Research on Detecting Credit Card Fraud Through Machine Learning Methods

DOI

10.2991/978-94-6463-102-9_107How to use a DOI?

Keywords

Machine Learning; Fraud Detection; SMOTE; Confusion Matrix

Abstract

The heavy use of credit cards inevitably leads to the escalation of fraud technology and a surge in fraudulent behavior. Machine learning, a multi-interdisciplinary discipline with numerous algorithms, can effectively detect and prevent financial fraud. This study focuses on several common machine learning methods applied to fraud detection and then evaluates how they perform on real data, including Bagging, Random Forest, Decision Tree, and AdaBoost. However, the proportion of fraudulent transactions in real transaction data is extremely unbalanced. SMOTE can determine the data imbalance problem, while confusion matrices visualize the classification results of different classes. The experiment results reveal that Random Forest performs best for both unbalanced and balanced data. It indicates that random forest is better for detecting fraudulent transactions.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shunning Dai
PY  - 2022
DA  - 2022/12/29
TI  - Research on Detecting Credit Card Fraud Through Machine Learning Methods
BT  - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)
PB  - Atlantis Press
SP  - 1030
EP  - 1037
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-102-9_107
DO  - 10.2991/978-94-6463-102-9_107
ID  - Dai2022
ER  -