Joan Saladich

speaker

211 appearances 1 recordings 1 series first heard Jun 2026 last heard 2 Jun

Joan Saladich’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jun 2026 with 1.

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Making Risk Flow | The Future of Insurance · Beyond Traffic-Light Risk Scores: Building Climate Risk Models That Actually Work | Joan Saladich (Part 1) · 2 Jun 2026 podcast

For extreme storms, for wind gust, for extreme temperatures, heat waves, impact on health, on health, and this kind of risk.

It's better not to go on an analysis, deterministic analysis with just one or two scenarios.

It's better to take a holistic view, a probabilistic view with all the

S scenarios, the seven S scenarios, for instance.

Yeah, you can discard this one that the IPCC was talking about, but at some point the uncertainty when you're modeling the climate is big, it's inherent, it's a projection.

So the best way to move ahead is to use all the S scenarios.

And this is something that well we are seeing when we are iterating with the market with insurance, reinsurers, but also

Utilities, consultants, experts, climate experts that then generate insights out of our data.

Because in the end, we're just providing data.

We're not providing insight, just the data, and we focus on the quality of the data.

We are seeing that every time more and more they are appreciating that.

So within this complexity, we're talking about having the uncertainty, having the curacy, and also having a lot of SNS.

Scenarios so that you can go with this holistic view.

So it's a lot of challenges.

Some challenges it's because how the industry has been growing, or maybe I want to be honest here, a bit more opportunistic, because well, let's take this scenario, the worst case scenario, and just report it.

And now the IPCC said, well, not possible anymore.

But there are ways to do it better.

ways to do it better.

The important thing is to know which ways and how to compare if you're doing better or not against the standard methods using historical data, so on and so forth.

So big challenges but there are still

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