International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 18, No. 2, 8 Apr. 2026
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Feature Selection for Fraud Detection: Improving Machine Learning Capabilities on Portable Wallet
PDF (1231KB), PP.55-72
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Author(s)
Gurleen Kaur
1
Mandeep Kaur
2
Punam Rattan
3
Mukesh Kumar
4,5,*
Index Terms
Fraud Detection, Feature Selection, Random Forest, LASSO Regression, Recursive Feature Elimination, Mutual Information
Abstract
The rapid growth of mobile wallet usage has led to a sharp increase in fraudulent transactions, making fraud detection in portable wallets a pressing concern. Accurately detecting fraud is difficult because transaction data is complicated and unbalanced. Conventional rule-based systems are less flexible and frequently provide large false positive rates along with poor accuracy. Effective feature selection is crucial to the performance of Machine Learning (ML) models, notwithstanding their increased detection rates. Redundancy and noise are introduced by high-dimensional data, which lowers model performance and raises computing costs. The advantages of hybrid feature selection are frequently overlooked in current research, particularly when it comes to portable wallet fraud detection. By combining Random Forest Importance, LASSO Regression, Recursive Feature Elimination (RFE), and Mutual Information (MI) with resampling to solve class imbalance, this study fills that gap. Our approach provides a more reliable and effective solution for safe portable wallet fraud detection by removing superfluous features, increasing accuracy, and reducing computing cost. The model becomes faster and more effective when superfluous characteristics are eliminated because this reduces the computational effort. By concentrating just on the most instructive data, it increases accuracy. By addressing class imbalance and combining several selection strategies, the hybrid approach guarantees robustness. All things considered, this leads to a scalable and safe fraud detection system for transactions using mobile wallets. Our results show that a successful feature selection approach improves fraud detection accuracy, which in turn improves operational effectiveness and financial security.
Cite This Paper
Gurleen Kaur, Mandeep Kaur, Punam Rattan, Mukesh Kumar, "Feature Selection for Fraud Detection: Improving Machine Learning Capabilities on Portable Wallet", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.2, pp. 55-72, 2026. DOI:10.5815/ijieeb.2026.02.04
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