AI4Contracts: LLM&RAG-Powered Encoding of Financial Derivative Contracts

2025-06-01

AI Summary

Converting over-the-counter (OTC) financial derivatives contracts into the standardized Common Domain Model (CDM) remains challenging due to the unstructured, heterogeneous nature of legal text. Method: This paper introduces CDMizer, the first template-driven, hierarchical generation framework for CDM structuring. It integrates deep-aware retrieval with large language models (LLMs) and employs hierarchical structured output generation, grammar-constrained templates, and schema-consistency verification to achieve high-fidelity encoding from raw legal text to CDM. Contribution/Results: We propose an LLM-powered automated evaluation framework supporting dual validation—semantic accuracy and structural completeness. Experiments on real-world OTC contracts demonstrate that CDMizer significantly improves both CDM encoding precision and scalability. The framework establishes a robust foundation for AI-driven contract understanding, synthesis, and automated regulatory compliance verification.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) are reshaping how AI systems extract and organize information from unstructured text. A key challenge is designing AI methods that can incrementally extract, structure, and validate information while preserving hierarchical and contextual relationships. We introduce CDMizer, a template-driven, LLM, and RAG-based framework for structured text transformation. By leveraging depth-based retrieval and hierarchical generation, CDMizer ensures a controlled, modular process that aligns generated outputs with predefined schema. Its template-driven approach guarantees syntactic correctness, schema adherence, and improved scalability, addressing key limitations of direct generation methods. Additionally, we propose an LLM-powered evaluation framework to assess the completeness and accuracy of structured representations. Demonstrated in the transformation of Over-the-Counter (OTC) financial derivative contracts into the Common Domain Model (CDM), CDMizer establishes a scalable foundation for AI-driven document understanding, structured synthesis, and automated validation in broader contexts.

Problem

• Extracting structured data from unstructured financial contracts
• Preserving hierarchical relationships in AI-processed documents
• Validating accuracy of machine-generated structured representations

Innovation

• LLM and RAG for structured text transformation
• Template-driven framework ensures schema adherence
• Depth-based retrieval maintains hierarchical relationships