Online Payment Fraud Detection

Abstract

This research explores innovative approaches to detecting fraudulent transactions by leveraging advanced machine learning algorithms and data analytics techniques. By analyzing real-world transaction datasets, the study aims to identify behavioral patterns and anomalies indicative of fraud. The proposed methodology incorporates supervised learning models, such as Random Forest and Gradient Boosting, alongside feature engineering to enhance detection accuracy. Furthermore, real-time detection mechanisms are examined to mitigate risks during online payment processing. Experimental results demonstrate the effectiveness of the proposed system in improving fraud detection rates while minimizing false positives. This research highlights the potential of intelligent systems to strengthen online payment security and provides a foundation for future advancements in combating financial fraud.