Research on an Intelligent Trust Contract Review Framework Based on Large Language Models and Neural Symbolic Reasoning

Abstract

With the continuous deepening of the digital and intelligent transformation in the trust industry, the asset management scale of trust companies has significantly increased. The traditional method of relying on manual review of trust contracts is facing severe challenges such as high time consumption, high error rate, and inconsistent standards. To enhance the accuracy and efficiency of risk control review for trust contracts, this paper constructs a Trust‑DRNS integrated framework for intelligent review of trust contracts, combining large language models with neural symbolic reasoning technology. Given the characteristics of trust contracts, such as long text, interrelated clauses, and high compliance requirements, this framework first builds a knowledge base for the review of trust business contracts. Using the large language model combined with industry knowledge, dynamic generation of natural language and semiotic review rules; and a neural‑symbolic reasoning hybrid chain of “local fact extraction ‑ symbolic matching positioning ‑ symbolic reduction and semantic execution” is constructed to conduct reasoning and judgment on contract ambiguous clauses, rights and responsibilities conflicts, and compliance risks, with interpretability and auditability. The experimental results show that compared with the baseline method of the general large language model, this system has increased the F1 value for trust contract risk identification by more than 8.3%, improved the accuracy rate of risk levels by more than 8.7%, and helps lower the compliance missed detection rate. This research provides a “flexible and tough” intelligent risk control solution for the trust industry, with practical and promotional value.