Abstract

MEDEIROS ASSEF, Fernanda  and  ARNS STEINER, Maria Teresinha. Ten-year evolution on credit risk research: a Systematic Literature Review approach and discussion. Ing. Investig. [online]. 2020, vol.40, n.2, pp.50-71.  Epub Dec 18, 2020. ISSN 0120-5609.  https://doi.org/10.15446/ing.investig.v40n2.78649.

Given its importance in financial risk management, credit risk analysis, since its introduction in 1950, has been a major influence both in academic research and in practical situations. In this work, a systematic literature review is proposed which considers both "Credit Risk" and "Credit risk" as search parameters to answer two main research questions: are machine learning techniques being effectively applied in research about credit risk evaluation? Furthermore, which of these quantitative techniques have been mostly applied over the last ten years of research? Different steps were followed to select the papers for the analysis, as well as the exclusion criteria, in order to verify only papers with Machine Learning approaches. Among the results, it was found that machine learning is being extensively applied in Credit Risk Assessment, where applications of Artificial Intelligence (AI) were mostly found, more specifically Artificial Neural Networks (ANN). After the explanation of each answer, a discussion of the results is presented.

Keywords : credit risk assessment; machine learning; systematic literature review.

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