Abstract
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, cross sectional study is used for risk analysis. Empirical approach is used to build models for credit risk assessment with supervised machine learning algorithms. Two classification and prediction models are built one using Logistic regression and other using Neural Network. Models are evaluated using chi square statistical test. This study infers the significance of using machine learning algorithms in predicting bad customers. For the data set and parameters considered logistic regression has shown better performance.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
