Deterministic Graph-Based Inference for Financial AI Compliance

This technical report discusses the implementation of deterministic graph-based inference as a method for ensuring compliance and control in financial AI systems, particularly in the context of large language models (LLMs). It outlines the evolution of AI reasoning systems, highlighting the shift from deterministic to probabilistic models and the emerging hybrid approach that combines the strengths of both paradigms. The report details the risks associated with LLMs in financial services, including issues of non-determinism, hallucinations, bias, and operational risks. It emphasizes the need for stronger AI guardrails in regulated environments and presents deterministic graph-based inference as a solution that provides explainability, consistency, and adherence to regulatory requirements. The methodology allows organizations to encode legal and regulatory expertise into systems, ensuring that AI-generated outputs are traceable and compliant. The report concludes by contrasting deterministic inference with other approaches to LLM guardrailing, underscoring its robustness for financial applications.