Using machine learning to create a credit scoring model in banking and finance

Recently, personal credit is one of the most important products among all credit products that banks offer on the market. Banks and Financial Institutions (FIs) are always trying to find an effective credit score assessment model to reduce lending risks as well as increase income for the banks and financial institutions. In this paper, machine learning is applied to create credit scoring model for bank application. The work also discussed on how to calculate and set threshold for setting an ideal credit score cut-off point. Experimental results show that our proposed method can be applied in the banks or Credit Institutions to reduce risks in loan services.