Development of a Fuzzy Logic Model based on Risk of Credit Default

Abstract:

The financial sector constantly changes and evolves, and in every facet of its evolution, credit risk has been an important topic. This research paper introduces an innovative method of evaluating credit risk: fuzzy systems which apply fuzzy sets to manage uncertainties in the process of credit risk prediction. By incorporating demographic variables such as education status, age, income and marital status and bank statement variables such as average credit, opening balance, average debt, and closing balance, the proposed system assigns a credit risk category to loan applicants, the fuzzy inference system is created which applies the fuzzy logic to access the creditworthiness of applicants based on the input variables. The system is assessed for its practicality and accuracy using a dataset of historical loan applications. The findings reveal that the system achieved an accuracy of 72% demonstrating its efficacy in predicting credit risk. Based on this result, the proposed credit risk assessment system can be implemented by banks and other financial institutions to automate and improve the loan approval system in enhancing the efficiency of credit risk assessment, resulting in proved profitability and more informed decision-making.

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