A Single Default Discrimination Model Based on the Selection of Multiple Single Models

The banking industry and other financial institutions face the economic problem of deciding whether it is appropriate to provide a client with credit who later demonstrates to be a good risk. Credit risk assessment is more crucial than ever in light of the recent global economic collapse and the terrible circumstances associated with COVID-19. Banks must utilize their resources, which include knowledge about their customers, to decide who may borrow money and is likely to pay it back. Feature selection critically choosing the optimal features for credit default discrimination. Removing outliers or noisy data from training sets is an alternative approach to improving discrimination model performance. This paper using optimal features through chi square (CS)- recursive feature elimination cross validation (RFECV) and select the optimal companies through Local outlier factor (LOF) as preprocessing combination to build single Default Discrimination Model for Chinese listed companies' dataset. Our model effectiveness has been demonstrated through in-depth comparisons with the baseline models across two datasets. The findings are based on a combination of data from Chinese listed companies and robustness cross German credit dataset. Experimental results verify the proposed model ability to generate multiple high-performance for credit default discrimination.