David Fischer On Pricing Physical Risk Into Every Financial Decision

Hi everyone and welcome to Climate Proofers, the show all about those on the front lines of Adaptation Finance, Tech and Policy. I'm Louis Woodall, the editor of Climate Proof, which you can read at www.climateproof.news. Now those who have been paying close attention to my writing, speech finds and general musings this year will know that a key theme has been how to use geospatial data to address climate risk and adaptation problems. For those who don't know and those who need a refresher, geospatial data is information that describes objects, events or other features with a location on or near the surface of the earth. Right away you should get why this would be useful for improving our collective climate proofing abilities. With geospatial data we can see in real time how climate hazards like floods and wildfires move over a region and use this to inform evacuation orders for vulnerable populations. We can also fuse information on the specific location's climate risk profile with data on the physical assets built there to determine their exposure and vulnerability. We can even take it one step further and use these insights to make tailor-made adaptations that can protect a given asset against the particular combination of climate hazards they face. These humongous possibilities have made the geospatial industry one of the hottest in adaptation land. Right now there's even something of an arms race happening between established incumbents and feisty new start-ups to bring the best data and analysis to the market. Today I'm joined by a key figure in this burgeoning space. David Fisher, Chief Product Officer of Risk Management Partners at Munich Re. David's career sits right at the intersection of climate, geospatial analysis and financial risk. Right now he's tasked with putting the reinsurers 140 years worth of catastrophe risk modelling into the hands of banks, investors, corporates and governments, most recently through the climate change edition of Munich Re's location Intelligent Suite. We get into how geospatial intelligence is turning physical climate risk into a financial language that credit committees and CFOs can understand and act on. We unpack why David believes climate risk remains grossly underpriced across large parts of the market and what's really behind the widening insurance protection gap, which last year left roughly half of all natural catastrophe losses globally uncovered. We also talk about the practical barriers that banks and corporates face in adopting location specific climate risk data and whether industry is headed as risk assessment starts to give way to real adaptation decision making. Before we get to the conversation, my usual reminder that if you enjoy and value the climate proof is podcast, please give us a star rating on your favourite platform and drop us a review if you really want to make my day. It really helps get the pod in front of more curious listeners. And of course if you want to go even deeper on climate risk and adaptation topics, please consider taking out a climate proof membership. Members get access to in-depth features, virtual teachers and webinars and brand new data products that explore corporate adaptation. You can upgrade by heading to www.climateproof.news/upgrade. Okay, in a minute my conversation with David, but first a roundup of last week's top adaptation and resilient stories. In finance, multinational development banks committed an all-time high 103 billion dollars of climate finance to low and middle income countries in 2025, a 21% jump from the previous year. Of this, adaptation finance to poorer economies climbed 31% to 34.6 billion, nearing the 42 billion annual goal MDB set for 2030. In policy, President Trump ordered a review of a scientific reference manual for the federal judiciary, accusing the National Academy of Sciences of including "fraudalist, biased and misleading" "climate guidance that shaped major court rulings". The directive issued via Truth Social on Sunday calls for federal suspension and barmat officials to review "this conduct, though what conduct and by whom was unclear". The move follows febries withdrawal of a climate chapter from the federal judicial center's reference manual on scientific evidence. More than 20 Republican turneys general had argued the chapter, drafted by Columbia Law School's Jessica Wentz and Radley Horton, was biased against fossil fuel companies. And in tech, UN Secretary General Antonio Gutereas pressed for fast deployment of AI-driven early warning systems last Thursday. Warning that a strengthening El Nino will intensify floods, storms and droughts for vulnerable populations. Multi-hazard warning coverage now reaches 128 countries, more than double the 2015 total, but a third of nations remain exposed, overwhelmingly least developed and small island states. Right, that's it for this week's roundup. Now onto my interview with David. Hi David, where are you calling from today and what do you do? Hi, I'm David, pleasure talking to you. I'm the Chief Product Officer of Risk Management Partners, a unit of Meenigree, and calling from Unic. Thank you for joining us today, cited to learn more about risk management partners, but first I'd like to start with you. David, can you tell me how your career to date lead to Meenigree and to climate risk as a theme and what does your current role entail? So the past 12 years I actually worked at the intersection of energy, finance and climate and I had different roles in the public and private sector. So I started first at the European Commission actually working on the emissions trading systems, the European common pricing scheme, and then switched to price water, as coopers worked there on energy and climate projects. And just before joining Inigree, I was part of the International Energy Agency in Paris and a senior official there leading its power sector investment unit, as well as its climate risk efforts. And the core contributor to the special reports that it generates every year. So for example, the World Energy Outlook or the World Energy Investment Report. And if you allow me, I would just expand a little bit more of how I got to Meenigree. So