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

arXiv: https://arxiv.org/abs/2510.13524 · HTML: https://mlsanity.com/paper/2510.13524/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 13 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [Evaluating LLMs in Finance Requires Explicit Bias Consideration](https://mlsanity.com/paper/2602.14233/) (2026)

## Abstract (arXiv)

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