Machine Learning for Credit Card Fraud Detection: A Comparative Study of Algorithms

Raji, Rahul (2024) Machine Learning for Credit Card Fraud Detection: A Comparative Study of Algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

This paper applies machine learning algorithms for the detection of credit card fraud by focusing on the identification of fraudulent transactions and their reduction. The imbalanced dataset was used with oversampling SMOTE and feature scaling techniques in order to improve the model's performance. Supervised models such as Random Forest, Support Vector Machines (SVM), and Logistic Regression were also evaluated for accuracy, precision, recall, and F1-score. In addition, the unsupervised methods like Isolation Forest were tested for anomaly detection. It is observed that Random Forest outperformed others by having a higher accuracy and feature importance analysis, while SVM showed excellent precision for binary classification. The findings thus emphasize the comparative approach to improving fraud detection systems.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Prior, Michael UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security H Social Sciences > HG Finance > Credit. Debt. Loans. Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	28 Jul 2025 10:06
Last Modified:	28 Jul 2025 10:06
URI:	https://norma.ncirl.ie/id/eprint/8248

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