Comparing Machine Learning Models and Voting Ensembles for Credit Card Fraud Detection

DOI

10.2991/978-2-38476-585-0_22How to use a DOI?

Keywords

Imbalanced Classification; Ensemble Learning; Credit Card Fraud Detection

Abstract

Credit card fraud poses a growing threat to global financial systems. The rarity of fraudulent transactions makes this a highly imbalanced classification problem, requiring models that can maintain high recall without sacrificing precision. The effectiveness of four supervised learning models is assessed in this study—Logistic Regression, Random Forest, eXtreme Gradient Boosting (XGBoost), and a Soft Voting Ensemble—on a PCA-transformed credit card dataset with a 5% fraud ratio. Area Under the Curve (AUC), F1-score, precision, and recall are evaluation measures. Logistic Regression demonstrated high recall but poor overall balance, leading to its exclusion from test set evaluation. Random Forest achieved perfect precision but lower recall, while XGBoost and the Soft Voting Ensemble showed stronger balance across metrics. Soft Voting produced the best F1-score and most stable performance across both validation and test sets. These results indicate that ensemble methods, particularly soft voting, can effectively address imbalanced classification in fraud detection. Future work may explore alternative sampling strategies, larger datasets, and model tuning frameworks to further improve detection performance and adaptability to real-world scenarios.

Copyright

© 2026 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  - Junhong Yang
PY  - 2026
DA  - 2026/06/18
TI  - Comparing Machine Learning Models and Voting Ensembles for Credit Card Fraud Detection
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 185
EP  - 192
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_22
DO  - 10.2991/978-2-38476-585-0_22
ID  - Yang2026
ER  -