Dr. Olcay Erdoğan
Independent Researcher
Email: [email protected]
DOI: 10.23918/ICABEP2019p40
(Full Paper)

This paper describes an approach for a credit risk evaluation based on machine learning classifiers. The constructed credit rating models were on sample data that consists of financial ratios from 356 enterprises that are listed on the Istanbul Stock Exchange. The applied methods are k- nearest neighbour, support vector machines and decision trees. This research develops models to evaluate the credit risk of the companies obtained from the financial statement of enterprises. The study supports building a balanced financial environment by reducing the cost of bankruptcy and help to determine the firms which are appropriate for the credit loan.

Keywords: CRA, credit risk, machine learning.

International Conference on Accounting, Business, Economics and Politics

ISBN: 978-9922-9036-3-7