What question did this study set out to answer?

To compare different machine learning algorithms to accurately detect credit card fraud.

May 7, 2026Open Access

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

AAAdedolapo Oluwadamilare AdedokunTeesside University GOGodwin Emmanuel OyedokunCollege of Accounting AEAyoola Abimbola ESOLagos State University

Key Points

To compare different machine learning algorithms to accurately detect credit card fraud.
Analyzed a dataset of 555,719 payments with 2,145 scams
Used SMOTE to balance data
Compared four algorithms: ANN, DT, KNN, XGBoost
Checked model success using accuracy, precision, recall, F1-score, confusion matrix
KNN showed the highest accuracy at 98.39% and precision of 96.90%
Recall was 99.99% with an F1-score of 98.42%
Risky spending patterns were identified during late night hours
The model suggests improved security measures and consumer education

Abstract

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 'shoppingₙet' and 'groceryₚause' 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 twofactor authentication during risky times, and educate consumers. The study suggests incorporating data from multiple countries could improve the model's predictive power and generalizability.

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