The construction of credit evaluation index system of Chinese family farm and pasture is not only a theoretical problem but also of great practical significance. In this paper, based on the depth‐weighted Bayesian theory and fuzzy mathematics, the improved depth‐weighted fuzzy Bayesian hybrid algorithm model is proposed to solve the unbalanced problem of default status of family farm and pasture and to build the index system with the ability of three categories of default identification. In this paper, the characteristics of the first one is based on fuzzy set theory, the definition of fuzzy linguistic assessment of different default set, family ranches characteristic is converted to the corresponding index of pasting with triangular fuzzy mathematical model, and then through the inner method converting triangular fuzzy number into accurate output data to deal with the blur and uncertainty about the state of the fuzzy default transformation is realized. Second, based on the insensitive characteristic of ROC curve to skewness samples, the depth weighting of characteristic indexes in nondefault, low default and high‐default states was completed by constructing multiclassification ROC curve, which solved the practical problem of sample imbalance in different default states of family farms and ranches, and selected the index system with significant discrimination ability for default states by integrating default identification ability.

In China, small enterprises have a direct role in economic growth, but they have difficulty in financing development. To address this problem, this paper creates a small business credit evaluation index using a two-stage Bayesian discriminant model. In the first stage, customers are distinguished by whether they are in default, and in the second stage, customers with continuing default are divided into those with a high default loss rate and those with a low default loss rate. The literature to date has identified a credit index only for the first stage; the credit evaluation index proposed here is based on two stages, which is more sensitive. Then, we conduct an empirical analysis using credit data on 3,111 small enterprises in China with a two-stage nonparametric Bayesian discriminant model and a parametric discriminant model, and then, we test the two indicator systems with discriminant accuracy and an ROC curve; the discriminant accuracy of the established index system is 77.95% and 70.95%, respectively, and their prediction accuracy is 0.902 and 0.866, respectively; they show that the constructed indicator system is robust and effective. Finally, we conduct a comparative analysis of discriminant accuracy in three models, finding that the two-stage nonparametric model is optimal, the two-stage logistic regression model is suboptimal, and the two-stage parametric model is poor.

This paper selects credit data of 1688 micro enterprises in a certain commercial bank in China from 2013 to 2016 as empirical research objects. We use the combination method of t test and correlation analysis to construct credit rating indicator system, then use entropy weight method and fuzzy integration to construct credit evaluation model, and lastly divide credit rating of micro enterprises. The empirical results indicate three main points. Firstly, final constructed indicator system can significantly discriminate credit state and avoid information overlapping, and curve ROC proves credit rating indicator system is effective. Secondly, we use entropy weight method to weight indicators objectively, and evaluate credit state comprehensively based on fuzzy integration model. At last, credit rating of micro enterprises proves that upper rating has minor default enterprises, and overall micro enterprises? credit state is basically on average credit level.

As an important part of the national economy, small enterprises are now facing the problem of financing difficulties, so a scientific and reasonable credit rating method for small enterprises is very important. This paper proposes a credit rating model of small enterprises based on optimal discriminant ability; the credit score gap of small enterprises within the same credit rating is the smallest, and the credit score gap of small enterprises between different credit ratings is the largest, which is the dividing principle of credit rating of small enterprises based on the optimal discriminant ability. Based on this principle, a nonlinear optimization model for credit rating division of small enterprises is built, and the approximate solution of the model is solved by a recursive algorithm with strong reproducibility and clear structure. The small enterprise credit rating division not only satisfies the principle that the higher the credit grade, the lower the default loss rate, but also satisfies the principle that the credit group of small enterprises matches the credit grade, with credit data of 3111 small enterprises from a commercial bank for empirical analysis. The innovation of this study is the maximum ratio of the sum of the dispersions of credit scores between different credit ratings and the sum of the dispersions of credit scores within the same credit rating as the objective function, as well as the default loss rate of the next credit grade strictly larger than the default loss rate of the previous credit grade as the inequality constraint; a nonlinear credit rating optimal partition model is constructed. It ensures that the small enterprises with small credit score gap are of the same credit grade, while the small enterprises with large credit score gap are of different credit grades, overcoming the disadvantages of the existing research that only considers the small enterprises with large credit score gap and ignores the small enterprises with small credit score gap. The empirical results show that the credit rating of small enterprises in this study not only matches the reasonable default loss rate but also matches the credit status of small enterprises. The test and comparative analysis with the existing research based on customer number distribution, K‐means clustering, and default pyramid division show that the credit rating model in this study is reasonable and the distribution of credit score interval is more stable.

The micro enterprises’ credit indicators with credit identification ability are selected by the two classification models of Support Vector Machine for the first round of indicator selection and then for the second round of indicator selection, deleting credit indicators with redundant information by clustering variables through the principle of minimum sum of deviation squares. This paper provides a screening model for credit evaluation indicators of micro enterprises and uses credit data of 860 micro enterprises samples in Inner Mongolia in western China for application analysis. The test results show that, first, the constructed final micro enterprises’ credit indicator system is in line with the 5C model; second, the validity test based on the ROC (Receiver Operating Characteristic) curve reveals that each of the screened credit evaluation indicators is valid.

Early warning models credit risk play a crucial role in helping the financial institutions to reasonably predict the credit status of family farms and ranches. An attempt is made in this paper to construct a new credit risk early warning model based on Probit regression and Kmeans clustering algorithm, and testing the model by using data from 246 family farms in 12 leagues and cities in Inner Mongolia. First, the credit risk evaluation indicators of family farms and ranches were screened out through a three-combination model with partial correlation analysis, tolerance analysis and Probit regression. Second, the ratios of the Z-squared statistic of a single indicator to the sum of the Z-squared statistics of all the selected indicators were used to measure the weights of the credit evaluation indicators. Finally, four warning levels containing heavy alert level Ⅰ, medium alert level Ⅱ, light alert level Ⅲ and no alert level Ⅳ were classified by Kmeans clustering with large intra-cluster similarity and small inter-cluster similarity. The empirical evidence shows that the early warning model of credit risk for family farms and ranches is effective.