农户贷款信用风险评估——基于CFPS2018数据的分析

研究旨在提高农户贷款信用风险度量的准确性,降低银行涉农贷款的不良率,促进银行对农户的信贷覆盖。从农户户主特征、资产负债、家庭收支和还款意愿4个方面选取违约判别指标,运用因子分析法克服多重共线性,基于Logistic原理构建适用于农村中小银行的农户贷款违约风险评估模型,并以中国家庭动态跟踪调查CFPS2018数据为样本进行实证研究。研究发现,农户的资产状况是影响贷款违约最主要的因素,资产负债率与其贷款信用风险正相关;家庭消费性支出越多,贷款违约的可能性越大;信任认同是重要因素,农户与人合作的信任度越高,违约风险越小。构建模型的预测准确率超过90%,具有普遍适用性。鼓励有条件的农村中小银行实施信用风险内部评级,提高农户的信贷覆盖,同时要加强相关配套建设,如优化风险组织架构、完善风险管理制度、优化风控人才队伍。

Credit Risk Assessment of Farmer Loans: Based on CFPS2018 Data

The aim of the study is to improve the accuracy of credit risk measurement of farmer loans, reduce the non-performing rate of banks’ agricultural loans and promote the bank's credit coverage to farmers. We selected default indicators from the four aspects of householder characteristics, assets and liabilities, income and expenditure, and willingness to repay, and used a combination of factor analysis and Logistic regression to build the credit risk assessment model for farmer loans. The empirical analysis took CFPS2018 data as a sample. The asset status of farmers was the most important factor affecting loan default, and the asset-liability ratio was positively correlated with the credit risk of loans. The more households spend on consumption, the more likely their loans will default. Recognition of trust is an important factor, the higher the trust of farmers in cooperation with others, the lower the risk of default. Moreover, the prediction accuracy of the constructed model exceeded 90%, which was universally applicable. We encourage qualified rural small and medium-sized banks to implement internal credit risk rating to improve the credit coverage of farmers. At the same time, banks should strengthen related supporting constructions, such as optimizing the risk organization structure, improving the risk management system, and building the risk control talent team.