Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64259
Volume:
10 |
5 |
May 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Masters of Information Technology
D. G. Ruparel
The FinTech project is an AI-based financial management and loan eligibility system developed to support people from rural areas and individuals with limited knowledge of banking and credit systems. The system helps users manage their daily financial activities such as income, expenses, and savings through a simple digital platform. Based on the collected financial data, the system uses Artificial Intelligence and Machine Learning techniques to analyze financial behavior and predict loan eligibility. The project also aims to spread awareness about government loan schemes and financial support programs that can benefit underserved communities. By simplifying financial management and improving access to credit services, the system promotes financial inclusion and contributes towards achieving SDG 1: No Poverty and SDG 8: Decent Work and Economic Growth. Overall, the project provides a smart, user-friendly, and scalable solution that bridges the gap between technology and financial accessibility for rural populations.
KEYWORDS
The implementation and evaluation of different machine learning models demonstrated that AI- based systems can effectively predict loan eligibility using users’ daily financial data. Among the models tested, Random Forest achieved the best overall performance in terms of accuracy and reliability, while Logistic Regression and Decision Tree also provided efficient and interpretable results. The FinTech system successfully helps users manage their finances digitally and supports financial inclusion by assisting rural and underserved communities in understanding loan opportunities and government financial schemes. Overall, the project proves that Machine Learning and FinTech technologies can simplify financial management and improve access to credit services.
· Bishop, C. M., Pattern Recognition and Machine Learning, Springer Publication. · Géron, Aurélien, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly Media. · Han, J., Kamber, M., and Pei, J., Data Mining: Concepts and Techniques, Morgan Kaufmann Publication. · Research papers on AI-based Loan Eligibility Prediction from IEEE Xplore Digital Library. · Research articles on Financial Inclusion and FinTech Technologies from Springer and Elsevier journals. · Flask Official Documentation: https://flask.palletsprojects.com · Kaggle Datasets for Loan Prediction and Financial Analysis: https://www.kaggle.com · GeeksforGeeks Tutorials on Machine Learning and Python Programming: https://www.geeksforgeeks.org · Towards Data Science Articles on AI and Financial Prediction: https://towardsdatascience.com · United Nations Sustainable Development Goals (SDGs): https://sdgs.un.org/goals · Government Financial Scheme Information Portals and Digital Banking Resources.
Publication Date
5/29/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
2
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...