Financial fraud detection is one of the core technological assets of Fintech companies. It saves tens of millions of money from 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.
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