Evaluating farmers’ credit risk: A decision combination approach based on credit feature

Abstract

The existing default discrimination models based on evaluation indicators are difficult to achieve higher credit risk identification performance of farmers’ default status under the situation of insufficient credit information and low correlation between indicators and default risk. Those models are difficult to find out the fundamental causes of farmers’ default risk. A credit risk discrimination model based on credit features strongly with default status is established to evaluate the farmer’s credit risk. Term frequency inverse document frequency and sentiment dictionary analysis method are used to quantify long text indicators, then the K-means method is used to Boolean the numerical data. The APRIORI algorithm is used to mine the credit features strongly associated with the default status. Finally, the default status of farmers is judged based on those credit features. The model is detailed using actual bank data from 2044 farmers within China. According to the five-evaluation criterion of AUC, F1-score, Type II-error, Balance error rate and G-mean, the empirical results show that the ability of the credit risk discrimination model with credit features is higher than that of the model based on evaluation indicators. This finding provides a new idea for commercial banks to measure the default risk of farmers, and provides a reference for the formulation of strategies to enhance farmers’ credit.