Fraud Detection in E-commerce: An Application of Machine Learning

Fraud Detection in E-commerce: An Application of Machine Learning

Submitted by:
Rahul Sharma
MBA (2023–2025), Galgotias University

Under the Guidance of:
Ms.Anamita Guha

1. Executive Summary
This report explores the role of machine learning in detecting and preventing fraudulent activities within the e-commerce domain. As online retail expands, so too does the sophistication of fraud tactics. Traditional detection systems are proving insufficient in this evolving environment. Machine learning (ML), with its ability to learn from data patterns and identify anomalies in real time, offers a promising solution.

The study utilizes a mixed-method research approach, combining survey data from 100 consumers and expert interviews to evaluate the effectiveness, challenges, and ethical considerations of implementing ML-based fraud detection systems. Findings show that ML significantly enhances detection accuracy and consumer trust but requires careful implementation and transparent data handling.