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

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
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.
Conclusion: Based on the data mining technology and learned other researchers’ achievements, we studied the methods of logistic regression, BP neural network and AdaBoost, and improve complex approval work and reduce prediction error for the traditional loan. In this paper we combine logistic regression with BP neural network and then we use AdaBoost to intensify the model. For the traditional loan approval problem, we fully consider the user registration information and user sources to more accurately predict user success rate for the loan. According to the user multidimensional messages, we can clearly know the users, furthermore, through analyzing the sources of users as well as the user fraud score, we can make accurate judgment to user. Finally L-B-A model was used to the P2P loan platform, and the practice proved that model had high practicability and can achieve the purpose of simplifying the loan approval process.
The Credit Scoring Model Based on Logistic-BP-AdaBoost Algorithm and its Application in P2P Credit Platform

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