AI4Contracts: LLM & RAG-Powered Encoding of Financial Derivative Contracts
Maruf Ahmed Mridul, Ian Sloyan, Aparna Gupta, Oshani Seneviratne
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 schemas. 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.
BibTeX
@inproceedings{ijcai2025_ai4contractsllma,
title = {AI4Contracts: LLM & RAG-Powered Encoding of Financial Derivative Contracts},
author = {Maruf Ahmed Mridul and Ian Sloyan and Aparna Gupta and Oshani Seneviratne},
booktitle = {IJCAI 2025},
year = {2025}
}