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Paper ID:IJSREM63822

Enhancing Credit Literacy and Loan Eligibility Accuracy in Emerging Markets through Large Language Model Integration: The Credit Smart Framework

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63822

Volume:

10 |

5 |

May 2026

Pages:

1-5

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

OB

Omkar Bhondve

Computer Science Engineering-Artificial Intelligence and Analytics

MIT ADT UNIVERSITY LONI KALBHOR PUNE

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SJ

Sachin Jaybhaye

Computer Science Engineering-Artificial Intelligence and Analytics

MIT ADT UNIVERSITY LONI KALBHOR PUNE

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TD

Tanishq Daundkar

Computer Science Engineering-Artificial Intelligence and Analytics

MIT ADT UNIVERSITY LONI KALBHOR PUNE

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RY

Raman Yerrabalam

Computer Science Engineering-Artificial Intelligence and Analytics

MIT ADT UNIVERSITY LONI KALBHOR PUNE

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Mentors

PAB

Prof Ashish Bhasme

Computer Science Engineering-Artificial Intelligence and Analytics

MIT ADT UNIVERSITY LONI KALBHOR PUNE

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Abstract

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

Index Terms: Active Financial Context (AFC) Attribute-Based Access Control (ABAC) CIBIL Credit Scoring Debt-to-Income (DTI) Ratio Financial Inclusion Financial Literacy FinTech Generative AI Google Gemini Large Language Models (LLMs) Loan Eligibility Risk Profiling.

Cite This Article

Omkar Bhondve, Sachin Jaybhaye, Tanishq Daundkar, Raman Yerrabalam, Prof Ashish Bhasme, "Enhancing Credit Literacy and Loan Eligibility Accuracy in Emerging Markets through Large Language Model Integration: The Credit Smart Framework," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-5, May 2026.

Full Article/Conclusion

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.

References

[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.

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Publication Date

5/23/2026

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Review Rounds: 2

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