Monitoring LLM Uncertainty in Financial Services on AWS

This article demonstrates how to operationalize conformal prediction on AWS to monitor LLM uncertainty in financial services, enabling label-free detection of unreliable predictions and automated human-in-the-loop routing.

• Conformal prediction transforms classifier outputs into prediction sets with guaranteed coverage, expanding when uncertain
• Average prediction-set size serves as a label-free monitoring metric tracked in CloudWatch without ground-truth labels
• Solution uses Least Ambiguous set-valued Classifiers (LAC) method with offline calibration and online inference routing
• Architecture integrates SageMaker endpoints, DynamoDB for thresholds, CloudWatch for metrics, and SQS for human review
• Demonstration on MMLU dataset shows coverage maintained at 90%+ while set size spikes from 1.6 to 2.3 on difficult questions
• Supports regulatory requirements including SR 11-7, EU AI Act, and UK SS1/23 model risk management frameworks
• Enables financial services use cases: credit risk rating, AML triage, insurance claims, regulatory filing classification

Conformal prediction provides statistical rigor for LLM reliability monitoring while AWS infrastructure enables enterprise-scale operationalization with automated uncertainty detection and human oversight.