Credit Card Fraud Detection Prediction: Machine Learning Algorithm

Yi Qu () and Jiani Jin ()
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Yi Qu: Wenzhou-Kean University, Department of Finance
Jiani Jin: Wenzhou-Kean University, Department of Mathematics

A chapter in Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023), 2024, pp 760-767 from Springer

Abstract: Abstract Someone commits payment fraud when they obtain the payment information of another person and use it for unauthorized transactions or purchases. Owing to the ease and convenience of e-commerce, digital purchasing is becoming increasingly popular today and because of the convenience of online shopping, many individuals prefer to shop online. This has resulted in a substantial rise in credit card fraud. Detecting and preventing payment fraud is difficult because the standard rules-based anti-fraud systems deployed by banks cannot manage the high volume of online transactions. This creates unique difficulties for banks and a substantial increase in losses. Therefore, it is crucial to effectively identify and eliminate fraud. In our research, we use machine learning methods to construct models that can detect and analyze fraudulent payments. We primarily employ the Generalized Linear, Decision Tree, Gradient Boosting, and Naive Bayes Models, and determine that the Generalized Linear Model is the most effective.

Keywords: credit card fraud; machine learning; Generalized Linear Model; Decision Tree; Gradient Boosting; Naïve Bayes (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-256-9_77

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