Credit Card Fraud Detection Based on Machine Learning Prediction

DOI

10.2991/978-94-6463-540-9_5How to use a DOI?

Keywords

Credit Card Fraud Detection; Machine Learning; Random Forest; Support Vector Machine; Cross Validation

Abstract

In recent years, credit card fraud has become increasingly rampant, posing a major threat to financial security. To effectively detect and prevent credit card fraud, this study combines three machine learning algorithms, namely Random Forest (RS), Support Vector Machine (SVM), and Logistic Regression (LR), to deeply analyze credit card transaction data through cross-validation with different multiplicity. The study results show that Random Forest performs best in terms of precision and F1 scores, SVM performs well in terms of recall, and logistic regression has a high Area Under Curve (AUC) value in distinguishing between fraudulent and non-fraudulent transactions. Through meticulous data preprocessing, feature engineering, and model optimization, this study significantly improves the performance and stability of each model.The research results of this paper provide an important reference for building an efficient and reliable credit card fraud detection system, which has important practical application value and theoretical significance across different sectors and industries.

Copyright

© 2024 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  - Ge Yang
PY  - 2024
DA  - 2024/10/16
TI  - Credit Card Fraud Detection Based on Machine Learning Prediction
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 35
EP  - 45
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_5
DO  - 10.2991/978-94-6463-540-9_5
ID  - Yang2024
ER  -