Research on credit strategy of small, medium, and micro enterprises based on commercial banks

Abstract

In order to help banks formulate Credit Strategies for small, medium-sized and micro enterprises, this paper analyzes enterprises from two aspects. On the one hand, starting from the enterprise's data and from the enterprise's relevant data, including qualitative indicators such as reputation level, invoice information, credit record and operating income, a model is established to quantitatively analyze the operation status and reputation status of the enterprise. On the other hand, through the analysis of the "maximum lending rate" and "maximum lending rate" of the bank, we can analyze the "maximum lending rate" and "how much lending rate" of the bank. Combining the above variables, we can get the best credit strategy of the bank under the factors of credit risk and income. Firstly, without considering the influence of other data, this paper abstracts five indicators to measure the reputation status and enterprise-scale from the data of small and medium-sized enterprises with credit rating, quantifies the indicators. At the same time, the Euclidean distance is calculated by MATLAB modeling, the correlation between lending enterprises and non-lending enterprises is analyzed, and the high-risk enterprises in lending enterprises are selected. Then, among the remaining enterprises that can issue loans, five different credit strategies are formulated according to the weighting of their credit rating and enterprise scale as the primary basis for the number of bank loans and interest rate. Secondly, the focus is on how to comprehensively quantify the credit status and business problems of small, medium-sized and micro enterprises without credit records. Without considering the influence of other data, this paper mainly analyzes the correlation between the enterprises without credit records and the enterprises mentioned above that do not grant loans. It preliminarily obtains the enterprises that can grant loans. Then, through decision tree modeling, the loan demand of enterprises with different credit levels is estimated as a variable. By fitting the curve between interest rate and customer churn rate, different interest rate ranges under different levels are obtained as the second variable. Finally, through the above two variables, we establish the profit function of bank credit, find the corresponding variables at the maximum value, and formulate relevant credit strategies.