Vol. 6 No. 1 (2026): January−June 2026
Articles

A Comparative Empirical Study of Machine Learning Methods for Credit Card Fraud Detection: Gradient Boosting, Deep Tabular Learning, and Classical Baselines Under Extreme Class Imbalance

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  • Jianuo Wang,
  • Yixin Zheng,
  • Xinyu Zhang
Jianuo Wang
Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA
Yixin Zheng
Information Science, University of Michigan, Ann Arbor, MI, USA
Xinyu Zhang
Applied Computing, University of Toronto, Toronto, ON, Canada

Published 2026-06-18

Keywords

  • fraud detection,
  • class imbalance,
  • gradient boosting,
  • precision-recall evaluation

How to Cite

Wang, J., Zheng, Y., & Zhang, X. (2026). A Comparative Empirical Study of Machine Learning Methods for Credit Card Fraud Detection: Gradient Boosting, Deep Tabular Learning, and Classical Baselines Under Extreme Class Imbalance. Spectrum of Research, 6(1). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/55

Copyright (c) 2026 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Credit card fraud detection poses a canonical extreme class-imbalance problem, in which fraudulent transactions account for well under one percent of all activity. Although gradient-boosted decision trees are widely regarded as the default choice for tabular prediction, recent deep tabular architectures have been advanced as competitive alternatives, and the question of which family offers the better operational trade-off on highly imbalanced financial data remains open. This study presents a controlled empirical comparison of nine methods spanning three families—gradient boosting, deep tabular learning, and classical supervised and unsupervised baselines—on the widely used ULB credit card transaction dataset of 284,807 records with a 0.172% fraud rate. All methods are tuned under an identical search budget and assessed with metrics suited to imbalance, taking the area under the precision-recall curve as the primary criterion. Gradient-boosting methods occupy the top of the ranking, with CatBoost reaching an AUPRC of 0.8631, while the strongest deep tabular method trails by a moderate margin of 0.0284. Resampling yields limited and model-dependent benefit, aiding linear models substantially yet leaving strong learners largely unaffected. A histogram-based boosting method matches near-best detection at a fraction of the training cost of the neural alternatives. The findings give practical guidance on method and metric selection for imbalanced fraud detection.

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