Detection of fraudulent users in P2P financial market
This work addresses fraud detection for a specific Fintech company, HC Financial, but is incremental as it uses existing methods.
The paper tackled fraud detection in P2P financial markets by applying random forest and gradient boosting decision trees, achieving effective filtering of fraudulent users.
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.