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Credit card fraud detection using a hierarchical behavior-knowledge space model

Author

Listed:
  • Asoke K Nandi
  • Kuldeep Kaur Randhawa
  • Hong Siang Chua
  • Manjeevan Seera
  • Chee Peng Lim

Abstract

With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.

Suggested Citation

  • Asoke K Nandi & Kuldeep Kaur Randhawa & Hong Siang Chua & Manjeevan Seera & Chee Peng Lim, 2022. "Credit card fraud detection using a hierarchical behavior-knowledge space model," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-16, January.
    • Handle: RePEc:plo:pone00:0260579
    DOI: 10.1371/journal.pone.0260579
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    References listed on IDEAS

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    4. Fang Lv & Junheng Huang & Wei Wang & Yuliang Wei & Yunxiao Sun & Bailing Wang, 2019. "A two-route CNN model for bank account classification with heterogeneous data," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-22, August.
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