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.

Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets.

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