The Credit Scoring Model Based on Logistic-BP-AdaBoost Algorithm and its Application in P2P Credit Platform

The problem of credit risks forecasting is one of the most actively studied issues nowadays, as it is the main risk that commercial bank faced in the management. With a fully opened the domestic financial sector, the banking industry is facing increased competition from this industry. Improving the client satisfaction, the business transaction efficiency and the risk-control ability has become the main focus of competition in the banking industry. In this paper, we apply the Logistics algorithm, BP neural network and the AdaBoost algorithm to build the model (Logistic-BP-AdaBoost model) which can estimate credit score of the applicant with their multidimensional personal data. Compared with other methods, L-B-A model have a higher assessing accuracy which can help identify the possibility of loan default of the applicant and provide a score for each applicant. We apply this model to a websites and establish an online loan platform which is expected to improve the efficiency and reduce costs of traditional lending business.