Nigel Walsh from Google Cloud steps in as a guest host on Podcast 132 to talk to Matthew about our new report, Location Intelligence 2021 – the Companies to Watch: Where, what and how risky?
The report offers a comprehensive look at the technology helping insurers identify and manage risk, and profiles more than 50 companies that are leading the way in the provision of location intelligence.
Talking points from Nigel and Matthew’s discussion include:
- Key considerations for pricing and selecting risks
- Data and developments for supporting claims
- Insights for residential and commercial property
- How location intelligence can help with ESG
- Consumers, trust and having too much data
Our Location Intelligence 2021 report is free to download until the end of April.
If you like what you’re hearing, please leave us a review on whichever platform you use, or contact Matthew Grant on LinkedIn.
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Location Intelligence 2021: Nigel Walsh enquires, Matthew Grant reveals – Episode 132 highlights
Nigel: What is location intelligence defined as? What’s included under that term?
Matthew: Location intelligence is about figuring out what exactly an underwriter has in front of them. What is it that they need to understand to define the risk, make a risk selection and maybe even charge a premium.
Nigel: I enjoyed one of the opening paragraphs in your Location Intelligence report, which said: “a boat’s just a floating building, isn’t it?” What sort of assets are covered by location intelligence?
Matthew: Property is one of the main areas for location intelligence, but there are some great developments in the Marine space from companies like Concirrus and IHS Markit.
There’s a true story behind that boat quote from when Hurricane Katrina flooded New Orleans. One of the big surprises for underwriters, and particularly in the London market, was around insuring casinos. Generally, a casino is a building, but licencing laws in Louisiana meant someone could only open a casino if it was on a boat.
Casino operators had been creating barges that were dressed up to look like buildings and the underwriters priced and modelled them as though they were static buildings. Sure enough, once a massive windstorm and 20 metres of storm surge came through, those buildings acted like boats.
Nigel: How good or bad is the information that’s available today?
Matthew: Still not as good as it needs to be. There are about 100 companies providing data related to location intelligence and more are coming in all the time. About two-thirds of the companies we looked at in this report started in the last 10 years and are defining simple things like the latitude and longitude of a building that you’d expect we’d all know by now. Then there are the more difficult questions like what’s it built from? What are the nearest hazards?
In some parts of the world, property information is really good, but there’s a big ‘buyer beware’ label that needs to go over some of the data because it’s not always as good as people think.
Nigel: With so much data available, what are the most important things to consider for pricing and selecting risks?
Matthew: To insure a property, insurers need to know the latitude and longitude, what it’s made from and if any hazards are close by. Also, if that building gets damaged or destroyed, what will it cost to rebuild it? In the event of a claim, is information about that building available remotely so there isn’t a need to send a loss adjuster?
To your point about the data, there is lots out there and there are two questions to consider. One is can underwriters trust the data? The second is once an underwriter knows everything about the building, can they do anything useful with that information?
In a perfect world, everyone wants complete information, but in a commercial environment with pressures on getting the best price for the customer it doesn’t always work out like that.
Nigel: How does location intelligence support the claims process and what developments are we seeing around loss adjusting?
Matthew: We’re going to see a lot more companies doing remote claims adjustment because there’s a massive cost saving. If there is clear evidence of a metre of flooding by a customer’s building, the insurer can pay out some of that claim straight away and everyone’s happy. They can even start doing things to mitigate it.
Parametric insurance is another area that can be used for claims handling. Claims hasn’t had as much attention from an investment point of view. Andrew Johnson’s Willis Quarterly InsurTech Briefing had about 18% of investment in the whole insurtech space going into claims. I’m surprised there’s not more focus on that because of the immediate cost benefits.
Todd Rissel from e2Value made an interesting comment about acquiring data, which was that insurers will spend about $150 on data to understand a claim for a loss, but only justify spending $1 at the point of underwriting. Data can have a lot more value when adjusting a claim because there is an immediate financial benefit.
