The effects of customer segmentation, borrower behaviors and analytical methods on the performance of credit scoring models in the agribusiness sector

The main aim of this study is to analyze the joint effects of customer segmentation, borrower characteristics and modeling techniques on the classification accuracy of a scoring model for agribusinesses. To this end, we used data provided by a Chilean company on 161163 loans from January 2007 to December 2013. We considered random forest, neural network and logistic regression models as analytical methods. Regarding borrowers’ profiles, we examined the effects of sociodemographic, repayment behavior, agribusiness-specific and credit-related variables. We also segmented the customers as individuals, small and medium-sized enterprises, and large holdings. As the segments show different risk behaviors, we obtained a better performance when we estimated a scoring model for each segment instead of using a segmentation variable. In terms of the value of each set of variables, behavioral variables increased the predictive capability of the model by double the amount achieved by including agribusiness-related variables. The random forest is the model with the best classification accuracy.