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Online payment fraud: from anomaly detection to risk management

Author

Listed:
  • Paolo Vanini

    (University of Basel)

  • Sebastiano Rossi

    (Novartis AG)

  • Ermin Zvizdic

    (swissQuant Group)

  • Thomas Domenig

    (IT Couture)

Abstract

Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account. Successfully preventing this requires the detection of as many fraudsters as possible, without producing too many false alarms. This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud. In addition, classical machine learning methods must be extended, minimizing expected financial losses. Finally, fraud can only be combated systematically and economically if the risks and costs in payment channels are known. We define three models that overcome these challenges: machine learning-based fraud detection, economic optimization of machine learning results, and a risk model to predict the risk of fraud while considering countermeasures. The models were tested utilizing real data. Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15% compared to a benchmark consisting of static if-then rules. Optimizing the machine-learning model further reduces the expected losses by 52%. These results hold with a low false positive rate of 0.4%. Thus, the risk framework of the three models is viable from a business and risk perspective.

Suggested Citation

  • Paolo Vanini & Sebastiano Rossi & Ermin Zvizdic & Thomas Domenig, 2023. "Online payment fraud: from anomaly detection to risk management," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    • Handle: RePEc:spr:fininn:v:9:y:2023:i:1:d:10.1186_s40854-023-00470-w
    DOI: 10.1186/s40854-023-00470-w
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    References listed on IDEAS

    as
    1. Andrada-Ioana Sabău (Popa) & Codruța Mare & Ioana Lavinia Safta, 2021. "A Statistical Model of Fraud Risk in Financial Statements. Case for Romania Companies," Risks, MDPI, vol. 9(6), pages 1-15, June.
    2. Juszczak, Piotr & Adams, Niall M. & Hand, David J. & Whitrow, Christopher & Weston, David J., 2008. "Off-the-peg and bespoke classifiers for fraud detection," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4521-4532, May.
    3. Power, Michael, 2013. "The apparatus of fraud risk," Accounting, Organizations and Society, Elsevier, vol. 38(6), pages 525-543.
    4. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    5. van Liebergen, Bart, 2017. "Machine learning: A revolution in risk management and compliance?," Journal of Financial Transformation, Capco Institute, vol. 45, pages 60-67.
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    Cited by:

    1. Sushrut Ghimire, 2023. "TimeTrail: Unveiling Financial Fraud Patterns through Temporal Correlation Analysis," Papers 2308.14215, arXiv.org.
    2. Yunchuan Sun & Lu Liu & Ying Xu & Xiaoping Zeng & Yufeng Shi & Haifeng Hu & Jie Jiang & Ajith Abraham, 2024. "Alternative data in finance and business: emerging applications and theory analysis (review)," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-32, December.
    3. Ludivia Hernandez Aros & Luisa Ximena Bustamante Molano & Fernando Gutierrez-Portela & John Johver Moreno Hernandez & Mario Samuel Rodríguez Barrero, 2024. "Financial fraud detection through the application of machine learning techniques: a literature review," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-22, December.

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