“You can only be at the top of your game when it comes to creating accurate prediction models with a large amount of up-to-date data. And that's what we do, because the risk industry is all about predicting the future.”
In this episode we are joined by Dr. Christoph Nabholz, Chief Research Officer and Head of the Research, Engagement & Sustainability Team at the Swiss Re Institute (Swiss Re).
Get ready for an insightful conversation as we delve into how Swiss Re is leveraging its technology and data expertise to tackle emerging risks. We kick things off by taking a snapshot of the biggest hurdles facing innovation in the insurance industry, giving you an inside look at how Swiss Re is driving its own transformative change. Things get even more exciting when we dive into the role of Artificial Intelligence (AI) in risk modeling. We'll explore the immense potential of AI to revolutionize the way insurers approach risk, and how a robust, high-quality, and up-to-date database is the key to unlocking this potential. Curious to learn more? Then tune in!
Below you will find the full transcript for the episode.
Swiss Re and the reinsurance industry in a nutshell
Chris: Hi and welcome back to Innovation Rockstars! My name is Chris Mühlroth and in this episode, I am thrilled to welcome Dr. Christoph Nabholz. So, Christoph was a postdoctoral researcher fellow at Harvard. He had several research and business functions at Swiss Re since 2002 already. And as of 2021, he is CRO, which means Chief Research Officer and the Head of the Research and Engagement Team at the Swiss Re Institute (SRI), where he's working together with more than 20 top academic research institutions every year to solve risk problems that really matter and of course will help the world becoming more resilient. So happy to have you here. Thanks a lot for joining us.
Christoph: Thanks for having me with you, Chris.
Chris: It’s always, my pleasure. And we start straight away, as always, with a short 60 seconds introduction sprint. Now, Christopher, this is all about you, your career and your current role. So for the next 60 seconds, the virtual stage is all yours. Let's go.
Christoph: Yeah. So I'm Swiss. I grew up in a small town. Then I actually moved on to become a biochemist at the University of Basel. Then that fascinated me quite a bit. And then I continued doing a PhD in the French part of Switzerland in Freiburg on molecular genetics. And then I went on to do a postdoc at Harvard for three years doing at the intersection of genomics and neurobiology. So that was quite exciting. But then I really stepped forward and did quite a move to the insurance industry 20 years ago. They got me in as a geneticist, medical geneticist actually, in the underwriting of life and health. That kept me busy for a couple of years, and then I went into business development, a broader mandate in the Think Tank at the Center for Global Dialogue, a Swiss Re think tank. And then moved back into the business again, into research function. Behavioral economics was one of the big topics that I worked on. And then medical genetics, of course, kept me busy all the time. But now I'm finally in my last function here at Swiss Re, this is the Chief Research Officer across the board, sustainability, all the rest. You can think of that Swiss Re would be doing. I'm also a founding member of a Think Tank, the Female Shift. It's a small Think Tank in Switzerland that is propagating our females to actually be also part of the leadership and helping us actually design the future.
Chris: Which is, I guess, quite busy days for you. Sounds amazing. And to get you up a little better, I do have three sentence starters for you and I would like to ask you to complete those sentences. Let's start with number one which goes like this: One question I never get asked, oddly enough, is...
Christoph: Interesting that in my last 20 years, I've been actually a geneticist, a genomic medicine expert, but never I was asked whether I had my genome sequenced.
Chris: Okay, so let's do this right now. Has your genome been sequenced by now?
Christoph: Only part of it.
Chris: Part of it, alright. Fantastic. Let's talk about that later. I'm interested too in understanding how this works. Number two: To stay organized and focused I...
Christoph: …do yoga sessions every week, continuously. I think getting the balance right is what you need to do.
Chris: Yeah, very disciplined. Wow, okay. And sentence number three: If I had to write a book about my journey so far, the title of the book would be…?
Christoph: Exploring the Unknown. From Genomic Medicine to Sustainable Futures.
Chris: From Genomic Medicine to Sustainable Futures. Okay, so let me know when you publish it. I'll make sure to be one of the first individuals to read it. So now let's start with some basics about the SRI, the Swiss Re Institute, and talk about Swiss Re and the reinsurance industry first. So for those who do not deal with reinsurance that often, could you maybe in a few sentences explain what exactly reinsurance is, and what is it good for?
