Rough set-based feature selection for credit risk prediction using weight-adjusted boosting ensemble method

With the tremendous development of financial institutions, credit risk prediction (CRP) plays an essential role in granting loans to customers and helps them to minimize their loss because credit approval sometimes results in massive financial loss. So extra attention is needed to identify risky customer. Researchers have designed complex CRP models using artificial intelligence (AI) and statistical techniques to support the financial institutions to take correct business decisions. Though there are various statistical and AI methods available, the recent literature shows that the ensemble-based CRP model provides improved prediction results than single classifier system. The small increase in the performance of CRP model could result in a significant improvement in the profit of financial institutions and banks. This work proposes a weight-adjusted boosting ensemble method (WABEM) using rough set (RS)-based feature selection (FS) technique with the balancing and regression-based preprocessing called RS _ RFS-WABEM. Regression is used to fill missing value in the records to improve the performance of CRP. Three credit datasets (Australia, German and Japanese) are chosen to validate the feasibility and effectiveness of the proposed ensemble method. The trade-off between the uncertainty and imprecise probability of the proposed classifier model is evaluated using the performance measures such as accuracy and area under the curve. Experimental results show that the proposed ensemble method performs better than other base and ensemble classifier methods.