| 摘 要: 为切实解决中小微企业贷款融资和银行对中小微企业贷款策略之间存在的问题，提出了基于风险等级的中小微企业信贷模型。该模型创新性地将机器学习算法引入传统中小微企业信贷风险及策略的研究当中，运用PCA降维、K-means聚类确定企业风险等级；通过Fisher线性判别确定银行信贷利率。应用该模型将123 家中小微企业分成五类风险等级，并给出银行对五类不同风险等级企业的贷款额度及利率，并通过实验验证模型的有效性和正确性。 |
| 关键词: K-means聚类；PCA降维；Fisher线性判别；信贷模型 |
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中图分类号: TP391
文献标识码: A
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| Research on the Credit Model of Small, Medium and Micro Enterprises based on Risk Level |
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GU Yifan1, HUANG Liyuan2, LIN Chenxin2, CAO Chunping1
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( 1.School of Optical -Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
guyifan2020@126.com; 948384993@qq.com; 1246506991@qq.com; 2213893844@qq.com
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| Abstract: In order to effectively solve the problems between loan financing of small, medium and micro enterprises and the bank's loan strategy for them, this paper proposes to build a credit model for small, medium and micro enterprises based on risk level. This model innovatively introduces machine learning algorithms into the research on credit risks and strategies of traditional small, medium and micro enterprises. PCA (Principal Components Analysis) dimensionality reduction and K-means clustering are used to determine enterprise risk level. Bank credit interest rate is determined by Fisher linear discriminant. Based on this model, 123 small, medium and micro enterprises are divided into five risk levels, and the bank's loan lines and interest rates for each level are given. Validity and accuracy of the model are verified through experiments. |
| Keywords: K-means clustering; PCA dimensionality reduction; Fisher linear discriminant; credit model |
