Author(s)

Chenyang Sun ¹

Affiliation(s)

¹ Department of Automation, North China Electric Power University, Baoding, Hebei, 071000

Corresponding Author

Chenyang Sun

ABSTRACT

In this paper, we mainly mine the input and output data in the appendix, and use the analytic hierarchy process, neural network, nonlinear programming, least square fitting curve, spss to draw matrix scatter diagram and other methods and tools to get the credit index, credit risk and other factors of the enterprise, and then determine whether the bank lends to the enterprise and the credit strategy such as loan amount, interest rate and term. Through quantitative analysis of a certain number of enterprises, this paper gives the credit decision of banks to small, medium and micro enterprises. Firstly, we quantitatively analyze the reputation of enterprises according to the reputation grade and whether there are default records, and then quantify the reputation index and reputation risk of 123 enterprises according to the results of data mining in Annex 1,The comprehensive strength of enterprises and other variables stipulate that enterprises below a certain reputation index will not lend, and enterprises that do not meet the requirements of reputation index will be removed, and then 91 enterprises will be screened out for loan quota allocation after removing abnormal data. Analytic hierarchy process, nonlinear programming, matrix scatter diagram and other methods and tools are mainly used.

KEYWORDS

data mining, analytic hierarchy process, neural network, nonlinear programming, least square matrix scatter diagram

CITE THIS PAPER

Chenyang Sun, Credit decision-making of small and medium-sized enterprises. Financial Engineering and Risk Management (2021) 4: 84-90. DOI: http://dx.doi.org/10.23977/ferm.2021.040108

REFERENCES

[1] Shan Guangnian,Research on Credit Risk Management Strategy of Commercial Banks under the Background of Big Data [J], Business Economics, 2020(08): 164-165. 
[2] Pi Changxing, Thinking on credit risk management of SMEs under the new financial normal [J], Financial and Economic Circles, 2020(26): 24. 
[3] Gu Ming 'en, Deng Zhuo, Tang Zude, Chen Yu, Yang Yonghong, Quantitative evaluation of complete streets based on fuzzy comprehensive analytic hierarchy process [J/OL], Highway, 2020(09): 226-231 [2020-09-12] 
[4] Zhang Zhiping, Zhu Siying, Lu Fengguang,Study on the capital market shock effect of COVID-19 epidemic [J],Friends of Accounting, 2020(18): 131-137. 
[5] Xie lihui, risk prevention and control strategy analysis of small and micro enterprise loans of commercial banks under the impact of epidemic situation [J], science and technology economic market, 2020(07): 72-74.

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