A Comparative Analysis of Supervised Machine Learning Algorithms for Credit Card Fraud Detection

A Comparative Analysis of Supervised Machine Learning Algorithms for Credit Card Fraud Detection | Zenodo Skip to main Communities My dashboard Log in Sign up Zenodo is currently experiencing slowness and intermittent outages due to heavy automated traffic from bots and AI crawlers. We are aware of the problem, and our team is focused on stabilizing the service. Thank you for your patience. Published October 5, 2025 | Version v1 Journal article Open A Comparative Analysis of Supervised Machine Learning Algorithms for Credit Card Fraud Detection Authors/Creators Adedokun, Adedolapo Oluwadamilare 1 Oyedokun, Godwin Emmanuel 2 Eso, Ayoola Abimbola 3 Show affiliations 1.

School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, dolapodamilare@gmail.com || +447747250835 2.

Legal Scholar and Professor of Accounting & Financial Development, Department of Management and Accounting, Faculty of Management and Social Sciences 3.

Department of Finance, Lagos State University of Science and Technology, Lagos, Nigeria ayoeso@hotmail.com; +234 802 291 3114 Description The rapid growth in digital payments has led to a rise in credit card scams around the world, causing consumers, stores and banks to lose money. This paper addresses this problem by building and testing a machine learning system to quickly and accurately detect credit card scams. This study uses a set of 555,719 payments from Kaggle with 2,145 scams. After using SMOTE to prepare the data and correct unequal clusters, the study performs detailed data analysis. The analysis reveals key risk signals, such as 'shopping_net' and 'grocery_pause' spend peaking during late night (10:00-11:59 pm) scams and affecting card users aged 35-40 the most. It also suggests that gender does not change the risk of fraud. The main part of this study is to use, adjust, and compare four machine learning methods: Artificial Neural Network (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), and XGBoost. The success of the model is checked with accuracy, precision, recall, F1-score and confusion matrix. The K-Nearest Neighbor (KNN) method works best, with a test accuracy of 98.39%, precision of 96.90%, recall of 99.99% and F1-score of 98.42% making it the best in distinguishing scams from real payments. The study reaches its goal by detecting risky payment habits and building a very accurate prediction model. It states that KNN is a good choice for live scam detection systems. It suggests banks use this model, add better security like two factor authentication during risky times, and educate consumers. The study suggests incorporating data from multiple countries could improve the model's predictive power and generalizability. Files JFAFI Volume 10, Issue 2, July - December, 2025_10-end.pdf Files (1.6 MB) Name Size Download all JFAFI Volume 10, Issue 2, July - December, 2025_10-end.pdf md5:420ffa6926746802bf2292893f8bd044 1.6 MB Preview Download 60 Views 87 Downloads Show more details All versions This version Views Total views 60 60 Downloads Total downloads 87 87 Data volume Total data volume 163.7 MB 163.7 MB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Keywords and subjects Keywords Credit card fraud Fraud detection Machine learning Supervised learning Classification algorithms Details DOI DOI Badge DOI 10.5281/zenodo.20041664 Markdown [](https://doi.org/10.5281/zenodo.20041664) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.20041664.svg :target: https://doi.org/10.5281/zenodo.20041664 HTML <a href="https://doi.org/10.5281/zenodo.20041664"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.20041664.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.20041664.svg Target URL https://doi.org/10.5281/zenodo.20041664 Resource type Journal article Publisher Association of Forensic accounting Researchers (AFAR) Published in Journal of Forensic accounting & Fraud Investigation (JFAFI), 10(1, July-December, 2025), 1-30, ISSN: 2659-1138, 2025. Languages English Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more Citation Export Technical metadata Created May 5, 2026 Modified May 11, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. Find out more on how we use cookies Accept all cookies Accept only essential cookies

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