Predicting Loan Risk in Banking Sectors using Machine Learning

In the world of banking, loan eligibility is essential for ensuring that institutions manage risk effectively while providing customers with the credit they need. This project focuses on optimizing and modernizing the loan eligibility process through the application of advanced machine learning algorithms, especially Artificial Neural Networks. Our objective is to create predictive models that evaluate an applicant's qualification based on their financial and behavioral data. By leveraging the power of ANN s, we will identify the most robust model to validate loan eligibility in real time. For applicants who do not meet the criteria for their desired loan, our system will recommend alternative loan options, increasing their chances of securing credit. This method enhances the applicant's experience while adhering to contemporary banking standards by offering personalized solutions. By automating loan risk analysis, we aim to minimize human bias, save time, and ensure data-driven, objective decision-making. Ultimately, this project will accelerate loan approvals, provide deeper insights into risk management, and enhance customer satisfaction within the financial sector.