Detection of fraudulent users in P2P financial market

TL;DR

This paper presents a machine learning approach using random forest and gradient boosting decision trees to detect fraudulent users in the Chinese P2P financial market, aiming to reduce financial losses due to fraud.

Contribution

It introduces a specific fraud detection methodology tailored for the P2P market, emphasizing feature selection and model tuning for improved accuracy.

Findings

Effective filtering of fraudulent users achieved

Model tuning improves detection accuracy

Significant reduction in financial losses

Abstract

Financial fraud detection is one of the core technological assets of Fintech companies. It saves tens of millions of money fro m Chinese Fintech companies since the bad loan rate is more than 10%. HC Financial Service Group is the 3rd largest company in the Chinese P2P financial market. In this paper we illustrate how we tackle the fraud detection problem at HC Financial. We utilize two powerful workhorses in the machine learning field - random forest and gradient boosting decision tree to detect fraudulent users . We demonstrate that by carefully select features and tune model parameters , we could effectively filter out fraudulent users in the P2P market.