An Automated Literature Analysis on Data Mining Applications to Credit Risk Assessment

Abstract

This chapter presents an automated literature analysis of data mining applications to credit risk assessment, encompassing the period from 2010 to 2014. Google Scholar was used to collect the 100 most relevant articles published in management and information systems conferences and journals containing the keywords ‘data mining’ and ‘credit risk’. This set of articles served as a basis for assessing the main trends of research in data mining applications to credit risk, first by using text mining, then through the Latent Dirichlet allocation Algorithm for grouping the articles into logical topics.