for me, I think I'm always drawn to those wicked system-level problems a little bit. So those problems that are difficult to solve and that often require actors to work together, especially public and private sector actors. And to me, climate adaptation is one of those problems. We know of course that every country, every city, every business will have to adapt to a changing climate. But out of the roughly two trillion-use dollars in global and new climate finance, for example, only around 100 billion-use dollars today are actually directed towards adaptation and resilience. And Boston Consulting Group last year came out with a figure estimating that you would require however around 500 billion to 1.3 trillion-use dollars in adaptation investments annually in 2030 in a Paris Alliance scenario. And so in an even higher-warming scenario, you would of course lead more than that as well. And to me, the core question for that was also, where do you start? Because I think across policymakers, regulators, finance, corporates, there's no common understanding of what exactly climate adaptation is and how adaptation finance can be scaled up, how to create the investable business models for climate and adaptation finance, and how the public sector can then help scale that up as well. And for me, the solution starts, and this is how I come to me in a agree, with understanding where physical risk actually exists and how it will change going forward. And everything else then follows kind of from that. So how many people are exposed? What is the financial impact these risks could cause? Where should one focus in adapting and deploying Scorescaptzole? And what kind of adaptation be that insurance, be that climate resilient products, be that physical adaptation measures make sense? And so, to me at least, these 140 years of risk modeling for natural catastrophes that Munich re already brings with its long history, it makes it uniquely positioned kind of to help governments and businesses understand, measure, and manage those physical risks. And that is ultimately what brought me here. And today, as I said earlier, I'm the chief front-end officer of risk management partners. And in practice with terms that actually means I bring Munich re's climate and natural catastrophe capabilities into the hands of banks, investors, corporates, and governments. Wonderful. Thanks to that overview, David. And yes, Munich re is this storied institution being around for decades, if not hundreds of years, is under over a hundred years, Munich re? Over a hundred years. Or what's approaching hour and 50 soon? And you're absolutely right that this understanding of the physical world is integral to understanding financial impacts, the climate risk, and building adaptation to physical risks. And I'd like to ask a little bit about Munich re's capabilities here in terms of being able to identify climate risks and do assessments of the vulnerability and exposure of assets. And what makes Munich re? History and its business well suited to this task. Yeah, sure. And maybe not everyone of the listeners is familiar with what Munich re is and what is reinsurance. It's also not something that I knew for my entire career, of course. So its traditional business is of course providing reinsurance and insurance capacity to other insurance companies, but also to large corporates. And so that in itself is a very important function to keep insurance against natural catastrophes available and affordable. But maybe a bit more specific, another of Munich re?s dedicated to climate risk abilities is location risk intelligence. And so that is a software service platform assessing physical climate and natural catastrophe risks and location level granularity for any point on the world. And does this build on the proprietary risk models and is hundreds of years or decades of years of loss experience that I talked about. So that means that it's actually built on the same scientific risk foundation that mean agree relies in its own risk assessment for pricing insurance products. And it's the same risk intelligence that we use to manage our own book. And that can ultimately serve we found banks asset managers, asset owners, corporates, insurers, which has at scores with forward-looking climate scenarios and financial lost metrics across multiple perils and climate pathways. And so with that knowledge and expertise that we can provide through this platform to those different companies, we can help them to understand actually when exposure is how financially material the impact of physical risks is today and how that might change on the different climate pathways. And the exact use cases that you would find in our client base, they can be, for example, from ranging from analyzing locations for new wind parks or production sites. They can be essential inputs during the due diligence process of a net position by a private equity company, for example. They can also be used as input to improve risk pricing models by insurance companies, but they often are also used to be integrated into credit risk models for mortgage portfolios, for example, of global banks so they can better understand how the physical risks of different homeowners might be. And so actually what we've done is we expanded those capabilities even further now. So we just launched, for example, something called a company climate risk condition, which estimates the financial impact of climbers and a company low, because we saw that this was actually a gap in terms of capability that our clients needed. And so with this, you have the ability to actually look at first of all the risk at a company level, but also be able to deep dive into the assets of a company and which assets are driving the risk. And so with that, banks and asset managers in particular, they can actually analyze for the first time they cooperate alone on the equity portfolios, even if they themselves do not have the exact location data for each production side or commercial side. And we also equipped that then with financial indicators such as expected loss or embedded impact. And for this is the the ability or our attempt to actually translate physical risk into a financial language that the CEO or a credit committee