Christoph: Yes, that's a very good question. Thanks for asking that. Reinsurance is not in everyone's head, right? So we're looking at the insurance industry. The reinsurers are basically taking the risk from the insurance companies. So our clients are the large insurance companies like Zurich, Axa, or like, right? But everybody knows what Swiss Re is the one brand in the background that you probably don't know. So we take on risks. We take on portfolio risks. So that are given by these insurance companies to us. So maybe a life insurance policy book of a million customers, for example, they sign over to us. We take then the risk of the biometric risk, for example, and cover the unforeseen future of the life expectancy in that case. So that's how you can think of us. We as a reinsurance company are focusing quite a bit on the natural catastrophe risks. Because that's where the high volatility exists, right? A typhoon or a hurricane hitting Miami, for example, would be a catastrophic risk that you want to help ensure. And typically this is this we try to take out the volatility of the insurance books, and then we take on that risk for a premium. And we get extra reimbursed for that. So this only works because we are a global company. So this you can only do that if you can diversify your portfolio of risks, winning like we have life insurance, but then we have natural capital insurance, but we have also global diversified risks. So and typhoon in Japan is not going to hit us at the same time as an earthquake in San Francisco, right? So hopefully not. That would be good. And if not, then we can diversify that risk and take on a lot of the big risks. So we are really the absorber of the risk industry if you like.
Chris: Yeah, ok. So de-risking basically the portfolios and the risk industry. Okay. And then, you know, in your personal opinion, what do you think are the biggest challenges facing the reinsurance industry when it comes to innovation? And what are companies doing about it these days?
Christoph: Well, first of all, I think it's consumer behaviors. As you have seen with the COVID crisis, for example, consumer behaviors have changed dramatically, right? So that's definitely a big, big change. Insurance companies have to digitize quite a lot, make sure that they actually have access. I mean, people were not able to actually go and sell their insurance policies anymore, right? So they had to actually find new solutions and they were digital. So that was a big digital push, I would say. The other one is, of course, the new AI like ChatGPT, for example. I mean, these are new technologies out there that are going to make use of that. And this is such a fast moving field that you have to be on the toes on that, right? But that, you know, only works if you have data, right? Access to data, and that's a big one, right? That you need to be worried about its access to data has become restrictive in certain markets. And you can't really transfer data across. And for a global player like us, that's quite a challenge. And there's a lot of other global insurance companies out there who have the similar challenges, right? It's really data transfer, accessibility, yeah, and being able to use data meaningfully in the risk assessment process.
“New technologies like ChatGPT have made our field much more fast-moving, and you have to be on the toes on that. But that, you know, only works if you have data.”
Chris: I mean, you know, obviously technology keeps on changing. But I guess the interesting part of it is that the speed of change is going to increase and increase and increase. You just mentioned the example of ChatGPT or the models behind this, the large language models like GPT 3.5, GPT 4, whatever, which we have in April 2023 right now. So just this weekend, for example, I deployed AutoGPT or BabyAGI to new technologies using GPT 4. And it's interesting to see how fast, you know, in a couple of weeks, basically, now this technology is advancing and how big the community is and actually the effort of the community to advance this. So I agree. And with data, yeah, absolutely. But it's also the right data, as you say, right? Obviously, just any data won't do it. But what, for example, these, you know, what ChatGPT right now is really bad at is giving credible sources to scientific sources, for example. They try to, you know, come up with some resources, maybe sometimes it's even hallucinating. So access to the right data and make exactly sure, you know, you can trust in the outputs of the data and reason with good sources and with proper sources will be a key. And I could imagine that, for example, Swiss Re holds, you know, data that's obviously not publicly accessible. That could be quite a competitive advantage to that.
Christoph: Yeah. You're totally right. I think it's super important that you make meaningful judgment calls with whatever that machine is producing. And currently, yes. Where does it get the information from? Well, from the Internet, right? Do you trust the Internet?
The Swiss Re Institute (SRI) - The research arm of the Swiss Re
Chris: Exactly. That's the thing. So that's, I guess, impressive. And I as introduced earlier, you are as the CRO, the Chief Research Officer and Head of the Research and Engagement Team at, you know, you are at the Swiss Re Institute, the SRI. So what exactly is the SRI, and how does it contribute to Swiss Re?
Christoph: The Swiss Re Institute is really the research arm of the organization. So as a global company, we have 14,000 employees. We have probably about 900 researchers that do really research. Right. And the Institute is a virtual instrument to actually bring these resources together on the one hand and have one aligned research agenda, which we try to orchestrate. And I think that's what the Institute does there. What we do for the company is forecasting. So it's really bringing the right researchers into the right forecast because with risk, it's all about the future. Unexpected risk. How do you make a judgment call on what is the potential future of these risks going to look like? And that's where you need to instrumentalize the research and make sure that these forecasts are best in class. Right. On that side. So that's what we try to bring. We try to bring in the outside in perspective, meaning that we actually orchestrate quite a bit of research together with universities, as you said before, like 20 universities, 20 to 30 university contracts a year, trying to really look at our best, best talents and best knowledge out there that we can then match with our own internal expertise and then come up with best in class models. So that orchestrating or forecasting then matching with the research capabilities inside outside and then actually deriving new products and bringing that to the market through dialogue and enhanced engagement with our executive clients. We are in a B2B business. It's all about our partnerships and working with your clients. And I think that's what we try to facilitate from really outside in to solution finding at the end of the day.
