Comparing Open-Source and Commercial LLMs for Domain-Specific Analysis and Reporting: Software Engineering Challenges and Design Trade-offs

2025-09-29

AI Summary

This study addresses the automation of financial reporting, focusing on the comparative suitability of open-source versus commercial large language models (LLMs) for analytical summarization and commentary generation—particularly concerning data compliance, engineering overhead, and system reliability. Method: Adopting a design science research methodology, we develop a multi-agent workflow integrating locally deployed open-source LLMs (e.g., Llama 3) with cloud-based commercial APIs (e.g., GPT-4o), augmented by domain-specific prompt templates, output validation mechanisms, and fault-tolerant architecture tailored to financial semantics. Contribution/Results: Empirical evaluation reveals that open-source LLMs require substantial engineering effort—including fine-tuning, retrieval augmentation, and output calibration—to achieve professional-grade accuracy and operational stability. In contrast, commercial LLMs deliver superior fluency and out-of-the-box performance but introduce material risks related to data privacy leakage and vendor lock-in. The study yields actionable, domain-adapted design principles and empirically grounded guidelines for LLM selection and systematic deployment in regulated verticals.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Context: Large Language Models (LLMs) enable automation of complex natural language processing across domains, but research on domain-specific applications like Finance remains limited. Objectives: This study explored open-source and commercial LLMs for financial report analysis and commentary generation, focusing on software engineering challenges in implementation. Methods: Using Design Science Research methodology, an exploratory case study iteratively designed and evaluated two LLM-based systems: one with local open-source models in a multi-agent workflow, another using commercial GPT-4o. Both were assessed through expert evaluation of real-world financial reporting use cases. Results: LLMs demonstrated strong potential for automating financial reporting tasks, but integration presented significant challenges. Iterative development revealed issues including prompt design, contextual dependency, and implementation trade-offs. Cloud-based models offered superior fluency and usability but raised data privacy and external dependency concerns. Local open-source models provided better data control and compliance but required substantially more engineering effort for reliability and usability. Conclusion: LLMs show strong potential for financial reporting automation, but successful integration requires careful attention to architecture, prompt design, and system reliability. Implementation success depends on addressing domain-specific challenges through tailored validation mechanisms and engineering strategies that balance accuracy, control, and compliance.

Problem

• Comparing open-source and commercial LLMs for financial analysis automation
• Addressing software engineering challenges in domain-specific LLM implementation
• Balancing data privacy, compliance, and usability in financial reporting systems

Innovation

• Multi-agent workflow with local open-source models
• Commercial GPT-4o for superior fluency and usability
• Design balancing accuracy, control, and compliance