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

Chao Wang, Chuanhao Nie and Yunbo Liu ()
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Chao Wang: Rice University, Department of Computer Science
Chuanhao Nie: Georgia Institute of Technology, College of Computing
Yunbo Liu: Duke University, Department of Electrical and Computer Engineering

A chapter in Proceedings of the 2025 3rd International Academic Conference on Management Innovation and Economic Development (MIED 2025), 2025, pp 613-624 from Springer

Abstract: 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.

Keywords: Supervised Learning; Machine Learning & Deep Learning; Class Imbalance; Online Transactions; Fraud Detection (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-835-6_65

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