A Machine Learning-Based Fraud Prevention Model for Improving Customers’ Trust in E-Commerce

Abstract

The growth of e-commerce has led to significant challenges regarding fraud, resulting in a decline in customer trust and confidence in online transactions. This research proposes a comprehensive Fraud Prevention Model aimed at enhancing customer trust and security within e-commerce platforms by integrating advanced machine learning (ML) techniques, an Address Verification System (AVS), and Two-Factor Authentication (2FA). The model leverages Convolutional Neural Network - Long Short-Term Memory Network (CNN-LSTM) and Random Forest techniques to capture the complexities and temporal dependencies of e-commerce transaction data. The AVS component of the system verifies transaction legitimacy by comparing billing addresses with credit card records, and the implementation of 2FA adds an extra layer of security. The system's effectiveness was evaluated through rigorous testing using a dataset of transaction records. The results indicate that the combined approach of machine learning, AVS, and 2FA significantly enhances the detection of fraudulent transactions and improves overall customer trust in e-commerce platforms.