Paper Title
Scalable Fine-Tunning Strategies For LLMs In Finance Domain-Specific Application For Credit Union
Article Identifiers
Registration ID: IJNRD_302425
Published ID: IJNRD2412028
: http://doi.one/10.1729/Journal.42480
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Keywords
Scalable fine-tuning, Large Language Models (LLMs), Credit unions, Regulatory compliance, Operational efficiency, Domain-specific optimization, Bias mitigation.
Abstract
Abstract The study presents scalable fine-tuning strategies for Large Language Models (LLMs) for applications around credit unions dealing with the issues of financial data processing, regulatory compliance, and member interaction. A mixed methods design in the form of qualitative inputs from constituents of credit unions and objective domain-specific fine-tuning approaches built and tested against related metrics are used. Results of this study show categorical improvements: member inquiries increased in accuracy by 27%, compliance by 35%, and bias mitigation by 78%. A parameter-efficient fine-tuning strategy has shown to be scalable across institutions of all sizes with very little computational resources, keeping it accessible to small credit unions. Fine-tuned models have also kept 95% of their general language capabilities while achieving excellence in a domain-specific part, that is, there is a 40% increase in the understanding of financial jargon alone. These were derived from the feedback of 85% of the professional respondents who considered the models "useful" or "very useful," keeping the direct return in operation to about 30% time-saving in handling routine inquiries. This research indicates the kind of revolutionary difference that can be made in this industry through LLMs with domain optimization. Further research will have to address the fine-tuning of such models: integration with new technologies such as blockchain, and extension of other financial applications towards not-so-mainstream levels that can further take LLMs to higher levels of performance and innovation at credit unions.
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How To Cite (APA)
Kartheek Kalluri (December-2024). Scalable Fine-Tunning Strategies For LLMs In Finance Domain-Specific Application For Credit Union. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(12), a267-a274. http://doi.one/10.1729/Journal.42480
Issue
Volume 9 Issue 12, December-2024
Pages : a267-a274
Other Publication Details
Paper Reg. ID: IJNRD_302425
Published Paper Id: IJNRD2412028
Research Area: Science and Technology
Author Type: Foreign Author
Country: Brambleton, VA, United States
Published Paper PDF: https://ijnrd.org/papers/IJNRD2412028.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2412028
Crossref DOI: http://doi.one/10.1729/Journal.42480
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