DESIGN AND IMPLEMENTATION OF A WEB-BASED CREDIT RISK ASSESSMENT SYSTEM

Credit risk assessment is a critical process in lending institutions aimed at evaluating the likelihood of loan default by applicants. Traditional manual assessment methods are often inconsistent and time-consuming, while many automated credit scoring systems lack transparency and explainability. This study presents the design and implementation of a web- based credit risk assessment system . The proposed system captures key applicant attributes and applies predefined decision rules to compute credit risk scores. Debt-to-income ratio and other affordability indicators are used to classify applicants into low, medium, or high risk categories. The system generates clear and interpretable credit decisions accompanied by explanatory reasons. A structured system analysis and design methodology was adopted to guide development. The system was implemented using web technologies and deployed as a browser- based prototype. Functional testing was conducted using representative test cases. Test results confirmed that the system produces accurate, consistent, and explainable credit decisions. The system enhances transparency, usability, and decision accountability. The study demonstrates that explainable rule-based models can effectively support automated credit risk assessment in small and medium-scale lending environments.