Ensemble Learning with Feature Optimization for Credit Risk Assessment

Abstract

Credit risk assessment stands as a cornerstone in financial decision-making, with significant implications for economic stability and growth. This paper highlights the transformative advantages of credit big data over traditional methods, particularly in enhancing the creditworthiness evaluation of small and medium-sized enterprises (SMEs). We delineate the distinctive features of the big data financial innovation model across six economic dimensions, showcasing its potential to reshape financial practices. To address the inefficiencies of traditional expert-driven approaches, we introduce an innovative 'Feature Selector-classifier Optimization Framework' that streamlines the credit risk prediction process. This framework not only refines the accuracy and efficiency of predictions but also integrates seamlessly with economic analysis, offering a robust tool for financial decision-makers. Our ensemble classifier delivers remarkable performance, exemplified by its high accuracy and AUC scores across multiple datasets, thereby validating the framework's efficacy in enhancing predictive power while ensuring operational efficiency.