Designing a smart model for granting banking facilities based on big data based on macroeconomic variables, sanctions and economic shocks

The granting of bank facilities and the targeting and identification of suitable customers by banks is a serious concern. The non-repayment of granted loans by customers can severely damage the profitability of banks, leading them to move towards proprietorship and economic activity, which ultimately results in increased inflation and many other economic problems. Based on the problem mentioned, the aim of the present research is to design an intelligent model for granting bank facilities based on big data, considering macroeconomic variables, sanctions, and economic shocks. To design this model, ۶ macroeconomic variables, shocks, and sanctions were included in the model. The model was evaluated using four machine learning algorithms: multiple regression, support vector machine, decision tree, and random forest, based on customer data from the country's banks. Subsequently, economic growth, unemployment rate, and Gini coefficient have relatively less influence, estimated to affect loan repayment or the number of unpaid loans by ۹%, ۱۰%, and ۱۱% respectively.

کلیدواژه ها:

Smart model ، banking facilities ، big data ، macroeconomic variables ، economic shock