Machine learning-based classifiers ensemble for credit risk assessment

Abstract

Credit risk assessment is acting as a survival weapon in almost every financial institution. It involves deep and sensitive analysis of various financial, social, demographic and other pertinent data provided by the customers and about the customers for building a more accurate and robust electronic finance system. The classification problem is one of the major concerned in the process of analysing gamut of data; however, its complexity has ignited us to use machine learning–based approaches. In this paper, some machine learning algorithms have been studied and compared their effectiveness for credit risk assessment. Further, as an extension of our study, we develop a novel sliding window–based meta–majority voting ensemble learning to improve the prediction accuracy of credit risk assessment problem by properly analysing the underlying samples. The experimental findings draw a clear line between the proposed ensembler and traditional ensemblers. Moreover, the proposed method is very promising vis–à–vis of individual classifiers.