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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.02165 |
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| _version_ | 1866916672555712512 |
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| author | Tavasoli, Ahmadreza Sharbaf, Maedeh Madani, Seyed Mohamad |
| author_facet | Tavasoli, Ahmadreza Sharbaf, Maedeh Madani, Seyed Mohamad |
| contents | Financial institutions of all sizes are increasingly adopting Large Language Models (LLMs) to enhance credit assessments, deliver personalized client advisory services, and automate various language-intensive processes. However, effectively deploying LLMs requires careful management of stringent data governance requirements, heightened demands for interpretability, ethical responsibilities, and rapidly evolving regulatory landscapes. To address these challenges, we introduce a structured six-decision framework specifically designed for the financial sector, guiding organizations systematically from initial feasibility assessments to final deployment strategies.
The framework encourages institutions to: (1) evaluate whether an advanced LLM is necessary at all, (2) formalize robust data governance and privacy safeguards, (3) establish targeted risk management mechanisms, (4) integrate ethical considerations early in the development process, (5) justify the initiative's return on investment (ROI) and strategic value, and only then (6) choose the optimal implementation pathway -- open-source versus proprietary, or in-house versus vendor-supported -- aligned with regulatory requirements and operational realities. By linking strategic considerations with practical steps such as pilot testing, maintaining comprehensive audit trails, and conducting ongoing compliance evaluations, this decision framework offers a structured roadmap for responsibly leveraging LLMs. Rather than acting as a rigid, one-size-fits-all solution, it shows how advanced language models can be thoughtfully integrated into existing workflows -- balancing innovation with accountability to uphold stakeholder trust and regulatory integrity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Responsible Innovation: A Strategic Framework for Financial LLM Integration Tavasoli, Ahmadreza Sharbaf, Maedeh Madani, Seyed Mohamad Computational Engineering, Finance, and Science Financial institutions of all sizes are increasingly adopting Large Language Models (LLMs) to enhance credit assessments, deliver personalized client advisory services, and automate various language-intensive processes. However, effectively deploying LLMs requires careful management of stringent data governance requirements, heightened demands for interpretability, ethical responsibilities, and rapidly evolving regulatory landscapes. To address these challenges, we introduce a structured six-decision framework specifically designed for the financial sector, guiding organizations systematically from initial feasibility assessments to final deployment strategies. The framework encourages institutions to: (1) evaluate whether an advanced LLM is necessary at all, (2) formalize robust data governance and privacy safeguards, (3) establish targeted risk management mechanisms, (4) integrate ethical considerations early in the development process, (5) justify the initiative's return on investment (ROI) and strategic value, and only then (6) choose the optimal implementation pathway -- open-source versus proprietary, or in-house versus vendor-supported -- aligned with regulatory requirements and operational realities. By linking strategic considerations with practical steps such as pilot testing, maintaining comprehensive audit trails, and conducting ongoing compliance evaluations, this decision framework offers a structured roadmap for responsibly leveraging LLMs. Rather than acting as a rigid, one-size-fits-all solution, it shows how advanced language models can be thoughtfully integrated into existing workflows -- balancing innovation with accountability to uphold stakeholder trust and regulatory integrity. |
| title | Responsible Innovation: A Strategic Framework for Financial LLM Integration |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2504.02165 |