| Abstrakt: |
With the quick growth of the credit card system, there is a rising number of misconduct rates on credit card loans, which creates a financial risk for commercial banks. Thus, successful resolutions of the risks are significant for the sound advancement of the industry in the long term. Numerous financial banks and organizations become more and more attentive to the issue of credit card default because it brings about a high probability of financial risks. Credit risk plays a significant part in the financial business. One of the main functions of a bank is to issue loans, credit cards, investment mortgages, and other credit. One of the most popular financial services offered by banks in recent years has been the credit card. With its constant rise in risk factors, the banking industry is perhaps the most fragile and volatile in the world. Credit risk remains a crucial element for financial institutions that have experienced losses amounting to hundreds of millions of dollars as a result of their incapacity to retrieve the funds disbursed to clients. In the banking industry, it is now vital to forecast whether a borrower will be able to repay the loan. In this paper, we applied different machine learning classifiers, including Random Forest, K Nearest Neighbor, Logistic Regression, Decision Tree, Decision Tree with AdaBoosting, and Random Forest with AdaBoosting, to build a credit default prediction model. The results show that the AdaBoosting model achieved better accuracy than the other machine learning algorithms. Our proposed technique can support financial organizations in controlling, identifying, and monitoring credit risk, and it can identify credit card clients who pay the loan in the next month. [ABSTRACT FROM AUTHOR] |
