Neuro-Symbolic Compliance: Integrating LLMS and SMT Solvers for Automated Financial Legal Analysis

2025-11-19

2025 2nd IEEE/ACM International Conference on AI-powered Software (AIware)

AI Summary

This work addresses the growing complexity of financial regulations, which hinders the automation of logically consistent and low-intervention compliance processes. The authors propose a neuro-symbolic compliance framework that integrates large language models (LLMs) with SMT solvers: the LLM translates regulatory texts and enforcement cases into formal constraints, while the SMT solver verifies their logical consistency and computes minimal factual modifications to automatically rectify violations. Centered on logic-driven optimization, the approach prioritizes verifiable legal consistency reasoning over post-hoc interpretability. Evaluated on 87 enforcement cases from Taiwan’s Financial Supervisory Commission, the method achieves an 86.2% accuracy in SMT constraint generation, improves reasoning efficiency by over 100-fold, and effectively sustains corrective actions for regulatory violations.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Natural Language Processing: Safety and Robustness
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Financial regulations are increasingly complex, hindering automated compliance-especially the maintenance of logical consistency with minimal human oversight. We introduce a Neuro-Symbolic Compliance Framework that integrates Large Language Models (LLMs) with Satisfiability Modulo Theories (SMT) solvers to enable formal verifiability and optimizationbased compliance correction. The LLM interprets statutes and enforcement cases to generate SMT constraints, while the solver enforces consistency and computes the minimal factual modification required to restore legality when penalties arise. Unlike transparency-oriented methods, our approach emphasizes logic-driven optimization, delivering verifiable, legally consistent reasoning rather than post-hoc explanation. Evaluated on 87 enforcement cases from Taiwan's Financial Supervisory Commission (FSC), the system attains 86.2 % correctness in SMT code generation, improves reasoning efficiency by over $100 \times$, and consistently corrects violations-establishing a preliminary foundation for optimization-based compliance applications.

Problem

• financial compliance
• logical consistency
• automated reasoning
• regulatory complexity
• legal analysis

Innovation

• Neuro-Symbolic Integration
• LLM-SMT Collaboration
• Formal Verifiability
• Optimization-based Compliance
• Legal Reasoning Automation