Abstract: The proposed loan application processing. system for rural areas is specifically designed to address the unique challenges faced by agricultural communities when seeking financial assistance. This system aims to overcome the obstacles inherent to rural settings, ensuring a seamless and effective process for securing crucial financial support.Tailored to the specific needs of rural users, the system commences with farmers initiating the application process through a user-friendly interface designed explicitly for their use. A paramount focus is placed on robust data storage and management, ensuring the secure preservation of loan application forms. Employing advanced missing data imputation techniques enhances the integrity of the datasets. The website design emphasizes user interfaces that are both intuitive and accessible, accommodating varying levels of technological literacy prevalent in rural settings. The assessment of loan eligibility is facilitated by the integration of a machine learning model, carefully considering factors pertinent to agricultural finance. This model is seamlessly deployed locally and integrated via APIs, ensuring adaptability to both local systems and external services. The workflow concludes with a transparent and streamlined loan approval or rejection process, accompanied by insightful financial recommendations for approved applicants. This holistic approach, merging technology, effective data management, and machine learning customized for rural contexts, aspires to diminish the financial inclusion gap in rural areas. Ultimately, the system endeavors to empower farmers, enabling them to secure essential financial resources for sustainable agricultural practices.

Keywords: Machine Learning, Loan, Data, Validation, Document

| DOI: 10.17148/IARJSET.2024.11640