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Reducing false positives in fraud detection: Combining the red flag approach with process mining

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  • Baader, Galina
  • Krcmar, Helmut

Abstract

Fraud detection often includes analyzing large datasets of enterprise resource planning systems to locate irregularities. Analysis of the datasets often results in a large number of false positives, that is, entries wrongly identified as fraud. The aim of our research is to reduce the number of false positives by combining the red flag-based approach with process mining. The red flag approach presents hints for unusual behavior, whereas process mining reconstructs and visualizes the as-is business process from the underlying dataset. The combination of these two techniques allows for identification and subsequent visualization of possible fraudulent process instances with the corresponding red flags. We exemplarily applied our new approach to the purchase-to-pay business process to successfully identify 15 of 31 fraud cases in our dataset. Our false positive rate was 0.37%, which is considerably lower than rates reported in similar research papers.

Suggested Citation

  • Baader, Galina & Krcmar, Helmut, 2018. "Reducing false positives in fraud detection: Combining the red flag approach with process mining," International Journal of Accounting Information Systems, Elsevier, vol. 31(C), pages 1-16.
    • Handle: RePEc:eee:ijoais:v:31:y:2018:i:c:p:1-16
    DOI: 10.1016/j.accinf.2018.03.004
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    References listed on IDEAS

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    1. Ariane Lambert‐Mogiliansky & Konstantin Sonin, 2006. "Collusive Market Sharing and Corruption in Procurement," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 15(4), pages 883-908, December.
    2. Ariane Lambert-Mogiliansky & Konstantin Sonin, 2006. "Market Sharing in Procurement," Post-Print halshs-00754126, HAL.
    3. Ariane Lambert‐Mogiliansky & Konstantin Sonin, 2006. "Collusive Market Sharing and Corruption in Procurement," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 15(4), pages 883-908, December.
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    Citations

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    Cited by:

    1. Mark E. Lokanan & Prerna Sharma, 2023. "Two Decades of Accounting Fraud Research: The Missing Meso-Level Analysis," SAGE Open, , vol. 13(3), pages 21582440231, September.
    2. 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.
    3. Adrian-Cosmin Caraiman, 2020. "Internal Control in Corporate Governance," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 606-611, December.
    4. 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.
    5. Werner, Michael & Wiese, Michael & Maas, Annalouise, 2021. "Embedding process mining into financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 41(C).
    6. 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).
    7. Zerbino, Pierluigi & Stefanini, Alessandro & Aloini, Davide, 2021. "Process Science in Action: A Literature Review on Process Mining in Business Management," Technological Forecasting and Social Change, Elsevier, vol. 172(C).

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