or a risk, a more investment committee can actually work with. Yes, this seems to me to be the holy grail of climate risk technology, right? It's taking observation data, data in the physical world. And yes, as you said, translating that into financial indicators, because if you can speak the language of corporates or financial institutions, you can enable them to make investments that help to reduce their risk exposure and maybe even proactively support adaptation amongst themselves and the suppliers and also contribute to potentially public private partnerships to increase the resilience of our whole community. I'd like to talk a little bit about the kind of tech involved here because a phrase I'm familiar with and I wonder if you share this is spatial intelligence or spatial finance. This idea of using satellite data, earth observation data from UAVs, drones, ground sensors as well, getting all this data and then, as you said, making this translation happen. Can you talk a little about that process, how you think about spatial intelligence at Mutipri. So I mean, spatial intelligence, of course, has been around for a few years, but it's becoming a bit more mainstream, I would say, no. At its core, it's understanding the risk where it actually materializes. And so it becomes specific to the location, the assets and the facility and it doesn't use regional averages or it's not only backward looking, but it's actually forward looking. And so with this, what I can do then is I can identify across a large portfolio, for example, the exact locations, assets and hazards driving the risk and how does risk might change given different climate scenarios. The ability of spatial intelligence of course starts at the ground level in that sense, so at an asset level. But again, if you combine that with the necessary financial data with the necessary, let's say, legal entity data, for example, you can actually translate that and aggregate that upwards so you can get a much more in-depth understanding of climate risk and a different spatial level, so to say that you require it. And you can go basically from an entire corporate portfolio all the way down to a single asset. So spatial intelligence therefore makes climat risk granular, specific, forward looking and comparable across portfolios. And so then it allows, as you said earlier, as well, for discussion about how to manage the risk and where and how much to invest and to adapt to the risk. Or for banks, for example, there's even a business opportunity in the realm of spatial intelligence, because you can then talk to your customer after identifying that they might have a significant or material risk. And you can ask them, well, how could we support you in financing the adaptation and the resilience measures that you would like to deploy against this risk? So there's also a real business opportunity. And so maybe just to summarize in a nutshell, spatial tells us for climate risk in particular, it is what credit models did for lending kind of, what credit scoring did for lending. So it turns this complex opaque exposure and those multitudes of risks that exist into something measurable and therefore and also actionable. Yes, I feel sometimes it's difficult to kind of understand this translation process from Earth observations to financial metrics. So I mean, example is flood risk. I suppose I understand that if you have information on proximity of a water source, the terrain near a asset, for example, and you also have information on precipitation patterns, you can do some pretty good modeling about how if a range storm, a one in 50 year range storm hits this particular area, how that water will flow through the body of water, how it will flow through the terrain and potentially flood a factory or a destructur, infrastructure. Similar with heat, you can have thermal satellites that kind of identify hot spots and can project like, okay, what's the likely extreme range of that heat in this area? And how could that affect people? But I think there's still some taste and discernment or expertise that you have to bring right because you might know the physical variables, you might know a lot about the asset itself or the company itself, but you still have to kind of provide some expert judgment as, okay, how do we think in reality this asset will react? How do we think the workers at this facility will react? Can you tell us a little bit about that? Is that the kind of secret source you're bringing the sort of expertise from 100 plus years of municree? So basically how, let's say, dimensionally, how we would talk about this ad municree is the first part that you mentioned, we would call let's say the hazard modeling. So, there's a water body or there's a certain slope in the territory and then you can deploy flow models to understand how fast the water run if there's a 150 years precipitation event, for example, happening. So this is one part of the secret source in that sense is to actually get this hazard modeling correct and right and improve on that continuously. The other part that's absolutely crucial to be able to understand, especially then what this means in terms of finance, in terms of US dollars, euros or pounds affected is that you need to deploy so-called vulnerability curves. And so for that, you on the one hand need to understand the asset specifics, yes. So is it, you know, are we talking about a office building? Are we talking about a private house or are we talking about a production site? Three times. This differs then in terms of full abilities, but at the same time, you also need to be able, of course, not only to create generic vulnerability curves that exist also in academia out there, but what we do is we use our loss experience that we've had in the past 140 years to understand, okay, how has in the past a, for example, a production site or a private house been affected by a significant 150 years flood and what kind of damage has this cost to this particular building? So this is how we deploy our expertise that we get from all the reinsurance insurance business that we do and the lost data that we can collect through that, and we can then apply that to optimize to calibrate, to perfectionate in that sense the vulnerability