Enhancing Payment Security Using Machine Learning

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Shaik Sharon Raju; Ms. E. Vinothini; S. Bharath Kumar Reddy; M.S. Roobini

Enhancing Payment Security Using Machine Learning

Authors: Shaik Sharon Raju, Ms. E. Vinothini, S. Bharath Kumar Reddy, M.S. Roobini

Unique Paper ID: 172993
Volume: 11
Issue: 9
PageNo: 1518-1523

Keywords: Fraud detection BankSim dataset Synthetic Minority Oversampling Technique (SMOTE) KNN XGBoost ensemble learning.

Abstract:
Detecting fraudulent financial transactions represents an essential banking system priority dedicated to safeguarding customer trust and decreasing financial losses. This research analyzes fraud detection through the BankSim synthetic dataset that contains transaction information including amount details alongside demographic traits and merchant identifiers. Because the dataset featured a significant unbalance between legitimate and fraudulent transaction records SMOTE became indispensable for addressing this class imbalance challenge. The research deployed KNN Random Forest and XGBoost classification algorithms and subsequently developed an ensemble system for superior predictive accuracy. Experimentation resulted in KNN alongside XGBoost and the ensemble classifier producing 99% accurate results maintaining Random Forest at 98% accuracy. The study confirms how modern machine learning approaches together with ensemble learning deliver remarkable success in detecting fraudulent incidents. This research delivers essential understanding of building dependable automated analysis tools for detecting financial transaction fraud.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 9
PageNo: 1518-1523

Enhancing Payment Security Using Machine Learning

Available:https://ijirt.org/article?manuscript=172993

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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