Beyond Traffic-Light Risk Scores: Building Climate Risk Models That Actually Work | Joan Saladich (Part 2)
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What is the main topic discussed in this episode?
If something is not insurable, no bank will finance this something if there is nobody ready to insure this. So it will not develop. So if a hospital cannot be insured, we have a big problem. I'm trying to think about what's important for the insurance industry or what I would ask because it's a very crucial industry for our society.
Hello and welcome. My name is Jake Harding, and you're listening to Making Risk Flow, Exploring the Ecosystem, a new companion series to the Making Risk Flow podcast, where we sit down with data providers, technology leaders, and Cytora partners to uncover actionable knowledge on how insurance can achieve frictionless risk flows. So I guess onto the science then. How does a massive shift in underlying science, like we've discussed, how does that expose the limitations of static climate indices? And what does that mean for carriers whose risk assessments are built around scenarios that science now deem impossible?
So with the concept of static that you are asking, let's say there are two interpretations, because that's kind of a double-edged sword. On one hand, in climate modeling, it's not weather forecasting. So climate modeling per se is static. That's the first interpretation. And I also see in some cases that there are different actors and colleagues in the industry that are trying to promote a more dynamic climate modeling, like, hey, we are updating our climate models every year. In my humble view, this is also not the correct approach because imagine that you're modeling the next 100 years and you're updating based on the last 50 years. One year change shouldn't have a big impact on your model that runs for the next 100 years because your model is pretty much solid, maybe a decade of data.
It could have an impact on the trends, on the deviations, return periods. But just one single year, that could be an anomalous year. So per se, this is a concept that I believe it's very important because at some point it could mislead the end user. I guess not the AI, but the end user.
Why do static climate‑risk scores mislead insurers and what are their limitations?
It's like constantly saying that you are updating your model so that your model is not static in climate modeling and projections, right? not in weather forecasting or in seasonal forecast, or if somebody is doing, we tried a yearly forecast, but real predictions, but in the projections, it shouldn't have an impact in your projection. Yeah, so it's static. Now, the other idea is the silos idea. It's the other field. Due to this, as we mentioned at the beginning, traffic light factor or a nutritional score factor, yeah, like what we've seen in supermarkets from one to five, it seems like when you have a wildfire risk index, it's purely relying on the weather or on the climate, on what's on the atmosphere, right?
And there should be science on what's on the ground, which kind of vegetation, how will this vegetation evolve? So this concept of static, it's very interesting on the other hand. Let me put you a practical example. Following wildfires, because as I was speaking, I just remember, Wildfire risk indexes. There are some risk indexes that were generated for weather forecasts, and they are coupling atmosphere, like temperature, rainfall, relative humidity, irradiance, blah, blah, blah. They are coupling this with soil data. And for a weather forecast, that's cool for the next five days. And you just have a relation, yeah. So at some point, soil data is almost assumed to be static. So the next five days, my vegetation, it's green today, it's going to be green in five days.
But it happens a lot in climate projections that they just extrapolate the same index of wildfire risk with the same coupling. And this is a very wrong idea because vegetation growth and vegetation dies. And it's more prone, let's say, to get into fire when it dies. And this happens in Spain a lot. And wildfire risk indices, per se, are not working in Spain. We have January and February, these last two years, January and February were very, very rainy. And so if anybody was forecasting, let's say, the risk index, the wildfire risk index for June, for July,
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Chapters
7 chapters
1
What is the main topic discussed in this episode?
0:06–2:25
2
Why do static climate‑risk scores mislead insurers and what are their limitations?
2:25–5:56
3
How can annual updates to climate models create false confidence in long‑term risk?
5:56–11:04
4
Why does ignoring vegetation and soil dynamics cause wildfire risk indexes to fail?
11:04–13:21
5
What did the UN say about AI‑enabled climate risk intelligence and the danger of black‑box models?
13:21–19:34
6
Should insurers build their own AI agents or partner with specialist platforms for climate risk?
19:34–26:27
7
How can explainable AI and human critical‑thinking improve climate‑risk decisions?
26:27–29:42
Speakers
1 identifiedMore from Making Risk Flow | The Future of Insurance
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