IIARD International Institute of Academic Research and Development

WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY (WJIMT )

E-ISSN 2504-4766
P-ISSN 2682-5910
VOL. 9 NO. 4 2025
DOI: 10.56201/wjimt.v9.no4.2025.pg74.85

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Comparative Analysis of K-Nearest Neighbors, Naive Bayes, and Decision Tree Algorithms for Credit Card Fraud Detection

McKelly Tamunotena Pepple, Efiyeseimokumo Sample Ikeremo

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Abstract

This study is based on the comparative analysis of K-Nearest Neighbors (KNN), Naive Bays, and Decision Trees based on the machine learning field and applied in the fraud detection of credit cards. The algorithms are evaluated and compared based on computational efficiency, classification metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The results reveal that KNN has the highest overall accuracy, but it has low recall in the detection of fraudulent activity; Naive Bayes has the highest recall, but suffers from a high false positive rate and low accuracy; Decision Tree is the most stable algorithm and maintains a competitive accuracy of 95.3% with reasonable trade-offs for precision and recall. In credit card fraud detection, particularly for imbalanced datasets, this study recommends a mechanism for balancing two conflicting perspectives: computational weight and accuracy, with Decision Tree being the most effective among the three algorithms considered.

keywords:

Credit Card; Fraud Detection; Machine Learning; Algorithms; imbalance

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