Monitoring LLM behavior: Drift, retries, and refusal patterns

Monitoring LLM behavior: Drift, retries, and refusal patterns

Monitoring LLM Behavior: Drift, Retries, and Refusal Patterns

Section 1 – What happened?

Swiss fintech companies are facing a new challenge in the development of their Large Language Models (LLMs): the unpredictability of their behavior. Unlike traditional software, LLMs are stochastic and can produce different results on different days, making it difficult for engineers to develop robust tests. This unpredictability can lead to "hallucinations" – incorrect or misleading responses – which can be a huge compliance risk in high-stakes industries such as finance.

Section 2 – Background & Context

The use of LLMs in fintech has been increasing in recent years, with many companies incorporating them into their products and services. However, this also means that they are now facing the challenge of ensuring that these models behave as expected. The traditional approach to testing, which relies on binary assertions (pass/fail), is no longer sufficient for LLMs. Instead, a more nuanced approach is needed, one that takes into account the stochastic nature of these models.

Section 3 – Impact on Swiss SMEs & Finance

The unpredictability of LLM behavior can have significant implications for Swiss SMEs and the finance sector as a whole. If LLMs are not properly tested and validated, they can produce incorrect or misleading results, which can lead to financial losses or even regulatory issues. This is particularly concerning in the Swiss banking sector, where compliance and risk management are of utmost importance. To mitigate this risk, fintech companies will need to adopt a new infrastructure layer, the AI Evaluation Stack, which includes a structured pipeline of assertions that verify the LLM's intended function.

Section 4 – What to Watch

As the use of LLMs in fintech continues to grow, it will be essential for companies to monitor their behavior and adapt their testing and validation processes accordingly. This will involve developing new tools and techniques that can detect and mitigate the effects of drift, retries, and refusal patterns in LLMs. Companies that fail to do so risk being left behind by their competitors and facing significant regulatory and reputational risks.

Source

Original Article: Monitoring LLM behavior: Drift, retries, and refusal patterns

Published: April 25, 2026

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Disclaimer: This article is for informational purposes only and does not constitute financial advice. Consult a licensed financial advisor before making investment decisions.