COMPARATIVE EXAMINATION OF DEBIT CARD FRAUD DETECTION METHODS EMPLOYING MACHINE LEARNING INTELLIGENCE APPROACHES - Volume 30 - Issue 4 - 2025

Aug 5, 2026

This study compares supervised machine-learning approaches for detecting fraudulent debit card transactions, using models such as Extreme Gradient Boosting, Random Forest, KNN, Logistic Regression, SVM, and Decision Trees. A debit card dataset comprising 248,807 records and 30 variable fields supports data-mining and algorithm evaluation. The objective is to identify outliers and atypical user behavior patterns. Results indicate the combined technique model outperforms separate systems in fraud-related classification tasks.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

I. Introduction

Fraud detection challenges

Fraud types in debit card transactions

Methods and evaluation approach

Dataset description

Models used for classification

R. Abdulyekeen, F. O. Echobu Volume 30, Issue (4), 2025 COMPARATIVE EXAMINATION OF DEBIT CARD FRAUD DETECTION METHODS EMPLOYING MACHINE LEARNING INTELLIGENCE APPROACHES R. Abdulyekeen (1) F. O. Echobu (2) A. Zakariyya (3) Received: 21/01/2025 Revised: 26/02/2025 Accepted: 27/02/2025 © 2025 University of Science and Technology, Aden, Yemen. This article can be distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 2025 © ـــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ 1 Department of Computer Science, Federal University, Dutsin-Ma, Nigeria 2 Department of Information Technology, Federal University Dutsin-ma, Nigeria 3 Department of Software Engineering, Federal University Dutsin-Ma, Nigeria *Corresponding Author’s Email: [rabdulyekeen@fudutsinma.edu.ng](rabdulyekeen@fudutsinma.edu.ng) 68 [https://doi.org/10.20428/jst.v30i4.2756](https://doi.org/10.20428/jst.v30i4.2756) R. Abdulyekeen, F. O. Echobu Volume 30, Issue (4), 2025 Comparative Examination of Debit Card Fraud Detection Methods Employing Machine Learning Intelligence Approaches R. Abdulyekeen Department of Computer Science, Federal University, Dutsin-Ma, Nigeria, F. O. Echobu Department of Information Technology, Federal University, Dutsin-ma, Nigeria A. Zakariyya Department of Software Engineering, Federal University Dutsin-Ma, Nigeria Abstract—For this study, we look at how to spot fake debit card transactions using supervised intelligent expert systems techniques like Extreme Gradient Boosting, Random Forest, KNN, Logistic Regression, SVM, and Decision Trees to solve classification problems. A debit card dataset with two parts, 30 variable fields, and a total of 248,807 records was used to do a thorough evaluation of data mining and machine learning algorithms. This dataset helped with the investigation of fraud detection. After finishing the basic structure, I can say with certainty that the combined technique model performs much better than a separate system that uses k-means, XGBoost, SVM, and logistic regression to find outliers. Given that the main objective is to detect fraudulent activities in a debit card dataset, its effectiveness is measured by how frequently it identifies outliers or atypical user behaviour patterns. Keywords— Artificial Intelligence, Machine Learning, Dataset, SVM, And Logistic Regression. I. INTRODUCTION Debit card fraud involves the unauthorised use of a bank account by an individual apart from the rightful account holder. To mitigate this kind of abuse, appropriate strategies can be implemented, and understanding the patterns associated with fraudulent behavior can aid in decreasing the chances of future incidents and protecting against repeat occurrences [1] . In other terms, debit card fraud occurs when someone uses another person's debit card without the knowledge or consent of the owner or the card-issuing institution. Fraud discovery consists of observing user behaviour to identify, predict, or prevent negative activities, such as fraud, unauthorised access, and defaulting on payments. Domains such as machine learning and data science, which can create automated approaches, must focus on this vital matter. The intricacy of fraud detection arises from issues like class imbalance, where valid transactions significantly exceed fraudulent ones, and the shifting transaction trends that evolve over time. Data mining refers to extract and identify useful information from large sets of data [2] . Fundamentally, data mining is the process of scrutinising large collections offacts to gain vital insights that guide future choices. Once we establish an effective model, we can use it for forecasting by assigning categories to incoming data. In the realm of fraud detection, the process entails categorising transaction infor

FAQs

Which machine learning methods are used for debit card fraud detection?

The study evaluates supervised models including Extreme Gradient Boosting, Random Forest, KNN, Logistic Regression, SVM, and Decision Trees for classification.

What dataset characteristics support the comparison in the study?

The evaluation uses a debit card dataset with 30 variable fields and 248,807 total records to support fraud detection and algorithm assessment.

How is model effectiveness measured in detecting fraud?

Effectiveness is measured by how frequently the model identifies outliers or atypical user behavior patterns associated with fraudulent activity.

Aug 5, 2026

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