site of the equation. So then you can actually trust in that data and in that financial impact, output that you that received that does is actually something that's grounded in reality. Yes, this is credibility from being a reinsurer operating around the world for the decades that I feel like a number of interesting startups in this space don't have. So I can understand entirely why companies like Unigria are getting to this space and why they might have an edge in terms of the analysis they can provide. And I want to turn now to like what your analysis so far has informed you or may be surprised you about climate risk in the corporate financial sector, because there's a widespread belief in climate science circles that financial institutions are underestimating their climate exposure and vulnerability and you'll see surveys and reports about how we're expecting that they're brought to repricing in markets to take into account climate risk any day now. I've heard that so like 10 years now and I'm wondering do you believe climate risk is it being underpriced and if so for what kinds of assets in particular? Yeah, so in short, I would say yes, it is being underpriced and significant parts of the market. And so 2025 for me is a good illustration as a year to quote a few numbers that we connect every year. So for example in 2025, the United States didn't have a major hurricane landfall. And so even though there was no major hurricane landfall, natural catastrophe still caused around $20 billion in damages in the US and low. And so the single largest event that actually happened was the Los Angeles White Fires. And that caused $50 billion in losses. So this shows you on the one hand that climate change is increasing and also that exposure is increasing. So people are bestowed building in high-risk areas. But of course what happens also, and with such events happening, and they have been happening more frequent and more intense in the past years, is that they lead to an increase in insurance prices. And so does this maybe a gradual change that you see? And there's just been, for example, a study published by the US government accountability office that looked at how did the insurance premiums change across the United States for the average US homeowner. And on average, they increase by 3% even taking inflation into account between 2019 and 2024. But in certain parts of some states actually were increases with 25% or even more. So very significant increases. And so this is an economic signal that exposure is increasing. It means people are still continuing to build in high-risk areas. But also the intensity and frequency of hazards is increasing. If you look at how insurance works normally, in most cases future climate effects are not priced into those price changes yet. And so that can give you one illustration of maybe what's still to come in that sense. And maybe why it hasn't happened yesterday or the day before, even known as it has been set in the past. And maybe another observation is that while large-scale natural cask test-traffy events can catch the headlines and move stock prices even though only very shortly often. And when they rebound very quickly, the financial impacts, for example, of slowly compounding impacts of climate change. So the ones that don't hit the headlines, including in supply chains, they are not captured. Rarely do you see, for example, analyses of how much higher would have been the counter-sexual. So how much higher would have been the GDP or the profitability of a company if climate change was not there and was not increasing the frequency or intensity of natural hazards. So this is a bit of a, you know, a mechanic called a tragedy of time horizons when it comes to deciding about climate change. Maybe this is a tragedy of a mental model of not thinking about counter-factuals normally. And this is certainly something that is not currently priced enough. And so what that, to me, at least in a practical term, terms means, is that long-term decisions around, for example, mortgages or infrastructure finance or corporate lending, they're often priced at today's risk footprint and not taking into account the 20 to 30-year hazard profiles can differ materially. And so then ultimately, that means that climate risk is simply not part of the discussion in investment or credit committees. Yes, there's certainly a divergence in time horizons that different financial institutions, different actors are working towards. And I think sometimes we talk about misprice in or underpricing universally across markets. But you'll see, because the insurance industry, policies generally recess on annual basis, you'll see repricing happening in certain high risk areas, that's happening. And I do wonder whether for large long-term asset managers, like pension funds, several wealth funds, whether they can take that longer view and ask thinking more carefully about where to allocate capital. But I think I do feel markets are more reactive than proactive in terms of responding to two world events. I want to dig into this insurance component here, because a big discussion in climate risk and adaptation world is the question of uninsurability. So we've heard over here in the States, and also in Europe about this insurance protection gap, where economic losses, far exceed actually insured losses, which means there's a lot of damage that's been done to households and companies that aren't being covered by insurance. And this growing gap is a problem because it impedes economic growth, it cuts into available savings and revenues. And it can also mean that more financial burdens put onto governments to help bail out areas hit by these extreme weather events. So I wonder if you could speak a little bit about this uninsurability crisis or the insurance protection gap and how it could change the urgency with which corporates and financial institutions worked to understand their own physical risk exposures. Because from my point of view, it seems sometimes talking to financial institutions are like, it's okay, we've got insurance, we're going to be fine. And I wonder if you believe that is something they should believe. I mean, you know, there's also of course, often a topic within