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Performance criteria for plastic card fraud detection tools

Author

Listed:
  • D J Hand

    (Imperial College
    Institute for Mathematical Sciences, Imperial College)

  • C Whitrow

    (Institute for Mathematical Sciences, Imperial College)

  • N M Adams

    (Imperial College)

  • P Juszczak

    (Institute for Mathematical Sciences, Imperial College)

  • D Weston

    (Institute for Mathematical Sciences, Imperial College)

Abstract

In predictive data mining, algorithms will be both optimized and compared using a measure of predictive performance. Different measures will yield different results, and it follows that it is crucial to match the measure to the true objectives. In this paper, we explore the desirable characteristics of measures for constructing and evaluating tools for mining plastic card data to detect fraud. We define two measures, one based on minimizing the overall cost to the card company, and the other based on minimizing the amount of fraud given the maximum number of investigations the card company can afford to make. We also describe a plot, analogous to the standard ROC, for displaying the performance trace of an algorithm as the relative costs of the two different kinds of misclassification—classing a fraudulent transaction as legitimate or vice versa—are varied.

Suggested Citation

  • D J Hand & C Whitrow & N M Adams & P Juszczak & D Weston, 2008. "Performance criteria for plastic card fraud detection tools," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 956-962, July.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:7:d:10.1057_palgrave.jors.2602418
    DOI: 10.1057/palgrave.jors.2602418
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    References listed on IDEAS

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    1. D J Hand, 2005. "Good practice in retail credit scorecard assessment," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1109-1117, September.
    2. D. J. Hand, 2001. "Measuring Diagnostic Accuracy of Statistical Prediction Rules," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(1), pages 3-16, March.
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    Cited by:

    1. Yang, Yi & Guo, Yuxuan & Chang, Xiangyu, 2021. "Angle-based cost-sensitive multicategory classification," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    2. Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
    3. Christoforos Anagnostopoulos & Dimitris Tasoulis & Niall Adams & David Hand, 2009. "Temporally adaptive estimation of logistic classifiers on data streams," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 3(3), pages 243-261, December.
    4. Bart Baesens & Sebastiaan Höppner & Irene Ortner & Tim Verdonck, 2021. "robROSE: A robust approach for dealing with imbalanced data in fraud detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 841-861, September.
    5. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
    6. Hand, David J. & Crowder, Martin J., 2012. "Overcoming selectivity bias in evaluating new fraud detection systems for revolving credit operations," International Journal of Forecasting, Elsevier, vol. 28(1), pages 216-223.
    7. Sanjeev Jha & J. Christopher Westland, 2013. "A Descriptive Study of Credit Card Fraud Pattern," Global Business Review, International Management Institute, vol. 14(3), pages 373-384, September.
    8. Höppner, Sebastiaan & Baesens, Bart & Verbeke, Wouter & Verdonck, Tim, 2022. "Instance-dependent cost-sensitive learning for detecting transfer fraud," European Journal of Operational Research, Elsevier, vol. 297(1), pages 291-300.

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