LLM-as-a-Judge: The Missing Piece in Financial Services' AI Governance

The AI Oversight Challenge in Financial Services

Financial services institutions face a critical tradeoff: they need to embrace generative AI to remain competitive, but they also operate in one of the most heavily regulated industries, where accuracy, compliance, and risk management cannot be compromised.

The numbers tell the story: According to McKinsey, banks implementing generative AI can realize a potential value of $200-$340 billion annually. Yet the same institutions face astronomical costs for compliance failures, with regulatory fines in banking exceeding $400 billion since 2008.

This creates an urgent question: How can financial institutions deploy generative AI at scale while maintaining the stringent oversight needed to satisfy regulators, protect customers, and prevent costly errors?

Beyond Human-Only Review: A New Approach

Traditional approaches to AI governance rely heavily on human review. While essential, this approach creates three critical bottlenecks:

1. Scale limitations: With enterprise GenAI applications generating thousands of responses per hour, human-only review becomes physically impossible
2. Consistency challenges: Different reviewers apply different standards, creating compliance vulnerabilities
3. Speed constraints: Manual review processes dramatically slow deployment and iteration

Forward-thinking financial institutions have recognized that human oversight alone cannot scale with enterprise AI adoption. Instead, they're implementing a layered approach in which AI systems evaluate other AI systems, with humans providing strategic oversight of the process—not reviewing every individual output.

The Limitations of Traditional Evaluation Approaches for LLMs

Financial institutions have historically relied on straightforward evaluation metrics like BLEU and ROUGE for text-based models. These metrics perform well for structured tasks with clear-cut answers, such as machine translation or document summarization, where lexical overlap often correlates with quality. However, they frequently fall short when applied to the open-ended, generative nature of large language models(LLMs) for three key reasons:

• Lack of semantic understanding: Traditional metrics focus on word-level similarity rather than meaning. They fail to recognize when different phrases convey the same accurate information, a frequent occurrence with LLMs that generate natural, varied language.
• Inability to capture contextual nuance: LLMs excel at providing contextually appropriate, nuanced responses that may differ significantly from any pre-defined "correct" answer while still being accurate and valuable. Conventional metrics tend to penalize this flexibility rather than reward it.
• Insensitivity to domain-specific requirements: Financial services involve complex regulatory requirements, nuanced compliance language, and industry-specific accuracy standards that simple word-matching metrics are not equipped to evaluate.

As a result, many institutions have resorted to extensive human review to assess LLM output, an increasingly unsustainable bottleneck as AI adoption scales. Without purpose-built evaluation frameworks tailored to LLMs, financial organizations face a difficult trade-off: either constrain AI adoption to what manual review can support or risk lapses in compliance, accuracy, and customer trust.

LLM-as-a-Judge: From Theory to Practice

LLM-as-a-Judge has rapidly evolved from a theoretical concept to essential infrastructure at leading financial institutions. The approach uses a dedicated large language model to evaluate the outputs of operational AI systems against predefined criteria, checking for accuracy, compliance, bias, and alignment with business rules.

Deloitte reports that major global banks are already implementing this approach, with their risk advisors having "carried out LLM validations for several global banks" as part of evolving model validation frameworks.

How to Create LLM-as-a-Judge

Why Financial Institutions are Adopting LLM-as-a-Judge

Several converging factors explain why leading FSIs consider LLM-as-a-Judge essential:

1. Regulatory Expectations Are Evolving

New AI regulations explicitly require robust validation and monitoring systems. The EU AI Act and guidance from the UK's Prudential Regulation Authority (PRA) both mandate that AI systems—including LLMs—be treated as "models" subject to comprehensive validation and governance.

Traditional validation approaches focused primarily on a model's internal mathematics and straightforward error calculations. With increased model capabilities comes increased model complexities. The demand to document and explain these complexities is at the forefront of regulation standards, such as Federal Reserve Memo SR11-7 on Model Risk Management. Today's standards require validating the model's outputs and behavior in context, a more complex requirement that demands automation to achieve sufficient coverage.

2. The Volume Challenge Makes Automation Essential

The sheer volume of AI content generation makes manual review impossible beyond minimal sampling. RAG models generate thousands of responses per hour, making comprehensive human quality checking infeasible. LLM-as-a-Judge provides continuous, automated validation, instantaneously flagging issues for human review within a high-volume model output stream.

