Credit Card Fraud Prediction Based on the Improved Data Balancing Technique and the Gradient Boosting Algorithm

Ying Jin () and Yanming Chen ()
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Ying Jin: Student, Shantou University
Yanming Chen: Student, Shantou University

A chapter in Proceedings of the 2023 5th International Conference on Economic Management and Cultural Industry (ICEMCI 2023), 2024, pp 621-629 from Springer

Abstract: Abstract This paper aims to build a credit card transaction fraud classification model by combining improved data balancing techniques and gradient boosting algorithms. After data cleaning and preprocessing, we applied random oversampling, SMOTE oversampling, random undersampling, and Tomek Links undersampling methods to deal with the highly imbalanced dataset. Afterwards, we established classification models using LightGBM, XGBoost and CatBoost algorithms for comparative experiments. Finally, we selected the best performing gradient boosting model under each data balancing method as the first layer models of the Stacking algorithm, and the classification tree model as the second layer model. Its accuracy and F1-score on the testing set reached 0.98.

Keywords: Credit Card Fraud; Imbalanced Data; Gradient Boosting; Stacking; Financial Transaction Security; Classification (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-368-9_74

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DOI: 10.2991/978-94-6463-368-9_74

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