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Enhancing Payment Security Using Machine Learning

  • Unique Paper ID: 172993
  • Volume: 11
  • Issue: 9
  • PageNo: 1518-1523
  • 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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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 9
  • PageNo: 1518-1523

Enhancing Payment Security Using Machine Learning

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