the insurance world where there's a lot of finger pointing sometimes also going on, etc. And certainly it's a very valid and very important topic to take forward. But maybe I want to rephrase it to some extent. So uninsurability per se often doesn't even exist or very, very rarely exists. So risk seem today they remain insurable if they understood the price and managed properly. And so the bigger issue that we see is this protection gap and a simple version of it is just a comparison between what is the economic loss in a given year and what is the short loss. So the part that actually was covered by insurance. And why I want to rephrase that, it will reframe that also is because it is in no one's interest, not in the interest of the insurance sector or insurance company, or the government or the homeowner, they're not to be insurability going forward. Because everyone would lose out. In the end, insurance is there to diversify risk. And of course, it's also a model which a lot of companies rely on in order to make investment decisions, etc. And so absolutely going forward, we want to remain the world to be insurable. This is absolutely no question asked. But at the same time, the protection gap has been increasing. And so if we look at last year again, globally we had net-cat losses of 220 billion US dollars. And only half of that was actually in short. And if you would go back to 1980, you would actually see that the protection gap was not at 50%, rather it was around 70 to 80%. So there has been a significant decrease. And so for us, we need to read in its own strategy has to think about how do we deal with that topic of the protection gap. And in our ambition 2030, which is our most recent strategy that was published this year, we actually focus on expanding risk capacity. And also investing into better models. So we can actually better understand how might the risk change and what is the risk today and taking different elements into account. But also part of it is supporting adaptation and strengthening the insurance safety net. And one way to do that is our push to widen and deepen the offering of location risk intelligence. Because this isn't for me, and in general part, to help its own clients to help governments to help society in the end, strengthen their own capabilities and understanding risk, and also ultimately improving resilience against these risks. So very concretely, for example, this means for a bank or investor, they need to be aware that uninsured and underinsured assets can hit loan performance much more than a homeowner. It actually has kept up with his insurance. And they need to look at that from a portfolio perspective. But also they need to use this view on physical risk exposure to engage with their own clients to ask those questions, what are you doing about those risks? How can we support you in mitigating those risks? And so they need to look at these risk exposures before and after risk mitigation is applied. Yes, I certainly think we're seeing more cross sector collaboration or cross sector discussion on the risk mitigation, understanding that climate risks will cascade through financial systems, through economic value chains if they're left unchecked. And therefore, because everyone's affected, everybody should have skin in the game here to try and address this. One question I have, and again, this might be one that's outside the scope of this conversation. But I'm interested to know to what degree corporates and institutions are borrowing from insurers playbooks in terms of risk management. So insurers, re-insurers use catastrophe models. I've event the models are in house models to kind of identify the likely maximum loss that could occur because of a specific peril. I'm wondering if these tools are being used by other entities now. And maybe this explains why companies like Mutakria are trying to basically take data and insights that they use in-house and and try and sell them to to corporates and financial institutions because there's an understanding that they need to use some of the tools that insurance companies have used in order to get a better understanding of their extreme weather risk. So yes, what's your perception of that? Are insurance tools being used by other entities these days? Yeah, I think more and more. Certainly not everyone, of course, but what we've seen in the past five years is a significant increase in different sectors from every sector, to be honest, all the way from originally, let's say, primary insurance that wanted to get a better insight from re-insurance companies because they normally are especially more active than in the natural catastrophe modeling. All the way to banks, asset managers that invest into equities bonds, but also into infrastructure, as well as corporates that use it for their own assets, that use it to identify a size of the system. location or do it during that cyclical location for new assets to real estate companies that manage real estate, portfolios or invest into real estate. And so we've seen a take up in terms of usage of the models that we create of the expertise and data that we can provide across all of these sectors. And I think the reason behind that is of course that insurance and especially reinsurance has had so much longer in terms of track history of pricing, of modeling, of understanding these risks. But the other one is also that these sectors and these companies, they need to look at the risk before and after risk mitigation. And so one part that beyond the risk modeling that we can bring is an expertise on how does insurance market work. And what is actually normally insured to one extent and what are usually limits that are including an insurance contract. And so this is the other elements where a lot of clients approach us and ask for our help to basically so they can better understand what is their gross risk and what is their net risk and then react according. Because of course you do not have to engage with a company that might have a very high risk exposure but they are fully insured and therefore actually their risk is at least financially mitigated. Rather what you want to understand is that one company that is very high exposed to risk and that has a lack in