“What we do is bringing the right researchers into the right forecast because with risk, it's all about the future. And that's where you need to instrumentalize the research and make sure that these forecasts are best in class.”
Chris: Yeah. So that's that's a quite broad responsibility. And I guess adding a massive value actually to the Swiss Re business. Now, how can I imagine if there even is a typical day of a Chief Research Officer?
Christoph: I think it's all about engagement. And I'm thinking about my days, right? I'm sitting on the one call after the other, unfortunately. Right. So that's really a big part of my time is engaging with external partner organizations. We want to work with top experts out there who help us actually fill the knowledge gaps that we have. Internally, matching these with the internal knowledge experts that we have. And then at the end of the day, orchestrating a research agenda that produces then also output that we are known for. So Swiss Re and the Swiss Institute in particular is known for its research and expertise. We publish a lot of that. We're quite open about this, I must say. And that's what I love about this organization is open dialogue, trying to shape the market and so on. And of course, I play a role in bringing that knowledge out into the market and discuss it with peers and stakeholders to actually help us be, you know, set up the right collaborating partnerships, because I think collaboration partnerships are key to the success of Swiss Re and the Institute.
Chris: Yeah, it certainly is. And I would love to dive deeper into this and also to dive deeper into, you know, how is the insurance, but also the more specifically the reinsurance industry thinking about innovation these days? How is it transforming? But before we dive deeper, I would love to play a quick game. It's a really super simple game and this is how it works: So I will give you two options, and you choose one of the options and then maybe spend one sentence each to briefly explain the choice. So let's see where this is going to lead us. I'll start with the first one. The first one is on leadership. So what would you say: Would you rather lead a team of like-minded individuals or a team of individuals with more diverse perspectives?
Christoph: Oh, easy, easy answer. Diverse perspectives. Very clear to me. Foresight can only work if you have the whole spectrum of individuals that help you actually scope out what the different options are that you need to consider.
"Foresight can only work if you have the whole spectrum of individuals that help you actually scope out what the different options are that you need to consider."
Chris: Yeah, yeah, makes perfect sense. OK, then let's let's make this a little bit harder. Number two, if you had to choose: Would you rather listen to the Beatles or Rammstein for the rest of your life?
Christoph: Good question. Definitely the Beatles. I love the lyrics. They're insightful. It's also the calming nature of the tones and the music that is playing. I love that. Yeah. So it gets me into a different world, right? And I think if you think about the songs they wrote actually goes into this direction of, you know, thinking a different future.
Chris: True, true. I mean, I could imagine that specifically the Beatlemania hasn't been that calming for them. Even more impressive that their music still conveys the energy of what you just mentioned, because, you know, there is a great documentary on the Beatles and that was just a crazy time for them. Totally. OK, so the Beatles, cool. All right, we have that. And number three, let's talk about your personal habits or your routines to success: So if you only keep one of all your habits and routines to success, maybe the most impactful, the most important one for you, which one would you keep?
Christoph: Dialogue, constant dialogue with my employees, with my key stayer co-holders. I think dialogue is the success to a future bright outlook and successfully actually getting things done.
Digitalization and innovation within the new risk landscape
Chris: So that plays back into the engagement, the dialogue you mentioned earlier. OK, cool. So we have a diverse team, the Beatles and dialogue. That's nice. All right. Thanks for that. And now let's make sure to dive deeper into digitalization, to innovation, the new risk landscape, all of that basically. And let's see how far we can go in just one single podcast episode. I mean, obviously we can make this a serious, but let's see how far we can come. So OK, insurance and reinsurance sector. So, you know, some say I'm not saying that I do that, but some say that the insurance and reinsurance sector, you know, they haven't been known for its innovation power in the past years, maybe decades. And of course, you know, reinsurers were generally, you know, relatively little known in the past. But of course, you know, in the recent light of today's risk landscape, also the risks you mentioned earlier now, you know, they're gaining more recognition and more awareness. So there is a term. I'm not sure where and why, but I always hear, you know, “today's risk landscape”. That's a term I hear a lot. Can you talk about that? So what does today's risk landscape actually look like?
