{"id":6172,"date":"2026-09-07T18:08:53","date_gmt":"2026-09-07T12:38:53","guid":{"rendered":"https:\/\/blogs.infosys.com\/emerging-technology-solutions\/?p=6172"},"modified":"2026-09-07T18:08:53","modified_gmt":"2026-09-07T12:38:53","slug":"llm-as-a-judge-to-use-models-to-review-and-score-outputs-for-quality-in-banking","status":"publish","type":"post","link":"https:\/\/blogs.infosys.com\/emerging-technology-solutions\/artificial-intelligence\/llm-as-a-judge-to-use-models-to-review-and-score-outputs-for-quality-in-banking.html","title":{"rendered":"LLM as a judge to use models to review and score outputs for quality in Banking"},"content":{"rendered":"

How Leading Banks Can Use AI Judges to Review, Score, and Govern AI Outputs for Quality<\/h4>\n

Core idea: LLM-as-a-Judge turns AI governance from sampling and manual checks into a scalable, rubric-based assurance layer. In banking, it is most valuable when it is calibrated to human experts, grounded in approved sources, logged for auditability, and reserved for augmenting – not replacing – accountable human oversight.<\/strong><\/em><\/p>\n

Contents<\/h4>\n

1. Executive Summary<\/p>\n

2. What LLM-as-a-Judge Means in Banking<\/p>\n

3. Banking Use Cases and Evaluation Rubrics<\/p>\n

4. Peer and Market Signals<\/p>\n

5. Comparative Snapshot<\/p>\n

6. Overall POV: Implications for Bank of America<\/p>\n

7. Full Source List<\/p>\n

Executive Summary<\/h4>\n

LLM-as-a-Judge<\/strong> is the practice of using one model to evaluate the output of another AI system against defined criteria such as correctness, relevance, groundedness, safety, bias, tone, and compliance. For banking, this is not just an AI-quality technique; it is becoming a governance requirement for scaling generative and agentic AI safely. Traditional human review remains essential, but it cannot economically or operationally review every customer interaction, advisor answer, regulatory summary, code-generation output, or agent action. A calibrated judge layer provides always-on assessment, produces scores and rationales, and flags high-risk cases for human review.<\/p>\n

Why this matters now:<\/strong> financial institutions are moving from experimental GenAI to production usage across service, risk, compliance, operations, technology, and employee productivity. McKinsey notes that GenAI increases complexity because it creates new content through multi-step processes, uses public and private data, and introduces legal, data, technology, and model-risk exposures that conventional AI governance was not designed to manage. Its latest MRM research also highlights that banks are expanding model governance for machine learning, LLMs, GenAI, and agentic AI, while continuous monitoring and use-case-level oversight are becoming central to the model lifecycle.<\/p>\n

Our POV:<\/strong> Bank of America should treat LLM-as-a-Judge as a controlled assurance layer for AI outputs, not as a standalone automation shortcut. The near-term opportunity is to build a reusable evaluation fabric for AI experiences – including internal copilots, customer-service assistants, advisor-support tools, code assistants, RAG knowledge systems, and agentic workflows. The differentiator will not be the judge model alone; it will be the bank-specific rubrics, golden datasets, traceability, human calibration, escalation playbooks, and audit evidence that make the scores regulator-ready and business-actionable.<\/p>\n

Bank of America is, in a specific and useful sense, the client this technology was built for. Few institutions generate as much AI output that needs continuous, defensible quality checking: Erica has logged more than 3.2 billion client interactions since 2018, EricaAssist now supports over 18,000 customer service representatives with real-time guidance delivered in under three seconds, and ask MERRILL and ask Private Bank together handle roughly 23 million advisor-facing interactions a year. Each of these surfaces produces exactly the kind of high-volume, open-ended output that rule-based checks and manual review cannot keep up with, and that LLM-as-a-judge was designed to score.<\/p>\n

