DESIGN AND IMPLEMENTATION OF AN INTELLIGENT LOAN DEFAULT RISK SYSTEM

In today’s increasingly data-driven financial landscape, accurate loan default prediction is essential to reducing risk and improving decision-making for lending institutions. This project presents the design and implementation of an intelligent loan default prediction system leveraging machine learning (ML) algorithms. Traditional credit scoring systems, though widely adopted, are limited by their inability to process large volumes of diverse borrower data and often rely on static, rule-based logic that lacks flexibility and adaptability to current economic realities. To address these limitations, this systematic l was developed to analyze historical loan data and generate real-time predictions on the likelihood of a borrower defaulting. The system is designed with a three-tier architecture comprising a Streamlit-based user interface, a Python-driven backend with preprocessing and prediction logic, and a CSV-based dataset for storage. Core modules of the system include borrower data collection, feature engineering, model training, and real-time prediction display. The dataset used was obtained from public lending records, cleaned and transformed using standard preprocessing techniques, and then trained using a machine learning algorithm due to its superior performance on structured financial data. The model was evaluated based on accuracy, interpretability, and responsiveness. It demonstrated high performance in identifying high-risk loan applications with minimal false positives. The project also includes a visual interface that displays input fields and prediction results, making it accessible to users with little or no technical background. This research contributes to the field by demonstrating how a lightweight, accessible, and intelligent system can be developed to support smarter lending decisions and reduce non-performing loans in the financial sector.