insurance because those are the companies that actually could then default on their credit for example on their loan and those post the highest risk to your lending portfolio. Now I want to get into some of the potential challenges companies might find integrating the sort of intelligence and data that you provide into their own work processes. Now I speak to corporations and institutions a lot about physical risk and what data sources they're using and they always want more data but there seems to be particular challenges in terms of weaving that into their existing workflows and sometimes there's actually constraints. So banks for example are very highly regulated and they use numerous models to kind of estimate risk and to price their loans and they have to be very careful about what data they kind of let into those models. So I've heard from a number of banks that okay you know what our model risk management team won't allow us to use this or they will only after we've gone through several different very rigorous tests so that adds friction to incorporating physical risk data and then corporate themselves like if they have their own enterprise risk management system set up and that physical risk data doesn't a call to the systems they use then they won't do it. So I'm wondering if these are challenges you're familiar with and how do you try and get over that capability gap or that kind of discomfort gap so that folks will actually use your data in the sites. Yeah I mean I mean this two questions and that right one is kind of the capability gap of I don't have necessarily location data I don't know where the assets are and what do I do about it and the other one is more maybe around model governance and data validation and what do I do with my own risk management. So let me take one off to the other because both are very common barriers that we hear from our clients. Maybe only on the first part so how do I get this location data how do I know where the critical assets are. In most cases except maybe when we're talking about corporate stay of course you know where their own assets are but especially if you're talking about a large portfolios often you don't necessarily know where they are. We tackle this through let's say three ways. One is if you actually know when the assets are I mean does it the basic the basic so service that location risk intelligence provides is you can analyze them by uploading it with the precise key coordinates and you can adjust the vulnerability codes depending on the SD characteristics etc. But if you do not have those SD level data then this is in the development process that we've done for the past one and a half years and so basically does this now something that we can provide for the company climate risk division that I mentioned earlier where we basically build our own proprietary SD level database and met those to company family trees. So because it's important if you're analyzing for example Volkswagen group you want in this analysis how do you to be part of Volkswagen group analysis right because they are part of the family tree and so if you are collecting asset level data you need them to on the one hand make sure well one that the location of it is correct of course but secondly you need to be able to connect who owns this asset what to what degree do they own it sometimes there's a joint venture as well and what is the capacity of the asset meaning how important this is as it to the company and to be able to map that back to for example Audi and then to map back Audi to Volkswagen because otherwise you're you're not capturing the whole risk actually and so this is one part that we've developed with the was past one and a half years and the other part is they will still and always is and will be a certain level of lack of data and we basically developed also a top-down screening which is a first view if a first view is required but you really can't get that data when the asset data is missing we still can provide a certain assessment of physical risk even if that is not the most accurate but at least it gives you a first view and then you can start focusing and going in more depth and trying to get those assets level data that you require but on the second part of moral governance and data validation so this is really something that we learned over the yeah over the past seven years that we've run location risk intelligence is that the requirements around validation around auditability around integration to risk models of course has increased and has increased also in line with the regulatory scrutiny that is on these models and this is really also where we see our reputation needs to hold up or duck what we offer needs to hold up to the reputation that we agree past and the credibility that it has and so this is why on purpose we've chosen that location risk intelligence actually shouldn't replace internal models we do not want that you go and buy location risk intelligence and you replace your own internal risk management model I don't think any major company would actually do that because they want to be in control of their own risk more and being control of the assumptions that are being taken and the data that goes in there and so that's why on purpose we create and location risk intelligence in a way that it's transparent and audible risk layer that organizations then can take and they can validate it they can analyze it they can also provide documentation to their auditors and they can take it and if they want they can integrate it into their own frameworks and to their own models in line with their own internal governance and validation requirements and so this is often also where we together with a client go through validation processes because of course those big clients that's integrated into internal risk models they themselves have to have the internal validation process they have the internal validation teams and we and collaboration with them help them to go through these validation processes so they can be sure that the data that they use is for purpose they actually want to use and that it holds up to the internal requirements that are set and I think another element here is also that the underlying models that we