Abstract:

In today’s digital landscape, online payment fraud has emerged as one of the primary challenges for e-commerce businesses, with fraudsters continuously exploiting system vulnerabilities. Online payment fraud remains a growing concern in the e-commerce industry, with fraudsters continuously developing new methods to exploit vulnerabilities. This paper proposes a highly effective machine learning-based framework to predict and prevent fraudulent transactions. The framework uses advanced algorithms, including K-Nearest Neighbors, Decision Trees, Random Forest, Gradient Boosting, Support Vector Machines, and Neural Networks. After testing the models on three different datasets, Gradient Boosting emerged as the top performer, achieving 99.7% accuracy in fraud detection. This algorithm’s exceptional performance and adaptability to various fraud types make it an ideal choice for e-commerce platforms. The proposed framework provides businesses with the ability to identify and block fraudulent activities before they impact transactions, thereby strengthening their defenses. Traditional fraud detection systems often fail to detect sophisticated fraud patterns, but machine learning models like Gradient Boosting offer a data-driven approach that analyzes vast amounts of transaction data to identify emerging threats, ensuring a safer online shopping experience for customers.

Description:

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