# Credit Card Fraud Detection using Machine Learning: A Study

arXiv: https://arxiv.org/abs/2108.10005 · HTML: https://mlsanity.com/paper/2108.10005/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 49 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre](https://mlsanity.com/paper/2107.13502/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Survey on Generative Diffusion Models for Structured Data](https://mlsanity.com/paper/2306.04139/) (2023)
- [A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms](https://mlsanity.com/paper/2208.13251/) (2022)
- [A Privacy-Preserving Outsourced Data Model in Cloud Environment](https://mlsanity.com/paper/2211.13542/) (2022)
- [Foe for Fraud: Transferable Adversarial Attacks in Credit Card Fraud Detection](https://mlsanity.com/paper/2508.14699/) (2025)
- [A smart resource management mechanism with trust access control for cloud computing environment](https://mlsanity.com/paper/2212.05319/) (2022)
- [A Machine Learning Driven Website Platform and Browser Extension for Real-time Scoring and Fraud Detection for Website Legitimacy Verification and Consumer Protection](https://mlsanity.com/paper/2411.00368/) (2024)

## Abstract (arXiv)

As the world is rapidly moving towards digitization and money transactions are becoming cashless, the use of credit cards has rapidly increased. The fraud activities associated with it have also been increasing which leads to a huge loss to the financial institutions. Therefore, we need to analyze and detect the fraudulent transaction from the non-fraudulent ones. In this paper, we present a comprehensive review of various methods used to detect credit card fraud. These methodologies include Hidden Markov Model, Decision Trees, Logistic Regression, Support Vector Machines (SVM), Genetic algorithm, Neural Networks, Random Forests, Bayesian Belief Network. A comprehensive analysis of various techniques is presented. We conclude the paper with the pros and cons of the same as stated in the respective papers.