Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data

DOI

10.2991/978-94-6463-835-6_65How to use a DOI?

Keywords

Supervised Learning; Machine Learning & Deep Learning; Class Imbalance; Online Transactions; Fraud Detection

Abstract

Fraud detection remains a critical task in high-stakes domains such as finance and e-commerce, where undetected fraudulent transactions can lead to significant economic losses. In this study, we systematically compare the performance of four supervised learning models—Logistic Regression, Random Forest, Light Gradient Boosting Machine (LightGBM), and a Gated Recurrent Unit (GRU) network—on a large-scale, highly imbalanced online transaction dataset. While ensemble methods such as Random Forest and LightGBM demonstrated superior performance in both overall and class-specific metrics, Logistic Regression offered a reliable and interpretable baseline. The GRU model showed strong recall for the minority fraud class, though at the cost of precision, highlighting a trade-off relevant for real-world deployment. Our evaluation emphasizes not only weighted averages but also per-class precision, recall, and F1-scores, providing a nuanced view of each model’s effectiveness in detecting rare but consequential fraudulent activity. The findings underscore the importance of choosing models based on the specific risk tolerance and operational needs of fraud detection systems.

Copyright

© 2025 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  - Chao Wang
AU  - Chuanhao Nie
AU  - Yunbo Liu
PY  - 2025
DA  - 2025/09/17
TI  - Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data
BT  - Proceedings of the 2025 3rd International Academic Conference on Management Innovation and Economic Development (MIED 2025)
PB  - Atlantis Press
SP  - 613
EP  - 624
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-835-6_65
DO  - 10.2991/978-94-6463-835-6_65
ID  - Wang2025
ER  -