Machine Learning-Driven Risk Scoring Systems for Improved Fraud Prevention in E-Commerce

Abstract

Fraud prevention in e-commerce is a critical aspect of ensuring secure transactions and maintaining consumer trust. Traditional rule-based fraud detection systems often fail to adapt to the evolving tactics of fraudsters. Machine learning (ML) offers a robust alternative by enabling the detection of hidden patterns and anomalies in transaction data. This paper explores the application of machine learning-driven risk scoring systems for fraud prevention in e-commerce. We examine various algorithms such as decision trees, random forests, and neural networks to assess their effectiveness in predicting and mitigating fraudulent activities. The study presents an approach where transactions are scored based on risk levels, allowing for a more efficient and adaptive fraud detection system. Results indicate that ML models significantly improve fraud detection rates, reduce false positives, and enhance system efficiency over traditional methods.