# Credit card fraud detection using machine learning: A survey

arXiv: https://arxiv.org/abs/2010.06479 · HTML: https://mlsanity.com/paper/2010.06479/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Evaluating categorical encoding methods on a real credit card fraud detection database](https://mlsanity.com/paper/2112.12024/) (2021)
- [SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection](https://mlsanity.com/paper/2606.08146/) (2026)
- [Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset](https://mlsanity.com/paper/2206.13152/) (2022)
- [Credit Card Fraud Detection Using RoFormer Model With Relative Distance Rotating Encoding](https://mlsanity.com/paper/2507.09385/) (2025)
- [Improving Credit Card Fraud Detection with an Optimized Explainable Boosting Machine](https://mlsanity.com/paper/2602.06955/) (2026)
- [When Similarity Is Interaction-Driven: Quantum Kernels for Regime-Sensitive Learning](https://mlsanity.com/paper/2608.24631/) (2026)
- [Usefulness of Quantile-Aware Diffusion Modeling for Highly Imbalanced Tabular Data](https://mlsanity.com/paper/2610.05825/) (2026)

## Abstract (arXiv)

Credit card fraud has emerged as major problem in the electronic payment sector. In this survey, we study data-driven credit card fraud detection particularities and several machine learning methods to address each of its intricate challenges with the goal to identify fraudulent transactions that have been issued illegitimately on behalf of the rightful card owner. In particular, we first characterize a typical credit card detection task: the dataset and its attributes, the metric choice along with some methods to handle such unbalanced datasets. These questions are the entry point of every credit card fraud detection problem. Then we focus on dataset shift (sometimes called concept drift), which refers to the fact that the underlying distribution generating the dataset evolves over times: For example, card holders may change their buying habits over seasons and fraudsters may adapt their strategies. This phenomenon may hinder the usage of machine learning methods for real world datasets such as credit card transactions datasets. Afterwards we highlights different approaches used in order to capture the sequential properties of credit card transactions. These approaches range from feature engineering techniques (transactions aggregations for example) to proper sequence modeling methods such as recurrent neural networks (LSTM) or graphical models (hidden markov models).