Decision Support System for Loan Granting

Abstract

The aim of this research is to improve the Decision Support System (SPK) in purchasing credit loans. A key finding of this study is the requirement for financial institutions to have effective tools for calculating loan-to-value ratios to reduce the risks associated with credit failure. The analysis methods used include collecting credit history data, analyzing factors that influence credibility, and developing fuzzy logic algorithms. The developed system is then evaluated using non-public data from financial institutions to assess accuracy and reliability. Research findings show that this system can increase the accuracy of credit reporting compared to the manual method used previously. The main finding of this research is that fuzzy logic-based SPK training can be an effective tool to assist financial institutions in the loan application process, thereby reducing credit risk and increasing operational efficiency. The research results show that the Simple Additive Weighting (SAW) method can increase the accuracy of credit worthiness assessments. The SAW method has been proven to increase accuracy and reliability in the assessment process. The use of various criteria such as income, credit history, debt ratio, employment, and collateral provides more comprehensive results. This research also shows that fuzzy logic is able to handle uncertain and subjective data, so it can help financial institutions make better decisions.