Abstract
This study aims to improve the prediction of personal loan eligibility through the application of advanced machine learning techniques. Accurate prediction of creditworthiness is crucial for financial institutions to mitigate risks and optimize their lending processes. We evaluated three algorithms: Gradient Boosting, XGBoost, and AdaBoost, using a comprehensive dataset containing demographic and banking information. Among these, XGBoost proved to be the most effective model, achieving an accuracy, precision, recall, and F1 score of 0.95. These results demonstrate XGBoost's superior ability to accurately identify individuals likely to repay loans, making it an invaluable tool for enhancing decision-making in loan approvals. By leveraging XGBoost, banks can reduce the risk of defaults, streamline their operations, and provide better customer service, ultimately leading to more efficient and reliable lending strategies.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ADBA Information Technology and Publishing Limited Company
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
