Prediction banking loan credit risk using data mining technique in COVID-19 virus condition

In the current situation and the spread of the Corona virus around the world, many businesses have faced severe economic problems with the announcement of quarantine and closure by governments to prevent further spread of the virus. Due to this, businesses need to get a bank loan to restart and return to the pre-virus conditions to compensate for the economic damage. Therefore, banks have to provide a lot of facilities to different applicants, and this causes banks to be exposed to credit risk. Data mining is one of the best ways to predict the credit risk of bank loans so that banks do not suffer losses and do not go bankrupt. In this research, database data will be analyzed and forecasted and risk forecasting will be examined by Gradient boosting method which is one of the data mining methods. Database analysis and forecasting show which applicants will have the least credit risk for banks.

Keywords:

Data mining , Banking loan credit risk , COVID-19 virus