Detection of Fraudulent Sellers in Online Marketplaces using Support Vector Machine Approach

TL;DR

This paper proposes a machine learning framework using Support Vector Machine to detect fraudulent sellers in online marketplaces by analyzing historical transaction data to identify suspicious behaviors.

Contribution

It introduces a novel SVM-based approach specifically designed for identifying fraudulent sellers, addressing a gap in existing fraud detection methods.

Findings

Effective detection of fraudulent sellers demonstrated

High accuracy achieved in identifying suspicious behaviors

Framework can be integrated into existing marketplace systems

Abstract

The e-commerce share in the global retail spend is showing a steady increase over the years indicating an evident shift of consumer attention from bricks and mortar to clicks in retail sector. In recent years, online marketplaces have become one of the key contributors to this growth. As the business model matures, the number and types of frauds getting reported in the area is also growing on a daily basis. Fraudulent e-commerce buyers and their transactions are being studied in detail and multiple strategies to control and prevent them are discussed. Another area of fraud happening in marketplaces are on the seller side and is called merchant fraud. Goods/services offered and sold at cheap rates, but never shipped is a simple example of this type of fraud. This paper attempts to suggest a framework to detect such fraudulent sellers with the help of machine learning techniques. The model leverages the historic data from the marketplace and detect any possible fraudulent behaviours from sellers and alert to the marketplace.