The Research About Credit Risk Evaluation Of Borrower From P2P Lending

It has been nine years since the Peer to Peer Lending come into Chinese market.Peer to Peer Lending in China has much developed during this period and has its own features which different from those leading Peer to Peer lending platform in foreign country.Also,there are lots of problems exist in this industry,especially the credit risk related affairs.Due to the undeveloped credit environment and inadequate credit collection,Peer to Peer Lending platform in our country unable to implement the credit risk assessment and loan pricing in digital ways.So it cannot relief the burden of borrowers,and borrowing from Peer to Peer Lending platform even more expensive than the traditional ways.That means the Peer to Peer Lending platforms in China attract those borrowers who have bad credit performance and it will aggravate the credit risk in the whole market.So how to utilize the information technologies to improve the credit risk assessment in Peer to Peer lending is really an important issue.In this paper,we discussed the possibility of different credit risk assessment model applying in assessing the credit risk of borrowers in Peer to Peer lending.And we chose the Logistic regression model and BP neural network model.We chose several indicators to quantify the data from Eloan lending platform.Then bring these data into those two models above.The outcome shows us that the BP neural network model has an excellent performance.If the prediction outcome shows that the borrower will be unable to pay the debt in time.The platform should reconsider whether should lend money to this borrower.Somehow,the Logistic regression model cannot recognize the credit risk precisely in this experiment.Finally,this paper made a conclusion and suggested that our country shall accelerate the pace of building the credit collection system and utilize more digital technology in credit risk assessment and management in Peer to Peer Lending.