3. Research Shows LLM Judges Can Match Human Evaluation

Recent research demonstrates that advanced judges can match human evaluators on many assessment tasks. A 2023 NeurIPS study found that "strong LLM judges like GPT-4 can match both controlled and crowdsourced human preferences well, achieving over 80% agreement—the same level of agreement as between humans."

A properly configured LLM judge can approximate human oversight reliably for many use cases, which is crucial for maintaining high standards at scale.

LLM-as-a-Judge Scoring Approaches

Addressing the "Models Grading Models" Concern

Some leaders express legitimate concern about whether models should evaluate other models. This skepticism typically centers on three questions:

"Isn't this just the blind leading the blind?"

This concern misunderstands the implementation architecture. In effective LLM-as-a-Judge systems:

• Determinism: The evaluator model is specifically optimized for deterministic binary assessment, not generation
• Ensemble Method: Multiple evaluation models can be used in an ensemble to reduce single-model bias
• Human Escalation: Human reviewers calibrate the system and handle escalated cases
• Intentional System Separation: The evaluation process is distinctly separate from generation, and provides material insights to drive better generation

Rather than "blind leading the blind," it's more accurate to think of this as "specialized quality control AI" overseeing "specialized customer-facing AI."

"How can we trust the evaluator model?"

There are multiple best practices to apply. Trust is built through:

• Empirical validation: Financial institutions can directly measure how well the judge aligns with human experts before deployment
• Controlled scope: The judge evaluates against specific, predefined criteria
• Transparent reasoning: Advanced techniques ensure the judge provides clear explanations for its assessments
• Risk-based approaches: Higher-risk outputs receive more scrutiny, potentially including both AI and human review

Examples of LLM-as-a-Judge in RAG

"What happens when both models are wrong?"

Multi-layered safeguards prevent cascading failures:

• Ensemble approaches use multiple judges to reduce single-model bias
• Different model architectures can be employed for generation versus evaluation
• Pattern recognition identifies systemic issues that require human intervention
• Human oversight remains the ultimate authority in the system

A Hybrid Framework: The Emerging Best Practice

The most successful evaluation implementations don't replace human oversight—they enhance it. Leading financial institutions are creating hybrid governance models that leverage the best of both human and AI evaluation:

• Automated triage, human escalation: AI judges filter the content requiring human attention, allowing compliance teams to focus on exceptions and complex edge cases
• Explainable assessments: Advanced AI judges provide structured, explainable rationales that humans can review, verify, and override if needed
• Continuous improvement: Human reviewers feed corrections back into the system, improving the AI judge over time
• Enhanced accountability: The multi-layered approach demonstrates to regulators that institutions have implemented robust safeguards

This hybrid approach is particularly important for regulatory frameworks that demand robust justification for all model outputs. Due to their complexity, LLMs are challenging to align with the conceptual soundness and transparency expected by SR 11-7 and other regulations.

The Galileo Difference: Purpose-Built Evaluation for Financial Services

Galileo's approach addresses the unique requirements of financial institutions with several advanced capabilities.

ChainPoll: Superior Assessment Accuracy

The ChainPoll Algorithm

Galileo's proprietary ChainPoll method combines chain-of-thought reasoning with multiple prompt polling to evaluate generative AI outputs with exceptional accuracy. In rigorous benchmarks, ChainPoll:

• Beat the next best algorithm by 11%
• Outperformed industry-standard metrics by over 23% on aggregate
• Demonstrated greater compute efficiency
• Provided significantly more explainable evaluations

This means more thorough, transparent, and auditable assessments for financial institutions.

Multi-Judge Ensemble for Robust Oversight

Galileo leverages multiple LLM judges working in concert to reduce the chance of any single model's bias or blind spot affecting evaluation. We also offer multiple providers, and are continually adding more options. Whether you want to leverage OpenAI, Anthropic, Google, AWS Bedrock, or something else, Galileo enables organizations to pick and choose their models of choice and to decide on multiple judges to work in concert. This ensemble approach:

• Mirrors multi-approver human processes
• Operates at machine speed
• Provides redundant oversight to catch potential issues
• Creates a diversity of perspectives in evaluation

Multi-judge approaches consistently beat out open-source single judges and increase the explainability of AI systems, which is crucial for Model Risk Management. With the ability to customize and adapt using Continuous Learning via Human Feedback within the Galileo platform, we offer a customizable and adaptable approach that can enable specialized financial workflows.

