🤖 AI Summary
Traditional machine learning metrics and generic benchmarks frequently fail in financial applications of generative AI, while reliance on subject-matter expert (SME) evaluation introduces subjective bias and systemic risks, leading to erroneous performance assessment.
Method: This paper proposes the first LLM evaluation framework specifically designed for financial domains, systematically identifying four canonical risk categories—e.g., semantic mismatch and misalignment with business objectives—that arise when combining automated metrics with SME judgment. It integrates SME input, multi-granularity metric analysis, formal risk modeling, and validation on real-world industrial use cases.
Contribution/Results: The resulting multidimensional evaluation framework significantly improves assessment reliability and business alignment. Deployed across multiple financial institutions, it demonstrably reduces metric misuse risks in LLM deployment, thereby enhancing model trustworthiness and decision-making robustness.
📝 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