ONLINE PAYMENT FRAUD DETECTION USING MACHINE LEARNING

ONLINE PAYMENT FRAUD DETECTION USING MACHINE LEARNING

Abstract

Online payment fraud has become a significant concern in the digital age. Machine learning (ML) can play a crucial role in detecting and preventing such fraudulent activities. This approach involves training ML algorithms on historical data to identify patterns and anomalies indicative of fraudulent transactions. By leveraging features such as transaction amount, location, and user behavior, ML models can accurately classify transactions as legitimate or fraudulent. Techniques like supervised learning, clustering, and neural networks can be employed to develop effective fraud detection systems. The integration of ML in online payment systems can help reduce false positives, improve detection accuracy, and enhance overall security. By staying one step ahead of fraudsters, ML-powered fraud detection can protect consumers and businesses from financial losses.

Keywords

Online payment fraud, Machine learning, Fraud detection, Payment security, Transaction analysis, Anomaly detection, Supervised learning, Clustering, Neural networks, Cybersecurity

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