use for location risk intelligence are the same that meeting re-users in its own risk assessment and so that of course gives them clients confidence that the data has been tested in a real underwriting environment and not just in a research lab. Yes you're tasting your own cooking right that's the phrase that you are not selling something that you wouldn't use yourself and I think that gives reassurance and credibility to your solution just as an aside I think it's interesting we seem to be entering this new phase of climate risk assessment because when I started writing about climate risk in around 2020-19 climate disclosure was all the rage right the ideas that you're going to have corporates and financial institutions self-report do a lot of the data gathering themselves and that data would be used to inform the pricing of assets the pricing of companies and climate risk would be properly incorporated into financial decision making and I think one of the challenges that came up quite quickly as that as you said like a lot of institutions especially financial institutions don't actually know where all the assets are don't really have that direct data and even some corporates like they might have some basic data like the latitude and latitude but they don't actually know the terrain around their property they actually don't know the the flood risk or the hail risk whatever it is in their area so there was this big scramble by these companies to get data from some sources to fulfill these disclosures but now I think when this world where there's you know a proliferation of earth observation technologies there are companies like yours who can actually gather this data yourself and in this big database you mentioned and I think actually we're entering the tool where maybe we don't even need that much more disclosure we can actually get the data ourselves and map it to two companies and that will help maybe inspire the repricing we've been kind of waiting for and I'm not saying the climate disclosure is not important I think it's important as a risk management exercise for companies to do this and understand the risk themselves but I'm saying that it's not like financial institutions have to kind of wait for these disclosures anymore they can kind of get the state from other sources as well and I think that's going to really help speed things along let's say in terms of how climate risk is priced and then what adaptation investments makes sense do you agree with that do you think climate disclosure might be less important now we've got all the data available anyway I think it's a combination To be honest, certainly we do have more data available. And also ourselves, we are starting to collaborate with satellite data providers and use them in our tool as well, of course. And you have also global databases that cover, you know, tera, innovation models, et cetera. But at the same time, it just makes it very practical. Even if you have a satellite picture of a production side, you cannot look inside of it. Mostly they are covered, of course, and there's a building around it. And so you still need a certain nozzle of disclosure from those corporates to be able to build a better picture. Of course, I don't think the ambition is to ever get to a perfect picture. I think there's a, to some extent, through, or doing that through a digital tool is, is, to some extent, not realistic. But I also don't think that this is necessarily what the finished sector necessarily needs. What they need is, they need a bit of an ability to cut through the noise, in that sense. So meaning identifying who is the most risky, who is driving the risk in my portfolio, and also who can I support in becoming more resilient and more, and adapt to the risk? Because they are interested in, you know, the pro-incapital, for example, a bank wants to provide capital to its counterparties. A asset manager wants to invest in the most resilient company because that means, in the worst case, that if they, if they isn't natural, it desouce the happening to a company, that the stock price does not go down as far as it normally would. And so there's a, there's the kind of motivations that they have in, in trying to understand what physical risk is and what it is on a broad portfolio space, and to then cut through the noise to the single company. So I think you still need all of these different elements of data, also some disclosure, even if dad now is not so much to focus anymore because we have a proliferation of data models as well as satellite observation. Yes, you're very right to highlight the limitations of external observation data, especially we were talking about how physical risks interact with the interior buildings or yes, those parts of a facility that we can't see from space. Right, I am interested to know how tools like those that you provide at BUNIT REAR are actually informing adaptation, decision making. It seems like the focus of most climate risk intelligence providers has been to tell companies, look, here are your risks. This is the qualification of your risks. Enjoy. And now it feels like there's a real effort to be like, okay, here are your risks and here are the, here's a menu of things that you can do. Here are a series of solutions that can do that are tailored to your context. And that really feels like an error that there's a lot of energy going into. And I want to understand how you're seeing your tools being used to kind of help to inform these adaptation decisions and what else needs to be done to really move from just risk assessment to kind of risk mitigation. So I mean, overarching the question I asked you is, what is the real goal right of physical risk and sentiment in that sense? And so the real goal should never be, I think, just measuring the risk. I think maybe that is what you said in the past few years actually was and what providers said, well, here you go, here you have an assessment of your risk and then good luck. And no, in the end, it's enabling better decisions because this is only the only thing that will move the needle in that sense. And I started earlier with the investments that are currently being done in adaptation finance and what needs to be done to be on track with