Bridging the Probabilistic-Deterministic Divide: A Neuro-Symbolic Architecture for Verifiable Regulatory Compliance in Generative Financial Agents

Abstract

The integration of Large Language Models (LLMs) into the financial services sector has precipitated a paradigm shift in customer engagement, algorithmic trading, and automated advisory services. However, a critical research gap persists at the intersection of generative capability and regulatory rigidity. While LLMs exhibit unprecedented fluency and semantic understanding, they fundamentally operate as probabilistic engines, making them inherently ill-suited for the deterministic requirements of financial compliance frameworks such as the European Union’s Markets in Financial Instruments Directive II (MiFID II), the EU AI Act, and the United States Securities and Exchange Commission’s Regulation Best Interest (Reg BI). This research paper identifies and addresses the "Reasoning Gap" in current Conversational AI literature the inability of pure Transformer-based architectures to guarantee logically sound, verifiable, and legally compliant financial advice without hallucination. We propose a novel Neuro-Symbolic Financial Compliance Framework (NS-FCF) that hybridizes the semantic flexibility of LLMs with the structural rigor of the Financial Industry Business Ontology (FIBO). By leveraging logic-enhanced Retrieval-Augmented Generation (RAG) and Program of Thought (PoT) prompting, this framework offers a pathway toward "Verifiable Autonomous Finance," ensuring that AI-generated advice is not only human-like in interaction but mathematically and legally provable in its derivation.