Saving on claims, or making an efficiency on claims, or stopping the situation from getting worse makes everyone better off. If someone’s roof gets blown off by a hurricane, the longer it takes to fix and the more the increase of the risk of water damage or mould.
Nigel: Are there big differences in the information that’s available for residential and commercial property?
Matthew: Residential property tends to be a bit more homogenous. Once an insurer has figured out one semi-detached London property, chances are they will know a lot about most of them and they can just change things at the margins.
Small business commercial properties also tend to be more homogenous, but high-value commercial is very different. We’re talking to a lot of people in the insurance world about what they’re looking for, and there’s still a big gap in getting good information on commercial properties, both in the US and the UK. Cytora has been helping insurance companies by looking for data sources that identify leading indicators of commercial property risks. Underwriters use that data to look at commercial properties coming in and see if they are within appetite.
The vision is often ahead of the reality and people sometimes give up using data because they can’t achieve perfection of the vision. In practice, helping people with risk selection by getting something better is a good step towards the ultimate need-to-know information about a property. There are some interesting people looking at IoT and sensors to monitor building performance, but there’s a big gap between having that data and being able to use it for underwriting.
Nigel: My favourite questions that insurers ask about my home are what lock type have I got? How close am I to water? How much would it cost to rebuild my house? Three things that I don’t know very well. Is location intelligence giving the buyer more actionable insights?
Matthew: That’s where we’re seeing a shift. Those questions are a proxy to the fact that the insurer doesn’t have complete confidence in the location. They are relying on customers to tell them where the nearest stream is, which is ridiculous when some insurers are working off data and maps of rivers and streams.
It comes back to questions around prefill and how much information does the insurer need to get its customers through the process? If buying an insurance policy gets too difficult, we know that people give up. They go and find another insurer that doesn’t ask so many awkward questions.
Nigel: Let me jump onto another point you talked about, which was hurricanes. There seems to be a lot going on in the hazard modelling space right now.
Matthew: It’s getting very active again. I was fortunate to go to Northridge in California back in 1994 a few days after the earthquake there. Northridge, Hurricane Andrew, and a couple of big storms in the UK made everyone much more focused on hazard and location. Then it went quiet for 20 years. There were some damaging hurricanes but fewer earthquakes and flood was less prevalent as an insured loss.
What’s happening now is two things. One is people are starting to have more confidence in short-term hurricane forecasts. The price of hurricane risk up until now has been pretty flat and it’s been really hard to get confidence about short-term changes. Companies such as reask and others are now producing seasonal forecasts in June, which is the start of hurricane season, and they can tell whether there are more landfalling hurricanes which is key to identifying an increase in risk.
There is also more computing power available now. Nasdaq and Oasis Loss Modelling are creating more open source, open access platforms. That allows niche companies to come through offering specialist models in areas that big modelling companies haven’t got into.
The largest losses in 2020 came from wildfires, flooding and tornadoes so we’re seeing more hazards, but there’s also more awareness and more technology. We’ve got 23 companies looking at flood alone in the report. Whereas five years ago, there would have only been one or two doing flood modelling.
Nigel: What about climate change? Is that an area where location intelligence can play a significant role?
Matthew: Yes, and there are three considerations around that. The first is the increase in hazards like wildfire, which is a combination of new conditions and people building in exposed areas, and the second is the short-term changes that people want to model.
The third is Environmental Societal Governance (ESG), which is a combination of requirements and best practices for companies for demonstrating their environmental credentials. One of the benefits of all the data we’re getting from location intelligence is being able to point it towards more ESG reporting.
Regulation is often one of the biggest drivers of innovation. When regulators are saying companies have to report on their environmental mitigation impact and show it reducing year on year, the trickle-down effect is really powerful. Insurers can only do that by demonstrating what they are doing to reduce the risk to policyholders. They will need data to measure it and that will be massive.
