基于Logistic回归的中小企业信贷风险评估与信贷策略优化建模

doi:10.6040/j.issn.1671-9352.0.2023.206

Abstract

Abstract:

In order to facilitate banks to assess the credit risk of small and medium-sized enterprises, and formulate the optimal credit strategy, an indicator system composed of four primary risk assessment indicators, namely, business income ability, profitability, customer stability, and transaction vitality, is constructed by using the bank flow information of enterprises with upstream and downstream partners. The enterprise credit risk is predicted based on Logistic regression, and compares it with error back-propagation neural network. Combining the probability of default and the retention rate under different interest rates, taking the maximum expected return of banks on small and medium-sized enterprises as the objective function, the credit strategy optimization model is established. In order to verify the effectiveness of the model, the credit risk assessment and credit strategy optimization models are empirically analyzed. The results indicate that Logistic regression has high accuracy and recall, and the area under the curve of receiver operating characteristic reaches 0.964, which is suitable for credit risk prediction and assessment of small and medium-sized enterprises. The credit strategy optimization model can determine the loan amount and loan interest rate of each lending enterprise, and maximize the expected return of the bank.

Key words: credit risk assessment, credit strategy, Logistic regression, expected return, small and medium-sized enterprise

CLC Number:

Zhongfeng QU,Honghua WU,Fanjun LI. Creditrisk assessment based on Logistic regression and credit strategy optimization modeling of small and medium-sized enterprises[J].JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(8): 94-102.

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References 21

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观测值	预测值		准确率/%
观测值	0	1	准确率/%
0	22	2	91.7
1	0	7	100.0

违约概率	[0, 0.001)	[0.001, 0.1)	[0.1, 0. 5)	[0.5, 1]
企业数量	32	29	32	30

观测值	预测值		准确率/%
观测值	0	1	准确率/%
0	22	2	91.7
1	2	5	71.4

观测值	预测值		准确率/%
观测值	0	1	准确率/%
0	17	7	70.8
1	0	7	100.0

企业编号	月均营业额/万元	违约概率	贷款额度/万元	贷款利率
E075	17	0.000 03	16	0.050 00
E047	137	0.000 09	50	0.050 00
E061	56	0.005 20	50	0.051 60
E055	70	0.097 50	50	0.082 40
E042	67	0.119 40	50	0.090 60
E062	28	0.208 40	22	0.129 00
E088	6	0.325 30	4	0.150 00

Creditrisk assessment based on Logistic regression and credit strategy optimization modeling of small and medium-sized enterprises

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违约损失率	违约概率上限	贷款企业数	期望贷款总额/万元	期望收益/万元
0.1	0.50	93	2 480	121
0.2	0.43	89	2 347	115
0.3	0.33	84	2 245	110
0.4	0.27	82	2 166	106
0.5	0.23	76	2 098	103
0.6	0.20	72	2 041	101
0.9	0.13	65	1 923	95

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