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Credit Risk Assessment Using Machine Learning Algorithms

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Financial institutions suffer from risk of losing money from bad customers. Specifically banking sectors where the risk of losing money is higher, due to bad loans. This causes economic slowdown of the nation. Hence credit risk assessment is an important research area. In this paper research methodology based framework using diagnostic and cross sectional study is used for risk analysis. Empirical approach is used to build models for credit risk assessment with supervised machine learning algorithms. The Logistic Regression and Neural Network classification models are implemented and evaluated using are evaluated using chi square statistical test. This study infers the significance of using machine learning algorithms to predict bad customers. Logistic Regression has shown better performance for the data set and parameters which are considered for this work.
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Keywords: Chi Square Test; Credit Risk; Logistic Regression; Machine Learning; Neural Network

Document Type: Research Article

Affiliations: Department of Information and Communication Technology, Manipal Institute of Technology, Manipal University, Manipal, Karnataka, India

Publication date: April 1, 2017

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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