Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability

Abstract This paper applies the Random Forest (RF) method for the robust modelling of credit default prediction. This technique has been proven as an efficient classifier and can provide better interpretability in comparison to other classifiers. Using Chines micro‐enterprise credit data set, this study emphasizes the multidimensional analysis of credit risk, such as the whole sample, subsample, and the incremental effect of the group of predictors. To that end, relative variable importance (RVIs) has been presented for all predictors according to the contribution in the prediction accuracy so that to ensure interpretability of the model. The empirical findings confirm that RF technique is reliable and efficient across all of the criteria used in this study. In addition, the examined experimental analysis indicates that non‐traditional variables have a significant effect on the classification accuracy. Thus, this paper recommends some alternative predictors like the legal representative's basic information, internal non‐financial factors, along with traditional financial variables for sustainable model development. The performance is compared from the perspective of five different performance measures. This modelling algorithm can be used by different financial markets participants to measure systematically credit default prediction of individual and institutional customers.