The Luna Advantage: Custom Fine-tuned SLMs

Beyond bring-your-own-model LLM-as-judge approaches, Galileo also provides enterprise AI teams with even deeper specialization through custom fine-tuned judges and metrics through our Luna evaluation foundation models. We use LLMs to distill judges into deterministic Luna Evaluation Models, trained to generate specific, numerical scores for your custom use cases, providing both latency and accuracy benefits. Our Luna models offer an alternative set of models to power LLM evaluators when latency and evaluation cost are key considerations. Luna models are particularly beneficial for financial institutions when:

• Adding real-time protection guarding against unsafe or inaccurate outputs or agentic actions
• Seeking to reduce cost of internal monitoring at a large scale

Our new family of deterministic Luna models can power both prebuilt Galileo LLM evaluators and custom LLM evaluators while adapting to your organization’s data. Hosted on Galileo’s proprietary optimized inference engine, and powered using modern GPU hardware for low-cost and low-latency evaluations, Luna models can adapt to hundreds of metrics while remaining deterministic, and offer superior out-of-the-box metrics for agentic evaluation and reliability. This approach enables organizations to leverage both custom fine-tuned LLMs and SLMs as judges. Luna offers:

• Adaptability: Easily customizable with minimal data
• Efficiency: Low latency when running multiple metrics (10-20) simultaneously
• Cost-effectiveness: Lower cost compared to traditional LLM-based evaluation
• Sophisticated agentic metrics: Tool error rate, context adherence, tool selection quality, etc.

Real-Time Safety Safeguards

Beyond both adaptable, multi-judge LLM-as-judge approaches and proprietary, research-backed approaches in ChainPoll and our family of Luna evaluation models, Galileo also offers Galileo Protect, a real-time GenAI firewall. Our platform can intercept disallowed content or insecure behavior as it happens. This creates layered defenses that combine checks for:

• Factual correctness
• Compliance adherence
• Bias detection
• Policy alignment

As part of adding guardrails via Galileo Protect, we offer real-time quantitative evaluation scoring (Classification). These models will run binary classification for different GenAI evaluation metrics used to monitor the performance of an application’s inputs and outputs (e.g. hallucination: yes/no). They can reroute these inputs/outputs in real time depending on the need (e.g. Block all inputs where prompt injection is detected, block all outputs where PII exfiltration is detected).

Across our platform, Galileo enables companies to ensure effective and reliable AI applications by providing the customization, capacity, and protection they need.

From Pilot to Enterprise: A Strategic Implementation Path

For financial institutions considering LLM-as-a-Judge, we recommend a phased approach that builds confidence and demonstrates value:

• Start specific: Apply LLM-as-a-Judge to a defined use case with clear evaluation criteria
• Validate alignment: Compare AI judge results with human expert assessments to demonstrate reliability
• Build evidence: Collect performance data on catch rates, false positives, and time savings
• Expand gradually: Scale to additional use cases based on proven success
• Enhance human capabilities: Use the system to make human reviewers more efficient, not to replace them

With Galileo’s proprietary, research-backed evaluation algorithms and established expertise in customizing approaches for major financial service providers like JP Morgan Chase, we stand ready to be your partner and strategic advisor as you enhance the reliability and efficiency of your AI systems.

Strategic Necessity, Not Optional Enhancement

LLM-as-a-Judge is rapidly becoming a strategic necessity for financial institutions serious about scaling AI safely and efficiently. By combining the speed and consistency of AI evaluation with strategic human oversight, institutions can create governance frameworks that satisfy regulatory demands while enabling innovation.

The question for financial leaders is no longer whether to implement AI evaluation, but how quickly they can deploy it to gain a competitive advantage while maintaining the trust of customers and regulators.

Want to learn how leading financial institutions are implementing LLM-as-a-Judge with Galileo? Contact our team for a confidential consultation.