MINIMIZATION OF RISK AND MAXIMIZATION OF PROFIT ON BEHALF OF BANK BY USING MACHINE LEARNING ALGORITHM

Abstract

The main aim to develop a loan approval prediction model for a bank by utilizing machine learning algorithms. [Objective] The primary objective is to minimize the bank's loss by developing a decision rule for approving or rejecting a loan application based on an applicant's demographic and socio-economic profile. [Method] Three machine learning algorithms, namely Logistic Regression, Random Forest, and Decision Tree, were applied to a German credit dataset to achieve this objective. The data were preprocessed, including converting the target variable to binary, converting character variables to factors, and selecting only numeric variables. The accuracy, sensitivity, and precision of the models were evaluated using crossvalidation, and the results were compared. [Result] The Random Forest model showed the highest accuracy, with an average accuracy of 76.07%, followed by the Decision Tree model with an average accuracy of 71.99%, and Logistic Regression with an average accuracy of 71.62%. The sensitivity evaluation showed that the Decision Tree model had the highest sensitivity, with an average sensitivity of 70.88%, followed by the Logistic Regression model with an average sensitivity of 68.35%, and the Random Forest model with an average sensitivity of 65.86%.[Conclusion] In conclusion, the Random Forest model showed the highest accuracy, while the Decision Tree model had the highest sensitivity. However, all three models showed similar precision scores. Therefore, based on the objective of minimizing the bank's loss, the Decision Tree model may be more suitable as it has higher sensitivity, which means it has a lower chance of approving a loan for a potentially risky applicant. However, further research and testing are necessary before implementing these models in real-world applications.