SmartLoanAI: An Intelligent Framework for Automated Loan Decision Systems

The demand for scalable, secure, and intelligent loan processing systems has grown as a result of the financial technology industry's fast expansion. This study introduces SmartLoanAI, an AI-powered framework intended to automate risk assessment and loan eligibility determination in an online banking setting. In order to examine applicant characteristics including income, credit score, work type, age, and loan amount, the suggested system incorporates supervised machine learning techniques like Random Forest, Decision Trees, and Logistic Regression. To guarantee robustness and generalization, the model construction process includes data pre-treatment, feature selection, cross-validation, and performance tuning. In comparison to traditional rule-based systems, experimental study shows increased classification accuracy and decreased erroneous approval rates. To guarantee data security and scalability, the system architecture uses a modular multi-tier design with RESTful APIs, secure JWT-based authentication, and role-based access control. Effective administration of structured and document-based records is made possible by hybrid database support utilizing MySQL and MongoDB. The suggested approach greatly lowers manual involvement, improves decision transparency, and offers a solid basis for the future integration of cutting-edge fintech services like real-time credit verification and fraud analytics.