Imbalanced credit risk prediction based on SMOTE and multi-kernel FCM improved by particle swarm optimization

Recently ensemble models were adopted to predict the credit risk commonly. Although they have a better performance generally, ensemble models are easy to be badly affected by imbalance classes which are a common issue in credit risk prediction. And the prediction model should be constructed according to the feature of complex distributions of financial data. However, these problems have not attracted enough attention. This paper constructs an ensemble model for imbalanced credit risk prediction and improves algorithms for the feature of financial data. The ensemble model mainly combines Synthetic Minority Over-sampling Technique Evaluation (SMOTE) and Multi-Kernel Fuzzy C-Means (MK-FCM) optimized by Particle Swarm Optimization (PSO). In the preprocessing phase, the multi-method of descending dimension is used to reduce the dimension. The improved SMOTE can make new synthetic samples more decentralized, which can not only balance the number of samples of different classes, but avoid the overfitting to some extent. In the base classifier construction phase, Fuzzy C-Means (FCM) is improved by multi-kernel function to build a new base classifier MK-FCM, which can synthetize the merits of multiple kernel functions to enhance the evaluated performance. The improved PSO, which has dynamically adjustable function, is used to optimize parameters for MK-FCM. In the empirical research, the sample data are from financial indicators of Chinese listed hospitality and tourism corporations, and the proposed model makes the comparative analysis with other relative models. The results from Matlab software show that the presented ensemble model has the best performance on imbalanced credit risk prediction. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study constructs an ensemble model for imbalanced credit risk prediction and improves algorithms for financial data features. By combining SMOTE and MK-FCM methods, the model can effectively balance the number of samples from different classes in predictions, and avoid overfitting.