Research On The Borrower Credit Evaluation System Of P2P Financial Platform

In recent years, the credit business of P2 P financial institutions is flourishing, P2 P financial institutions have been a place for micro companies and consumers to borrow money from, besides, as for the legitimate investors, P2 P financial institutions have become a platform of earning interests. Because the character of P2 P is fast, convenient and without collateral, the safety of funds depends entirely on the borrower’s credit. As for P2 P financial platform and investors, it is no doubt that the biggest risk is the credit risk of borrowers. Combined with the fact that the credit evaluation system of our country is not perfect, if borrowers who with low credit don’t pay money back, the investors have to face the loss of assets and the P2 P platform may have to undertake the risk of bad debt, what’s more, the investors ’ will lessen the trust of P2 P platform, the entire operation easy to fall into a vicious circle. So it does good to ensure the good operation of P2 P platforms, protect the legitimate rights of investors, help borrowers with high credit obtain the money and maintain the financial order if we can find the factors which can affect the level of borrowers’ credit and set up a credit evaluation system of borrowers’.Previous articles tend to use the subjective method of scoring to evaluate the credit of borrowers and the method of analytic hierarchy process to decide the weights of evaluation index. This paper choose logistic regression methods to evaluate and analysis borrowers’ credit and gain the factors which can have effects on borrowers’ credit assessment, it does good help to cut down bad debts ratio of P2 P platform and provide references for the safety of investors’ investment through comprehensive evaluation of borrowers’ ability of repayment and credit level. This paper’s main contributions are: Establish a set of alternative indicators of borrowers’ credit risk assessment; Preprocess and classify data; Descriptive statistics analysis of different categories of borrowers; Establish credit assessment model of borrowers by the methods of multiple logistic regression and binary logistic regression; the result of parameter estimation shows these ten affecting factors will have influence on borrower’s credit: the work properties of borrowers, the industry of borrower’s job, the degree of borrower’s job, whether the borrower borrowed money from the platform before and paid well, the average daily balance of borrower’s bank water, borrower’s assets and liabilities ratio, the overdue times of borrower’s bank cards and credits cards within three months, the application times of borrower’s bank cards and credits cards within six months, Whether the borrower is a guarantor, the character qualities of borrower; At last, propose recommendations for the improvement of our country’s credit assessment system of P2 P financial platform and reduction of investor’s risk ratio.