Paper Title
Loan Utilization Tracking System
Article Identifiers
Registration ID: IJNRD_322713
Published ID: IJNRD2604017
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Keywords
Loan tracking, GPS verification, offline-first mobile application, AI validation, financial inclusion, microfinance, rural banking, FastAPI, React Native
Abstract
Microfinance institutions in rural areas face persistent challenges in monitoring loan utilization, ensuring transparency, and reducing fraud. Traditional verification methods, such as manual profiling and physical site visits, are costly, time-consuming, and prone to errors, often resulting in poor financial health and high rates of bad credit. To address these issues, this study proposes a mobile-based loan utilization tracking system that integrates three key components: offline-first mobile architecture, GPS verification, and AI-powered media validation. The research adopted a descriptive quantitative methodology, combining system development with pilot testing. Data was collected from 50 rural beneficiaries and cooperative officers over a 30-day period. The system was implemented using a client-server architecture, with a React Native mobile application as the frontend and a FastAPI–MongoDB backend as the server. GPS coordinates were automatically captured with each media upload, while lightweight AI models validated image authenticity and quality. Evaluation metrics included upload success rate, synchronization reliability, AI validation accuracy, fraud detection efficiency, and usability scores. The results demonstrate significant improvements compared to traditional systems. The offline-first design achieved a 95.3% upload success rate, far exceeding the 60% success rate of online-only systems. AI validation achieved 96.9% GPS verification accuracy and 99.2% precision in image quality detection, while fraud detection efficiency improved by 67% compared to manual-only review. Usability testing yielded an overall System Usability Scale (SUS) score of 73.4, with officers rating the system highest due to time savings and improved oversight. These findings confirm that the proposed system enhances transparency, reduces verification costs, and strengthens trust in loan programs, thereby contributing to financial inclusion in rural contexts. The study also highlights the potential of lightweight AI and offline-first mobile architectures to overcome connectivity barriers and fraud risks in resource-constrained environments. The implications of this research extend to microfinance institutions, policymakers, and technology providers. For institutions, the system offers a scalable solution to reduce operational costs and improve efficiency. For policymakers, GPS-verified digital tracking supports accountability in government loan schemes. For technology providers, the study demonstrates the viability of integrating AI validation and offline-first design without dependence on costly cloud services. This research contributes to the growing literature on digital financial inclusion and opens avenues for future work, including large-scale deployment, advanced fraud detection techniques, inclusive design for digitally inexperienced users, and integration with broader financial ecosystems.
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How To Cite (APA)
Varnika Tyagi, Srashti Agarwal, Sakshi Kumari, Aditi Saini, & Prof. Arvind Panwar (April-2026). Loan Utilization Tracking System. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), a178-a183. https://ijnrd.org/papers/IJNRD2604017.pdf
Issue
Volume 11 Issue 4, April-2026
Pages : a178-a183
Other Publication Details
Paper Reg. ID: IJNRD_322713
Published Paper Id: IJNRD2604017
Research Area: Other area not in list
Author Type: Indian Author
Country: Ghaziabad, Uttar Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2604017.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2604017
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