Return-Fraud Detection and Reverse Logistics Optimisation for Indian E-commerce

Objective: To detect fraudulent product returns and optimise reverse logistics operations in Indian e-commerce retail using data-driven approaches.

Indian e-commerce platforms face significant challenges with product returns, including fraudulent claims and inefficient reverse logistics, impacting profitability and customer satisfaction. High volumes of COD orders and consumer rights make return management complex and prone to abuse.

The project applies statistical analysis and machine learning to identify patterns of return fraud, and uses optimisation techniques to improve reverse logistics efficiency. Teams will analyse transaction data, customer profiles, and logistics routes, leveraging real-world datasets and company interviews.

Deliverables include a fraud detection model, reverse logistics optimisation simulation, actionable dashboards, and a business case report with recommendations. Analysis will focus on quantifying fraud risk, cost saving opportunities, and operational impact.

The outcome enables e-commerce managers to reduce fraudulent returns, streamline logistics costs, improve customer experience, and support decisions on process redesign and technology adoption.