credit risk assessment

Published: 19 June 2018| Version 1 | DOI: 10.17632/27cndjvfbx.1
madjid khalilian


Nowadays, the three major credit risks at banks and financial institutions are effective in various models. These factors include the risk of customer failures, losses rate in case of failure sand outstanding debts. For the purpose of reducing credit risks for banks and financial institutes, it is first necessary to obtain financial and non-financial information with the customers to design and validate a model to assess the credit risk. The main objective of this study is utilizing data mining and knowledge extraction methods to creating a model for analyzing the credit risk factors as well as the causes of delay in installments. Many efforts have been carried out to reduce credit risk by a diversity of approaches such as statistics methods, operating research and financial theories, but lack of the efficient credit risk approaches is a major cause of bank failures and crises around the world.



Data Mining, Cluster Analysis, Classification System