What LLM-as-a-Judge Means in Banking<\/h4>\n

At its simplest, an LLM judge receives the original prompt, the AI-generated answer or action, source context when available, and a scoring rubric. It returns a score, label, or pass\/fail decision, often with a written rationale. In a banking environment, this can be used to test AI before release, compare prompt or model versions, monitor live production traces, and create an exception queue for compliance, risk, operations, or model-validation teams.<\/p>\n

The framework is especially relevant to banking because many GenAI outputs cannot be evaluated with old-style metrics. BLEU, ROUGE, exact match, and static test sets are weak proxies for whether an answer is grounded in bank policy, appropriate for a customer segment, aligned to regulatory language, free from PII leakage, or safe for an employee to act on. FINOS describes LLM-as-a-Judge as a detective control that can support verification, validation, ongoing monitoring, issue detection, performance monitoring, and targeted human review.<\/p>\n

The banking design principle should be hybrid evaluation. AI judges should handle volume, consistency, regression checks, and first-pass triage; humans should own calibration, high-risk decisions, edge-case interpretation, and accountability. Google Cloud and AIR recommend controls for GenAI in financial services that include continuous monitoring, robust testing protocols, grounding and outcome-based evaluations, documentation expectations, and human-in-the-loop oversight.<\/p>\n

Banking Use Cases and Evaluation Rubrics<\/h4>\n

1. Customer Service and Virtual Assistants:<\/strong> Evaluate AI-generated customer responses for factual accuracy, policy adherence, tone, empathy, escalation need, prohibited advice, and completeness. This applies to chat, voice, branch support, contact-center agent assist, and complaint triage.<\/p>\n

2. RAG-Based Knowledge Search:<\/strong> Score whether answers are grounded in approved internal documents, cite the correct policy, avoid unsupported claims, and answer the actual user intent. This is critical for employee copilots, operations helpdesks, and wealth or banking knowledge assistants.<\/p>\n

3. Regulatory and Compliance Summaries:<\/strong> Assess whether generated summaries correctly reflect regulatory text, preserve obligations, avoid over-generalization, and separate facts from interpretation. Human legal and compliance reviewers should validate flagged or high-impact outputs.<\/p>\n

4. Credit, Risk, Fraud, and AML Analytics Narratives:<\/strong> Judge narrative explanations, case summaries, suspicious-activity rationales, and risk commentary for completeness, traceability, non-discrimination, and alignment to approved risk language.<\/p>\n

5. Software Development and Code Assistants:<\/strong> Review AI-generated code, test cases, documentation, and pull-request explanations against enterprise coding standards, security rules, maintainability, and architectural constraints.<\/p>\n

6. Agentic Workflows and Tool Use:<\/strong> Evaluate whether agents selected the right tools, followed the approved workflow, respected limits, avoided redundant calls, and produced an outcome that is safe, compliant, and auditable.<\/p>\n

Peer and Market Signals<\/h4>\n
Wells Fargo<\/h5>\n

How AI is helping in the current stage:<\/strong> Wells Fargo researchers have proposed LLM Jury-on-Demand, a dynamic approach that selects reliable judges for each item and weights their scores based on predicted reliability. The research is directly relevant to high-stakes domains because it addresses the limitation of relying on a single judge model and seeks stronger alignment with human expert evaluation.<\/p>\n

Our POV:<\/strong> The signal for banks is to move beyond point evaluations and make judge-based evaluation part of the operating model: independent, rubric-driven, production-aware, and integrated with human escalation.<\/p>\n

Capital One<\/h5>\n

How AI is helping in the current stage:<\/strong> Capital One has publicly described production multi-agent AI workflows where an independent evaluator agent checks plans against company policy and sends non-compliant plans back for correction. This pattern is the closest publicly visible banking example of judge-like evaluation embedded inside an AI runtime rather than applied only after the fact.<\/p>\n

