Abstract
Credit risk assessment is a fundamental component of financial decision-making in banking and lending institutions. Traditional credit scoring systems rely primarily on static financial metrics and rule-based evaluation methods, which often lack contextual intelligence and adaptability. This paper presents Credit scoring and risk assessment model, an intelligent credit scoring and risk assessment system developed using Large Language Models (LLMs) integrated with AI- based financial analysis and external financial data retrieval. The proposed system combines user financial inputs with real-time financial data retrieved from Yahoo Finance to generate dynamic and explainable risk evaluations. By leveraging contextual retrieval and AI-driven reasoning, the system enhances transparency, adaptability, and analytical depth compared to conventional approaches. Experimental results demonstrate improved interpretability and real-time responsiveness, making the system suitable for modern fintech applications and decision-support systems.
Keywords
Credit Scoring, Risk Assessment, Large Language Models, Retrieval-Augmented Generation, Financial Analytics, Fintech.
Citations
IRE Journals:
Maaz Chaudhary, Prathamesh Kadam, Sarthak Karve, Dr. Geetanjali Kale "AI Based Credit Scoring and Risk Assessment Model for Indian Banks" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 2411-2416 https://doi.org/10.64388/IREV9I10-1716861
IEEE:
Maaz Chaudhary, Prathamesh Kadam, Sarthak Karve, Dr. Geetanjali Kale
"AI Based Credit Scoring and Risk Assessment Model for Indian Banks" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716861
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