# Evaluating AI for Finance: Is AI Credible at Assessing Investment Risk?

arXiv: https://arxiv.org/abs/2505.18953 · HTML: https://mlsanity.com/paper/2505.18953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 25 May 2025.

## Builds on (most cited first, 2 of 2)

- [Learning Adversarially Fair and Transferable Representations](https://mlsanity.com/paper/1802.06309/) (2018)
- [Exposing Product Bias in LLM Investment Recommendation](https://mlsanity.com/paper/2503.08750/) (2025)

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

- [Artificial Finance: How AI Thinks About Money](https://mlsanity.com/paper/2507.10933/) (2025)

## Abstract (arXiv)

We assess whether AI systems can credibly evaluate investment risk appetite-a task that must be thoroughly validated before automation. Our analysis was conducted on proprietary systems (GPT, Claude, Gemini) and open-weight models (LLaMA, DeepSeek, Mistral), using carefully curated user profiles that reflect real users with varying attributes such as country and gender. As a result, the models exhibit significant variance in score distributions when user attributes-such as country or gender-that should not influence risk computation are changed. For example, GPT-4o assigns higher risk scores to Nigerian and Indonesian profiles. While some models align closely with expected scores in the Low- and Mid-risk ranges, none maintain consistent scores across regions and demographics, thereby violating AI and finance regulations.