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Stacked generalizations in imbalanced fraud data sets using resampling methods

Author

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  • Kathleen R Kerwin
  • Nathaniel D Bastian

Abstract

Predicting fraud is challenging due to inherent issues in the fraud data structure, since the crimes are committed through trickery or deceit with an ever-present moving target of changing modus operandi to circumvent human and system controls. As a national security challenge, criminals continually exploit the electronic financial system to defraud consumers and businesses by finding weaknesses in the system, including in audit controls. This study uses stacked generalization using meta or super learners for improving the performance of algorithms in step one (minimizing the algorithm error rate to reduce its bias in the learning set) and then in step two the results are input into the meta learner with its stacked blended output (with the weakest algorithms learning better). A fundamental key to fraud data is that it is inherently not systematic, and an optimal resampling methodology has yet not been identified. Building a test harness, for all permutations of algorithm sample set pairs, demonstrates that the complex, intrinsic data structures are all thoroughly tested. A comparative analysis on fraud data that applies stacked generalizations provides useful insight to find the optimal mathematical formula for imbalanced fraud data sets necessary to improve upon fraud detection for national security.

Suggested Citation

  • Kathleen R Kerwin & Nathaniel D Bastian, 2021. "Stacked generalizations in imbalanced fraud data sets using resampling methods," The Journal of Defense Modeling and Simulation, , vol. 18(3), pages 175-192, July.
    • Handle: RePEc:sae:joudef:v:18:y:2021:i:3:p:175-192
    DOI: 10.1177/1548512920962219
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    References listed on IDEAS

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    1. Mills, Clinton, 2017. "Predictive analytics in fraud and AML," Journal of Financial Compliance, Henry Stewart Publications, vol. 1(1), pages 17-26, June.
    2. Maher Maalouf & Theodore B. Trafalis, 2011. "Rare events and imbalanced datasets: an overview," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 3(4), pages 375-388.
    3. 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.
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