Using AI to Strengthen Claim Reserving

Accurate reserving underpins effective claims management, financial confidence, and regulatory trust. In long‑tail Casualty and Financial Lines claims in particular, reserving remains inherently challenging due to uncertainty and evolving exposure. Here, Head of Tetra Claims, Sean Hayes explores how AI is increasingly being used to support more informed, data‑driven reserving decisions.

Challenges in Traditional Reserving

Traditional reserving often relies on set review points and the judgment of experienced claims handlers. While that experience is essential, it can sometimes lead to similar claims being treated differently across portfolios based on individual interpretation.

How AI Enhances Reserving Accuracy

By drawing on historical claims data, AI provides helpful reserve benchmarks and flags claims that start to move off track. This supports reserving decisions that evolve with the claim, rather than being driven solely by periodic reviews.

Enhanced Data Capture and Improving Consistency

By bringing data into the picture, AI helps claims handlers avoid early assumptions sticking for too long. The result is reserving that is more consistent across similar claims and easier to explain reserving methods when questions arise.

Tetra Claims Perspective

At Tetra Claims, AI‑enabled reserving is viewed as a practical enhancement to expert claims handler judgment. Our AI development focus is on combining experienced claims handling expertise matched with advanced AI insights to deliver accurate reserving, stronger portfolio oversight, and greater confidence for our Lloyd’s market clients.

If you’re interested to learn more about Tetra Claims’ end-to-end claims management approach, please reach out via hello@tetraclaims.com