Innovation in Finance: Machine Learning Algorithms for Predicting Loan Eligibility

The financial industry is examined in this research paper, with a particular emphasis on risk management and loan prediction. Three well-known machine learning algorithms-Random Forest, Support Vector Machine, and Gradient Boosting Machine-are compared in this study in order to identify the best model for loan eligibility prediction. Based on performance parameters like accuracy, precision, and recall, each algorithm was thoroughly assessed. According to the results, the Gradient Boosting Machine performed better than the other models on a regular basis and had more robustness and predictive power. Using FastAPI, a high-performance web framework renowned for its effectiveness and simplicity of use, an API for the Gradient Boosting Machine model was created in order to operationalize these insights. Real-time loan eligibility assessments are made easier by the API's smooth connection with a variety of financial platforms. Concurrently, Angular was used to create a user-friendly interface that makes it simple for users to explore and engage with the application. A wide spectrum of users, including financial experts and prospective loan applicants, can access this interface due to its streamlined experience. In order to improve accessibility and usefulness in financial services, the research highlights the significance of combining open-source technologies with user-centric design. The research intends to protect the integrity of risk management procedures in the banking industry while promoting the normalization of financial services by democratizing access to sophisticated predictive technologies. This method encourages more inclusion in financial decision-making processes in addition to increasing the accuracy of loan estimates.