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

In a blog post this week, the Federal Trade Commission signaled that it's taking a hard look at bias in AI, warning businesses that selling or using such systems could constitute a violation of federal law. "The FTC Act prohibits unfair or deceptive practices," the post reads. "That would include the sale or use of – for example – racially biased algorithms." The post also notes that biased AI can violate the Fair Credit Reporting Act and the Equal Credit Opportunity Act. "The FCRA comes into play in certain circumstances where an algorithm is used to deny people employment, housing, credit, insurance, or other benefits," it says.

The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.