kuo-Tay Chen & Ruey-Ching Lin & Li-Chun Kuo, 2014. "Do internal controls improve operating efficiency of universities?," Annals of Operations Research, Springer, vol. 221(1), pages 173-195, October.
    24. Kristof Coussement & Stefan Lessmann & Geert Verstraeten, 2017. "A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry," Post-Print hal-01745261, HAL.
    25. Sheppard, Blair H & Hartwick, Jon & Warshaw, Paul R, 1988. "The Theory of Reasoned Action: A Meta-analysis of Past Research with Recommendations for Modifications and Future Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(3), pages 325-343, December.
    26. repec:eme:maj000:maj-06-2017-1579 is not listed on IDEAS
    27. Yeamin Jacky & Noor Adwa Sulaiman, 2022. "The use of data analytics in external auditing: a content analysis approach," Asian Review of Accounting, Emerald Group Publishing Limited, vol. 30(1), pages 31-58, January.
    28. Georgia Boskou & Efstathios Kirkos & Charalambos Spathis, 2019. "Classifying internal audit quality using textual analysis: the case of auditor selection," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 34(8), pages 924-950, May.
    29. Riadh Manita & Najoua Elommal & Patricia Baudier & Lubica Hikkerova, 2020. "The digital transformation of external audit and its impact on corporate governance," Post-Print hal-04314467, HAL.
    30. Rosa Lombardi & Giustina Secundo, 2020. "The digital transformation of corporate reporting – a systematic literature review and avenues for future research," Meditari Accountancy Research, Emerald Group Publishing Limited, vol. 29(5), pages 1179-1208, September.
    31. Anna Bartoszewicz & Anna Rutkowska-Ziarko, 2021. "Practice of Non-Financial Reports Assurance Services in the Polish Audit Market—The Range, Limits and Prospects for the Future," Risks, MDPI, vol. 9(10), pages 1-23, October.
    32. 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.
    33. Manita, Riadh & Elommal, Najoua & Baudier, Patricia & Hikkerova, Lubica, 2020. "The digital transformation of external audit and its impact on corporate governance," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    34. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
    35. Ahmed Atef Oussii & Neila Boulila Taktak, 2018. "The impact of internal audit function characteristics on internal control quality," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 33(5), pages 450-469, May.
    36. Laura F. Spira & Michael Page, 2003. "Risk management: The reinvention of internal control and the changing role of internal audit," Accounting, Auditing & Accountability Journal, Emerald Group Publishing Limited, vol. 16(4), pages 640-661, October.
    37. Steven Ji-fan Ren & Samuel Fosso Wamba & Shahriar Akter & Rameshwar Dubey & Stephen J. Childe, 2017. "Modelling quality dynamics, business value and firm performance in a big data analytics environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5011-5026, September.
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