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Notes on AI

Short reads on what's moving in artificial intelligence, through the eyes of a data scientist who works on fraud detection in insurance: what was announced, what it changes in practice, and what I would check before using it.

Opinions are my own and do not represent my employer.

  • 4 min read

AI near-misses as incidents: what the UK regulator's latest message means for fraud models

On 19 October 2026, the UK Financial Conduct Authority (FCA) sent a clear signal to banks and insurers. Its existing rules on reporting major incidents should also cover significant AI failures, and even near-misses. The same day, it published a review of good and poor practice for AI in financial services.

The FCA has no authority in Morocco. Still, its message is worth reading closely for anyone who runs machine-learning models in a regulated business. Most of it applies just as well to a classic fraud-detection model as to the latest large language model.

  • 4 min read

Meta opens the weights of an 816B agentic model: what it changes for regulated data teams

On 15 October 2026, Meta released Muse Spark Open, a large open-weight model trained for agentic work: reading large codebases, calling tools, checking results and recovering when a step fails. The weights are on Hugging Face, so anyone can download and run the model on their own infrastructure.

For teams in banking and insurance, "open weights" sounds like the answer to a recurring problem: how to use a frontier-class model without sending customer data to an external API. The reality is more nuanced. Here is what was actually released, what it takes to run it, and what I would check before using it on sensitive data.