Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63822
Volume:
10 |
5 |
May 2026
Pages:
1-5
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.
Computer Science Engineering-Artificial Intelligence and Analytics
MIT ADT UNIVERSITY LONI KALBHOR PUNE
Computer Science Engineering-Artificial Intelligence and Analytics
MIT ADT UNIVERSITY LONI KALBHOR PUNE
Computer Science Engineering-Artificial Intelligence and Analytics
MIT ADT UNIVERSITY LONI KALBHOR PUNE
Computer Science Engineering-Artificial Intelligence and Analytics
MIT ADT UNIVERSITY LONI KALBHOR PUNE
Computer Science Engineering-Artificial Intelligence and Analytics
MIT ADT UNIVERSITY LONI KALBHOR PUNE
Financial illiteracy remains a critical barrier to equitable credit access in emerging economies such as India, where millions face loan rejections not due to genuine incapacity, but because of misaligned financial profiles and an incomplete understanding of institutional lending criteria. Traditional eligibility tools deliver numerical outputs in isolation, offering neither qualitative reasoning nor actionable guidance. This paper presents CreditSmart, an AI-driven financial risk assessment and advisory platform that bridges the gap between complex banking parameters and borrower comprehension. The system integrates Google Gemini 2.0/2.5 Flash with Debt-to- Income (DTI) ratio computation, CIBIL-inspired credit health modeling, and personalized risk profiling under a unified Active Financial Context (AFC) framework, in which live financial data are injected directly into the large language model (LLM) prompt to generate context- sensitive recommendations. Unlike static calculators, CreditSmart provides interactive credit simulations, dynamic improvement roadmaps, and smart document guidance within a secure Attribute-Based Access Control (ABAC) architecture. Empirical evaluation demonstrates a 65% improvement in perceived platform responsiveness and measurable gains in user financial comprehension. The platform represents a scalable model for next-generation inclusive banking and AI-powered FinTech infrastructure in developing economies.
KEYWORDS
CreditSmart demonstrates that financial literacy platforms must evolve beyond static calculators. By unifying DTI computation, CIBIL-inspired credit health modeling, and LLM-powered personalized advisory under an Active Financial Context architecture, the platform transforms passive financial tools into intelligent, transparent consultants. Empirical evaluation confirms improvements in perceived performance, recommendation relevance, and user financial comprehension—particularly important in emerging economies where the gap between credit availability and credit literacy remains structurally wide. The platform offers a scalable, privacy-preserving model for next- generation inclusive banking systems and AI-powered FinTech infrastructure.
[1] Reserve Bank of India (RBI), "Financial Literacy and Consumer Protection Guidelines," RBI Publications, New Delhi, India, 2025. [2] TransUnion CIBIL, "Understanding Your Credit Score: Consumer Education Guide," CIBIL Consumer Portal, Mumbai, India, 2025. [3] S. Challagundla, B. Chandra, and Y. R. Gogireddy, "Financial Advisory LLM Model for Modernizing Financial Services and Innovative Solutions for Financial Literacy in India," ResearchGate, May 2024. [4] OECD, "Financial Inclusion and Financial Literacy in Emerging Economies," OECD Research Report, Paris, France, 2024. [5] A. Lusardi and O. S. Mitchell, "The Economic Importance of Financial Literacy: Theory and Evidence," J. Econ. Lit., vol. 52, no. 1, pp. 5–44, 2014. [6] H. Yang, X.-Y. Liu, and C. D. Wang, "FinGPT: Open-Source Financial Large Language Models," arXiv preprint arXiv:2306.06031, 2023. [7] A. H. Huang, H. Wang, and Y. Yang, "FinBERT: A Large Language Model for Extracting Information from Financial Text," Contemp. Acc. Res., vol. 40, no. 2, pp. 806–841, 2023. [8] I. de Zarzà, J. de Curtò, G. Roig, and C. T. Calafate, "Optimized Financial Planning: Integrating Individual and Cooperative Budgeting Models with LLM Recommendations," AI, vol. 5, no. 1, pp. 91–114, 2024. [9] S. Mestiri, "Credit Scoring using Machine Learning and Deep Learning-Based Models," Data Sci. Finance Econ., vol. 4, no. 2, pp. 236–248, 2024. [10] Consumer Financial Protection Bureau (CFPB), "What Is a Debt-to-Income Ratio?" CFPB Consumer Guidelines, Washington, DC, Aug. 2023.
Publication Date
5/23/2026
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...