Resource-Efficient LLM Application for Structured Transformation of Unstructured Financial Contracts

2025-10-27

AI Summary

This work addresses the challenges of high structural complexity in financial legal contracts (e.g., Credit Support Annexes, CSAs) and the substantial computational overhead and privacy risks associated with reliance on large proprietary language models. We propose a lightweight, automated conversion method built upon an extended CDMizer framework, employing a template-driven strategy integrated with small open-source large language models to generate syntactically correct and schema-compliant structured outputs strictly adhering to the Common Domain Model (CDM) specification. Evaluation on the ISDA benchmark demonstrates that our approach matches the accuracy and processing efficiency of large proprietary models while significantly reducing computational resource requirements and mitigating data leakage risks. Our core contribution is the empirical validation of a resource-efficient, privacy-preserving paradigm for financial contract automation—demonstrating both feasibility and scalability in constrained environments, and establishing a novel framework for compliant smart contract processing under regulatory and infrastructural constraints.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Security and Privacy: Cryptocurrency and smart contracts
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The transformation of unstructured legal contracts into standardized, machine-readable formats is essential for automating financial workflows. The Common Domain Model (CDM) provides a standardized framework for this purpose, but converting complex legal documents like Credit Support Annexes (CSAs) into CDM representations remains a significant challenge. In this paper, we present an extension of the CDMizer framework, a template-driven solution that ensures syntactic correctness and adherence to the CDM schema during contract-to-CDM conversion. We apply this extended framework to a real-world task, comparing its performance with a benchmark developed by the International Swaps and Derivatives Association (ISDA) for CSA clause extraction. Our results show that CDMizer, when integrated with a significantly smaller, open-source Large Language Model (LLM), achieves competitive performance in terms of accuracy and efficiency against larger, proprietary models. This work underscores the potential of resource-efficient solutions to automate legal contract transformation, offering a cost-effective and scalable approach that can meet the needs of financial institutions with constrained resources or strict data privacy requirements.

Problem

• Transforming unstructured financial contracts into machine-readable formats
• Converting complex legal documents into standardized CDM representations
• Achieving resource-efficient automation with smaller open-source LLMs

Innovation

• Template-driven framework ensures CDM schema compliance
• Integrates smaller open-source LLM for competitive performance
• Resource-efficient solution for financial contract transformation