Online Payment Fraud Detection Using Machine ...

Online Payment Fraud Detection Using Machine Learning Techniques

Author Name : Ch. Dilip Kumar

ABSTRACT The rapid expansion of online transactions has led to a significant rise in fraudulent activities, posing substantial financial threats to both individuals and organizations. Traditional rule-based systems for detecting fraud have become inadequate due to their limited ability to adapt to the evolving and sophisticated nature of fraud. In response, machine learning (ML) has emerged as a powerful and dynamic solution for fraud detection, leveraging historical transaction data to identify anomalies and potential fraud in real time. This paper provides a comprehensive analysis of various machine learning techniques applied to online fraud detection. We examine supervised learning models, including logistic regression, decision trees, and random forests, as well as more advanced methods such as neural networks and ensemble techniques. The study underscores the critical role of feature engineering in boosting model performance, with key features like transaction amount, time, location, and user behavior patterns highlighted. Additionally, we address the challenges posed by imbalanced datasets—common in fraud detection—and explore strategies such as resampling techniques and anomaly detection models to mitigate their effects.