As financial institutions increasingly adopt Large Language Models (LLMs) to enhance customer experiences and streamline operations, a critical challenge has emerged: how can these powerful but inherently probabilistic systems be deployed safely in a highly regulated environment?

Today, we’re pleased to announce the publication of our latest white paper, “Deterministic Graph-Based Inference for Guardrailing Large Language Models: An Approach to Compliance and Control in Financial AI“, which addresses this exact challenge.

The Problem with LLMs in Financial Services

While LLMs like Claude and GPT bring unprecedented language capabilities to financial services, they come with significant limitations that pose real risks:

• Lack of determinism: The same query can yield different results at different times
• Hallucinations: LLMs can confidently generate entirely false information
• Limited explainability: The “black box” nature makes regulatory compliance difficult
• Vulnerability to prompt injection: Specially crafted inputs can manipulate model behavior

In financial contexts where precision, consistency, and regulatory compliance are non-negotiable, these limitations create substantial barriers to adoption.

The Solution: A Hybrid Approach

This white paper explores how deterministic graph-based inference systems can be integrated with LLMs to create AI solutions that are both powerful and predictable. This hybrid approach combines:

• The linguistic fluency and generative capabilities of LLMs
• The precision, consistency, and explainability of rule-based systems encoded in knowledge graphs

We detail two architectural patterns for implementation:

1. Graph-First Reasoning: Where the deterministic inference engine serves as the primary decision-maker while the LLM acts as an interface layer
2. Post-Generation Validation: Where the LLM generates responses that are subsequently verified and potentially corrected by the symbolic inference engine

The Benefits for Financial Institutions

Financial institutions implementing this hybrid approach can expect:

• Complete transparency and auditability of AI decisions
• Elimination of hallucinations and non-compliant information
• Regulatory compliance by design rather than by hope
• Consistent and reliable responses that build customer trust

Implementation with Rainbird

The paper concludes with a detailed implementation framework leveraging Rainbird’s enterprise-grade knowledge graph reasoning platform. Our approach enables financial institutions to transform complex regulatory frameworks into executable, deterministic systems that can effectively guardrail LLM implementations at scale.

Major banks and financial services firms are already deploying Rainbird to address the critical compliance challenges outlined in this paper, encoding regulatory expertise into verifiable knowledge graphs that ensure AI-generated content remains fully compliant with intricate financial regulations.

Download the White Paper

Ready to explore how your institution can safely harness the power of LLMs while maintaining regulatory compliance? Download our white paper to learn how deterministic graph-based inference can transform your AI strategy.

Courtesy or Notebook LLM there is also a podcast based on this white paper here.

For more information on implementing these solutions in your organisation, contact our team for a consultation.