神经符号合规：整合大语言模型与SMT求解器用于自动化金融法律分析
Neuro-Symbolic Compliance: Integrating LLMs and SMT Solvers for Automated Financial Legal Analysis
AI总结：
本研究整合大语言模型与SMT求解器，提出神经符号合规框架，用于自动化金融法律分析，实现形式可验证性和基于优化的合规修正。
摘要：
金融监管正变得日益复杂，阻碍了自动化合规，尤其是需要在最小的人工监督下保持逻辑一致性。我们引入了一个神经符号合规框架，整合了大型语言模型（LLMs）与可满足性模理论（SMT）求解器，以实现形式可验证性和基于优化的合规修正。LLM解读法规和执法案例以生成SMT约束，而求解器则强制一致性并计算在处罚出现时所需最小的事实修改以恢复合法性。与以透明性为导向的方法不同，我们的方法强调逻辑驱动的优化，提供可验证且法律一致的推理，而非事后解释。在台湾金融监督委员会（FSC）的87个执法案例上评估，系统在SMT代码生成的正确性达到86.2%，推理效率提升超过100倍，并持续修正违规，为基于优化的合规应用建立了初步基础。
英文摘要：
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 optimization-based 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 100x, and consistently corrects violations-establishing a preliminary foundation for optimization-based compliance applications.
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