a Paris Alliance scenario. And so for me, this is one of the metrics that we can maybe use as data risk providers or tool providers to understand when actually we have an impact and whether better decisions are being made. But so overall, how can these companies be that a bank, an S-Mes or a corporate use this tools to make better decisions? So for me, at least judging from the discussions that we had and what our clients are doing and how they're deploying our tool is, of course, they're still using it in the very first space to identify the risk because there's changes, changes in portfolios, there's changes in production sites, et cetera. But they are now using it in a much more smart way, I would say. So they are taking the data, they are integrating it. For example, if you're a bank into their credit risk model, so they actually try to come to an understanding of what does this mean in terms of the probability of this homeowner of this company defaulting on the loan that I provided to them, which is a very different thing of just analyzing what the physical risk might be. It's taking into account the financial prowess that a company might have. It's also taking into account whether there is defense measures available. So do you have, as a corporate at this production site, do you have a die or not, or do you have air conditioning so that your employees can actually work even though it has 35 degrees outside? So those kinds of elements are increasingly being taken into account on top of that also whether those companies are short or not and to what extent they are short and what normally are the limits in those insurance contracts. So this is, let's say, a much more smarter application, first of all, of these tools. And secondly, of course, what we're also seeing is that more and more clients also are asking question, okay, but what kind of adaptation measures can I do, given that particular risk at that location? So for example, is that building made of water is made of concrete? Because that differs than in terms of wonderful ability it has. And the next step in the evolution is, of course, also say, okay, what happens if I would do build a die here and how much does it cost? And how does it influence that the climate expected loss that I would expect to receive without it being there? Because what you can then do is you're on the one side and have the cost of the climate risk and you're on the other side and have the cost of the adaptation measure. And of course, what does a business do with that? They calculate a return on the best. And so they will actually look at, okay, what does the what measures actually make sense? Is it more sensible to invest into a die here? Or is it more sensible to accept the physical risk, but financially mitigated through insurance resounds? So those are, I think, the next steps in terms of how you can actually deploy these digital capabilities to then make real world decisions around resilience and adaptation. Yes, we're definitely reaching that stage where we can calculate adaptation ROI, right? That's a big topic right now. And why I'm interested to see is how, as this data gets more sophisticated and as companies get more comfortable using it, how kind of seamlessly will be used to kind of inform every financial decision, right? Instead of being only flagged for like big infrastructure projects, maybe it comes down to, okay, so we choose the supplier, or that supplier and actually you can use climate risk data and adaptation data kind of help make that decision. I think we're still in at least in my observation companies are still looking at kind of the supplier is going to have to take some data for kind of discrete parts of their portfolios, discrete assets that are mission critical, which is probably the right way to do it given limited resources. But I'm really interested to see whether the dates like you provide the analysis you provide to be piped into almost every decision that's made by Corpus Fast institutions. And I think that's the world we're moving towards. Right, David, thank you so much for your time talking today. I just want to finish off by asking what's on the docket for you. I mean, agree with risk management partners. What have you got to look forward to the next few months? Ha, plenty. We just launched this company climate risk edition. So this is something that will keep us busy in the next few months. We have lots of conversations and events where we want to talk about it, where we also want to see to get feedback on it and to improve on it. The other one is certainly becoming much smarter and even more capable in terms of our platform and the adaptation space. So this is really trying to suggest what kind of adaptation measures make sense if I do them how they affect immolability and what is ultimately the ROI of it. And to be honest, I'm also very much looking forward to something that was just launched in Munich. So where we are based and that is the Munich climate climate week. And so we are founding part of that. And I think such orbs are absolutely core to what we need to do. And this goes back to when I said at the very beginning because these forums enable us to talk about partnerships and they enable us to talk between stakeholders because you're not going to solve the entire climate risk problem with just a digital tool. You're going to need all kinds of stakeholders in the room and they need to collaborate to actually get this needle moving around adaptation finance. Yes, climate risk is place based. So the more we can have these kind of local gathering to talk about climate risk and resilience the better and great. I can add Munich climate week to my roster of events that I'm going to. I've got a New York London. I've done what to DC. Maybe Munich is next in the list for me. David, thank you again for your time. Really appreciate it. I look forward to following what Munich, Reem, Risk Management partners can use to pioneer in this space. But for now, thank you so much and have a good rest of your week. Thank you very much. Hello, talking to you. Climate proofers is a climate proof media production. It is produced and presented by me, Louis Woodle. 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