# Detecting problematic transactions in a consumer-to-consumer e-commerce network

## TL;DR

This study uses network analysis and machine learning to detect fraudulent users in a consumer-to-consumer online marketplace, achieving high accuracy regardless of transaction type.

## Contribution

It introduces a network-based approach with local network indices and random forest classifiers to identify fraud in online transactions, outperforming traditional profile-based methods.

## Key findings

- High classification accuracy in detecting fraudulent users
- Performance consistent across different types of problematic transactions
- Network indices effectively distinguish fraud from normal activity

## Abstract

Providers of online marketplaces are constantly combatting against problematic transactions, such as selling illegal items and posting fictive items, exercised by some of their users. A typical approach to detect fraud activity has been to analyze registered user profiles, user's behavior, and texts attached to individual transactions and the user. However, this traditional approach may be limited because malicious users can easily conceal their information. Given this background, network indices have been exploited for detecting frauds in various online transaction platforms. In the present study, we analyzed networks of users of an online consumer-to-consumer marketplace in which a seller and the corresponding buyer of a transaction are connected by a directed edge. We constructed egocentric networks of each of several hundreds of fraudulent users and those of a similar number of normal users. We calculated eight local network indices based on up to connectivity between the neighbors of the focal node. Based on the present descriptive analysis of these network indices, we fed twelve features that we constructed from the eight network indices to random forest classifiers with the aim of distinguishing between normal users and fraudulent users engaged in each one of the four types of problematic transactions. We found that the classifier accurately distinguished the fraudulent users from normal users and that the classification performance did not depend on the type of problematic transaction.