DECISION SUPPORT SYSTEM FOR LOAN APPROVAL USING EMPLOYEE FINANCIAL DATA

In the financial sector, particularly in loan processing, the ability to assess the creditworthiness of applicants swiftly and accurately is crucial. This project presents the development of a Decision Support System (DSS) designed to automate and enhance the loan approval process using employee financial data. Traditional loan approval systems often rely on manual evaluation, which is time-consuming, error-prone, and subject to human bias. By integrating employee financial attributes such as income, savings, debt-to-income ratio, and employment history, the proposed DSS provides a data-driven framework that ensures objective and consistent decision-making. The system utilizes a combination of rule-based logic and predictive analytics to assess loan applications, with key features including automated data extraction, eligibility evaluation, credit risk scoring, and recommendation generation. A modular architecture allows the system to interface with existing human resource databases and banking software, enhancing adaptability and scalability. Various diagrams, including ER, class, and data flow diagrams, are presented to illustrate the system’s structure and workflow. This DSS not only improves the efficiency and transparency of the loan approval process but also reduces the workload on loan officers and minimizes risks for financial institutions, demonstrating the practical application of intelligent systems in credit risk management and serving as a foundation for further research into AI-driven financial decision support.