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Research on Bank Credit Strategy Decision Making Based On Neural Network and Nonlinear Programming

Published:14 March 2022Publication History

ABSTRACT

In this paper, on the basis of the existing bank lending schemes, first quantitatively from the enterprise strength, corporate reputation and the stability of supply and demand of three selected 6 most representative credit measure, to quantify credit risk model based on XGBoost decision tree is calculated for each corporate lending after the probability of default, in order to measure the possibility of its repayment on time, The function relation between annual interest rate and customer churn rate is fitted by curve, so as to perfect the constraint conditions, establish the mechanism of nonlinear programming model, and get the optimal credit strategy decision scheme of the bank. Finally, DNN neural network model is used to predict and verify the data, analyze the law of credit strategy, conduct robustness analysis and verify the rationality of the results. Thus it solves the problems of quantification of risk and optimal credit strategy in bank credit of small and medium-sized enterprises.

References

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  • Published in

    cover image ACM Other conferences
    AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture
    October 2021
    3136 pages
    ISBN:9781450385046
    DOI:10.1145/3495018

    Copyright © 2021 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 14 March 2022

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