Nigel: One of the concerns I have is how do we make sense of all the data that’s on offer? How do we orchestrate and organise information about the whole world?
Matthew: We used to talk about risk currency when I was at RMS. Back then, the two main catastrophe modellers were successful because there was a recognition of the risk currency, where the parties, counterparties, intermediaries and brokers all agreed on how to assess the risk.
There’s a lot of data now and some of that data is trusted. Without a trusted source of data that companies can share, it’s very difficult to have a transaction based on analytics.
Nigel: At what point does an abundance of data become too much data? Or can it all be combined to give the consumer something simple like a score?
Matthew: The reality is we’re already being scored by all sorts of things that we don’t know about, such as when it comes to credit or bank lending.
In the commercial world if an insurer has more information it might mean they decide to charge more because there’s more risk, they might then lose the business to a competitor. Or if they find something is less risky, they will charge less. That’s fine but they will have to write more business to keep the revenue coming in. It’s about recognising that not all data is necessarily going to make a difference.
Nigel: Let’s talk about IoT and sensors. I’ve read a lot about what companies like Hippo are doing. How good is the technology?
Matthew: Hippo has positioned itself very strongly around looking at what people are doing with their properties, even to the extent of monitoring changes in roof conditions. Hippo’s exposure in Texas and I suspect when we start to see more information around claims from the recent freeze there, that will be a reality check in terms of how well they managed the impacts.
One of the risks for new companies is the temptation to come into some of these spaces unaware what already exists, maybe thinking: “no one’s done anything good for hurricane risk, I’m going to build a product to help people based on data I can pull off the internet.” There’s been 30 years’ worth of hurricane modelling done on the East Coast of the US and it’s pretty good.
Behind the whole insurtech world, there are some well-established, good companies that are innovating too. Just because they’re not new and shiny, doesn’t mean they’re not doing something really valuable.
Nigel: You know I’m a big fan of connected gadgets and devices. We’ve seen an explosion of technology for the home and how we bring that together for the end-user is going to be critical.
Matthew: I completely agree. Half of the losses from water in the US are actually from burst pipes. It’s really difficult to retrofit a valve to turn off the water, but relatively easy to add to a new build.
If buildings come wired for IoT and risk prevention right from start, the cost is often absorbed into the cost of the build. A builder can test the plumbing for leaks by putting in a device, then they just pass it over to the homeowner. We’re going to see a lot more of that.
Nigel: Which other companies are doing things differently in location intelligence? Who do you like the look of?
Matthew: One is Archipelago which Hemant Shah, formerly of RMS, has founded. What’s interesting about them is they are going back to the building owners, asset managers and investors and they’re collecting data at source.
There’s a big debate about standards and how data is moved around. Ultimately, we’re going to find that the sources of data stay with the building owner and they make it available to people. Maybe it’s blockchain, maybe it’s something else, but the point is not having to shift spreadsheets around the world every time we want to understand what a Tilt-Up warehouse in San Francisco looks like. Archipelago has started to create those relationships, the brokers have got it, CNA is using it, so they’re worth looking at.
The other one is Safehub which is putting sensors into buildings. It was co-founded by Doug Fraser, who started Eqecat which was acquired by CoreLogic, and Andy Thompson who’s ex-Arup. What Safehub is doing is great because to find out how resistant a building needs to be to survive an earthquake. If there are tremors, the sensors can work out what the building frequency is. If there is a major earthquake they can tell if a building has fractures behind the walls and whether it could fall down or is safe to keep using.
There’s a whole lot of things that spin off that in terms of data, but it comes back to risk mitigation. If the cost of deploying new technology is too high, it never takes off. Those sensors cost $1,000 which is trivial for a commercial building project, so why wouldn’t people do it?
Nigel: I was lucky enough to get a preview copy of the new report and it’s extremely comprehensive. How can people get hold of a copy?
Matthew: It’s available to download now from the InsTech London website and will be free until the end of April.