A Methodology for Assessing the Risk of Metric Failure in LLMs Within the Financial Domain

TL;DR

The paper addresses the challenge of evaluating GenAI performance in finance, where traditional lab metrics often fail to generalize to production, regulatory, and trust-sensitive environments. It introduces a Risk Assessment Framework that blends SME evaluation with adaptable, domain-specific metrics, and advocates a use-case centric, enterprise-focused evaluation stack that includes continuous monitoring, drift detection, adversarial testing, synthetic data generation, and an OOD detector, complemented by SME judgments and business KPIs. A taxonomy of metric-failure modes across data, model, adversarial, process/annotation, scope, and governance risks is proposed to guide metric selection, monitoring, and mitigations in financial deployments. The approach aims to make AI implementations auditable, compliant, and cost-effective, thereby accelerating trustworthy adoption of LLMs in regulated financial settings.

Abstract

As Generative Artificial Intelligence is adopted across the financial services industry, a significant barrier to adoption and usage is measuring model performance. Historical machine learning metrics can oftentimes fail to generalize to GenAI workloads and are often supplemented using Subject Matter Expert (SME) Evaluation. Even in this combination, many projects fail to account for various unique risks present in choosing specific metrics. Additionally, many widespread benchmarks created by foundational research labs and educational institutions fail to generalize to industrial use. This paper explains these challenges and provides a Risk Assessment Framework to allow for better application of SME and machine learning Metrics