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

Version 2 2024-06-06, 08:14
Version 1 2022-02-24, 14:35
journal contribution
posted on 2024-06-06, 08:14 authored by AK Nandi, KK Randhawa, HS Chua, M Seera, Chee Peng Lim
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 multiclassifier 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.

History

Journal

PLoS ONE

Volume

17

Article number

e0260579.

Pagination

1-10

Location

San Francisco, Calif.

eISSN

1932-6203

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

1

Publisher

Public Library of Science (PLoS)

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