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@article{177088,
        author = {Aditya Gajjal and Prof. Supriya Munawar and Prof. Vrushali Wankhede and Abdul Kadir and Sahiloddin Shaikh and Aditya Naik},
        title = {Loan Eligibility Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2448-2454},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177088},
        abstract = {A Loan Eligibility Prediction App is designed to streamline the loan approval process by using machine learning algorithms to predict whether an applicant is eligible for a loan or not. The app collects key information from users, such as income, credit score, employment history of user, and loan amount requested. Using this data, the app applies predictive models trained on historical loan application data to evaluate the applicant's eligibility.
The app helps financial institutions make faster and more accurate lending decisions, reducing manual review time and minimizing the risk of lending to high-risk applicants. Additionally, it provides users with feedback on their inclination of approval, improving their loan application experience. The core functionalities include automated data analysis, real- time prediction, and user-friendly interfaces for both lenders and borrowers.
By leveraging data-driven insights, the Loan Eligibility Prediction App aims to enhance decision-making efficiency and promote financial inclusivity.},
        keywords = {},
        month = {May},
        }