OVERDUE PREDICTION OF BANK LOANS BASED ON DEEP NEURAL NETWORK
Journal Title: Topics in Intelligent Computing and Industry Design (ICID) - Year 2017, Vol 1, Issue 3
Abstract
With the development of information technology, the application of big data in financial aspects becomes more and more deepening. However, in the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, are not benefit from the data growth.This paper proposes to use DNN algorithm to forecast the risk of user loan based on the difficulties of current overdue prediction and the excellent learning ability of DNN. This article uses user basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. Firstly, this paper record bank records according to the transaction type, respectively, to generate income and spending data. Secondly, to sum the user browsing behavior also, and to record the average of credit card bill. In addition, in order to reduce the effect of eigenvalue size on the result, all characteristics are standardized. Finally, users who lack user information are discarded and the above fields are spliced. The spliced fields are the basic input for DNN. From the experimental results, DNN algorithm in crease over 6% prediction than kNN, Bayes algorithm.
Authors and Affiliations
Li Xin, Sun Guozi
Keywords
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- EP ID EP409149
- DOI 10.26480/iscsai.01.2017.26.28
- Views 102
- Downloads 0
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