Enhancing Credit Card Transaction Security Using Support Vector Machines and Feature Engineering Techniques

Mohd Asad Jawaid; Sayyed Ameen Naqvi; Mohd Safdar; Mohd Haroon

doi:10.55524/ijircst.2025.13.3.14

Abstract

The quick spurt in online transactions has also brought with it a parallel surge in fraud. With digital payments, the scope for fraud remains very high. Credit card fraud, among others, can cause heavy losses to customers and erode the confidence of consumers in Internet transactions. Furthermore, the detection of fraudsters in the online world poses big challenges. For one thing, there is an imbalance in data: fraud transactions are very few compared to genuine transactions. In this research paper, we intend to classify fraud through a supervised machine learning method. SVM is utilized for classification purposes. Based on the available dataset, we performed data analysis to obtain valuable information concerning fraud detection. To solve the problem of data imbalance, we first preprocessed the raw data by randomly choosing some legitimate transactions and normalizing the features. We also used feature selection and scaling methods to improve the accuracy of the model. After training the SVM on the sanitized dataset, we tested the model using performance measures like accuracy, precision, recall, and F1-score. These measures are especially important when working with skewed data. The findings show that the SVM model can detect fraudulent transactions with high precision and a good rate of recall. This shows that it can assist in reducing false alarms while being able to detect most fraud cases. In addition, we touch upon the tradeoff between false negatives and false positives because both pose very significant implications within financial institutions.

rity.

Keywords

Credit Card Fraud, Support Vector Machine, Feature Engineering, Anomaly Detection, Financial Secu

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Cites this article as

M. A. Jawaid, S. A. Naqvi, M. Safdar, M. Haroon, "Enhancing Credit Card Transaction Security Using Support Vector Machines and Feature Engineering Techniques", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-13, Issue-3, Page No-82-88, 2025. Available from: https://doi.org/10.55524/ijircst.2025.13.3.14

Corresponding Author

Mohd Asad Jawaid

B.Tech Scholar, Department of Computer Science & Engineering, Integral University, Lucknow, India

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