A NEW EFFICIENT CREDIT SCORING MODEL FOR PERSONAL LOAN USING DATA MINING TECHNIQUE FOR SUSTAINABILITY MANAGEMENT

Credit scoring models are used in decision-making processes to produce an accurate prediction of an applicant’s creditworthiness. A five-step credit scoring model for personal loans was developed using the seven-step credit scoring model by Siddiqi. It uses real data provided by a bank. This study aims to remove the unnecessary complexity of the credit scoring process. The five-step credit scoring model consists of data massaging, factor analysis, data mining modelling, credit scoring and post-modelling. To ensure accuracy, factors that were significant in determining the creditworthiness of applicants were used in the model, which are the type of installment, age, monthly expenses, job sector, payment method and income-to-finance ratio. Furthermore, by presenting a systematic and structured step for developing a credit scoring model, this study contributed to the research on credit scoring. Based on the findings of this study, banks may use this model to create their own credit scoring model to assess the creditworthiness of personal loan applicants. By managing risks with this model, banks can create a long-term solution for credit system management and aid in the decision-making process.