mlsanityData to 7 Oct 2026

Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments

arXiv 2312.13896 ↗First posted 21 Dec 2023cs.LG q-fin.STcited by 4

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Abstract (arXiv)

This study explores the application of anomaly detection (AD) methods in imbalanced learning tasks, focusing on fraud detection using real online credit card payment data. We assess the performance of several recent AD methods and compare their effectiveness against standard supervised learning methods. Offering evidence of distribution shift within our dataset, we analyze its impact on the tested models' performances. Our findings reveal that LightGBM exhibits significantly superior performance across all evaluated metrics but suffers more from distribution shifts than AD methods. Furthermore, our investigation reveals that LightGBM also captures the majority of frauds detected by AD methods. This observation challenges the potential benefits of ensemble methods to combine supervised, and AD approaches to enhance performance. In summary, this research provides practical insights into the utility of these techniques in real-world scenarios, showing LightGBM's superiority in fraud detection while highlighting challenges related to distribution shifts.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 43 references that are in the map. First posted on arXiv 21 Dec 2023.

Builds on

20 of 43 references.

References · 20

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  3. 2021Revisiting Deep Learning Models for Tabular Datacited by 725
  4. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  5. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  6. 2021SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Trainingcited by 310
  7. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  8. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
  9. 2020Classification-Based Anomaly Detection for General Datacited by 210
  10. 2020Geometric Dataset Distances via Optimal Transportcited by 157
  11. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  12. 2021Well-tuned Simple Nets Excel on Tabular Datasetscited by 151
8 more
  1. 2020COPOD: Copula-Based Outlier Detectioncited by 143
  2. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 98
  3. 2018Extended Isolation Forestcited by 78
  4. 2020Anomaly Detection With Conditional Variational Autoencoderscited by 46
  5. 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
  6. 2023Beyond Individual Input for Deep Anomaly Detection on Tabular Datacited by 14
  7. 2022TracInAD: Measuring Influence for Anomaly Detectioncited by 8
  8. 2021Evaluating categorical encoding methods on a real credit card fraud detection databasecited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 1.

4 papers.

  1. 2024Retrieval Augmented Deep Anomaly Detection for Tabular Datacited by 6
  2. 2024Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic Systemscited by 4
  3. 2024Anomaly Detection from a Tensor Train Perspectivecited by 0
  4. 2026Ranked Activation Shift for Post-Hoc Out-of-Distribution Detectioncited by 0