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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM64259

FinTech – AI-Based Daily Finance and Loan Eligibility System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64259

Volume:

10 |

5 |

May 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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.

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Authors

TP

Tanaya Patil

Masters of Information Technology

D. G. Ruparel

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Abstract

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

SDG 8: Decent Work and Economic Growth

Cite This Article

Tanaya Patil, "FinTech – AI-Based Daily Finance and Loan Eligibility System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-7, May 2026.

Full Article/Conclusion

  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.

References

·                  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.

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Publication Date

5/29/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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