Our POV:<\/strong> The signal for banks is to move beyond point evaluations and make judge-based evaluation part of the operating model: independent, rubric-driven, production-aware, and integrated with human escalation.<\/p>\n

Citigroup<\/h5>\n

How AI is helping in the current stage:<\/strong> Citi has applied GenAI to regulatory analysis, including reviewing large bodies of new capital-rule text, and has board-level technology oversight of GenAI and machine-learning risks. The implication for LLM-as-a-Judge is that judge outputs for regulatory and compliance use cases must be explainable, traceable, and governed at a senior accountability level.<\/p>\n

Our POV:<\/strong> The signal for banks is to move beyond point evaluations and make judge-based evaluation part of the operating model: independent, rubric-driven, production-aware, and integrated with human escalation.<\/p>\n

Goldman Sachs<\/h5>\n

How AI is helping in the current stage:<\/strong> Goldman Sachs has emphasized centralized AI tooling and independent model-risk practices. A centralized AI access layer can make LLM-as-a-Judge easier to scale because prompts, responses, traces, policies, and evaluation scores can be captured consistently across use cases rather than fragmented across business units.<\/p>\n

Our POV:<\/strong> The signal for bank is to move beyond point evaluations and make judge-based evaluation part of the operating model: independent, rubric-driven, production-aware, and integrated with human escalation.<\/p>\n

JPMorgan Chase<\/h5>\n

How AI is helping in the current stage:<\/strong> JPMorgan Chase represents the scale problem: large banks running many AI use cases need continuous, automated, risk-tiered evaluation rather than periodic manual sampling. LLM-as-a-Judge can act as a common quality-control layer across customer-facing, internal productivity, code, risk, and compliance applications, provided it is independently calibrated.<\/p>\n

Our POV:<\/strong> The signal for banking industry is to move beyond point evaluations and make judge-based evaluation part of the operating model: independent, rubric-driven, production-aware, and integrated with human escalation.<\/p>\n

Comparative Snapshot: LLM-as-a-Judge in Banking<\/h4>\n

\"\"\"\"<\/p>\n

Overall POV: Implications for Bank of America<\/h4>\n

1. Build a banking Evaluation Fabric:<\/strong> Create a reusable LLM-as-a-Judge service that covers rubric management, scoring, rationale capture, prompt\/model version tracking, production sampling, and audit logs across AI use cases.<\/p>\n

2. Start with Three High-Value Pilots:<\/strong> Prioritize customer-service QA, internal policy\/RAG assistant evaluation, and developer-code-assistant review. These offer high volume, clear rubrics, and enough human feedback to calibrate judges safely.<\/p>\n

3. Use Bank-Specific Rubrics:<\/strong> Do not rely on generic helpfulness scores. Define rubrics for factual grounding, policy adherence, fair-treatment language, prohibited advice, PII exposure, escalation need, evidence quality, and tone.<\/p>\n

4. Calibrate Judges Against Experts:<\/strong> Benchmark judge scores against SMEs from compliance, risk, operations, legal, technology, and customer experience. Track agreement, false positives, false negatives, drift, and model-version sensitivity.<\/p>\n

5. Implement Risk-Tiered Escalation:<\/strong> Low-risk outputs can be batch-scored; medium-risk outputs should be sampled and monitored; high-risk or customer-impacting outputs should trigger human review, approval, or blocked delivery.<\/p>\n

6. Keep the Judge Independent:<\/strong> Separate the generator model and evaluator model wherever feasible. Use ensemble or multi-judge designs for high-stakes cases to reduce self-preference, vendor lock-in, and single-model bias.<\/p>\n

7. Make Evaluation Evidence Regulator-Ready:<\/strong> Store prompt, response, source context, judge rubric, score, rationale, model version, reviewer decision, and remediation outcome so every AI judgment can be reconstructed during audit or examination.<\/p>\n

Full Source List<\/h4>\n