Christoph: Quite broad, right? So if you're thinking about it, the risk landscape for us is really looking at where can we be hit in our insurance propositions that we reinsure. As I said, it's portfolio insurance. It's large, large consumer bases that we can protect. So when you're looking at the COVID crisis, for example, that was one of these big ones that we knew. And we had started to model in 2007 already kinds of putting a pandemic model in place. We published documentation around how we built that model. We're quite open about it again. And yeah, you know, some 10 years or 10 years later, we actually see that hitting us. And it hit us hard. It hit the whole market. I think that's part of the risk landscape. These are these big, big changes that can be foreseen, but come at a low frequency. So these are the big ones that we need to be worried about because they can like an earthquake - San Francisco, for example, that's a huge one to keep us busy thinking about. Do we have the right protection? We need to protect the balance sheet of Swiss Re at this moment, right? And the insurance industry as such. So on that side, cyber is a big one that is coming along. Cyber risk is a new portfolio, but then, of course, has large risks. But then you're looking again at the geopolitical landscape, right? With the Ukraine crisis, energy crisis and so on. Also, this is hitting us maybe not always as directly as you would think, but supply chain mechanisms are interlinked, interconnected. The world has become interconnected after all. It's huge. And you need to think it through. It's not just a COVID crisis that leads then to, you know, delivery issues because China's closing down their doors. But it's an energy crisis. The same thing, right? All of us supply and demand are not made. So I would say like a lot of interconnected risks that can accelerate. That you need to be taken care of. So it's not just a risk landscape as such, but it's an interconnected risk landscape. I think that's what they would like to say here.
“When talking about the risk landscape, it's not just 'a risk landscape' as such, but it's an interconnected risk landscape.”
Chris: Yeah. So is it fair to summarize that we talk about risks and interconnected risks, obviously, that are plausible, that have quite a high probability that would have massive impact, but it's not known when they might occur. Is that kind of the playing field or is it more than that?
Christoph: Yeah. And the intensity it hits us. Let's take, for example, 2022, a just very recent example of last year. Huge severity in terms of records in lost histories and true natural catastrophes. And it started not just with the hurricane, it started with floods, floods in Australia and then heat waves in Asia and Europe. And then we had the hurricane, Ian, that hit us in Florida, right? This accumulated in terms of economic losses of 275 billion US dollars. And it's this accumulation of things. So here is an accumulation, but it's also the intensity. Florida was hit. So that's an intensity one that I think just in short losses there were just of natural catastrophes by itself, 125 billion of insured losses. So for us, a huge, huge hit. And what we see less, we see in loss history increase of about 6% of accumulated risk every year going up and up and 6% more losses every year is a lot. And if you're comparing it then to the 10-year average, which is probably 91 billion US dollars a year is the 125 billion last year's hit. That's quite a significant increase, right?
Chris: Yeah, it is. So I guess, you know, the ambition of Swiss Re is to play a leading role in mitigating these risks, but also new risks. And as I learned, one of the key principles, if not THE key principle, is also to move from protection to prevention. Now that's interesting. What is needed to do this successfully?
Christoph: You need to really get closer to the consumer, right? So we are in a B2B business for a reinsurer. If we say as a reinsurance company, we'd like to help mitigate the losses. So how can we do that? That's a big question. So we can only work in partnership. You need to work in partnership with our customers, which are the big insurance companies, and they have access to the end customers. So can we help them? And can we help them to actually manage and mitigate the losses better? So if every one of us better understands there are risk exposure to get the right protection, to also take the right measures in terms of mitigating risk, then we are getting closer to actually having a more sustainable outcome going forward. What can we do is now the question as a consumer, right? If you're taking it from a life and health perspective, I think the life insurance industry has tried to help consumers take healthy actions, right? Wearable devices have helped us there, right? So the whole wearable industry has given us a tool that all of a sudden you can measure with outcomes, right? And behavioral economics really plays into this, right? This is you can incentivize people to have the right behaviors. You can give them some benefits, cash benefits, for example, on your insurance policy, because you have done your 10,000 step a day, right? Type of environment. So there you can help them actually take the right actions. You can incentivize them and support them in these actions. And it's a win-win. And I think that's the most beautiful part of it, is that it benefits us because we have fewer claims. It benefits the clients because they are healthier. They live longer lives, longer healthier lives in particular. And so I think this benefit needs to be the right one, right? Otherwise, you don't get that done.
Data models for risk prevention and mitigation
Chris: So that sounds like, you know, a lot of digitalization business model of business models, maybe also new business models geared towards that, right? Prevention, risk prevention, also mitigation, rather than just, you know, as the traditional business goes, you know, pay out after some event has occurred. Can we talk about the data and some models behind risk prevention and mitigation? Is there something you can share? I mean, one idea, for example, was, as you mentioned, incentivize people for healthy behavior. OK, that's maybe a product or some, you know, nudge to a specific desired behavior, which I guess is beneficial to everybody. And I'm just waiting for any insurer to come up with, you know, I don't know, a cryptocurrency coin or whatever that rewards people for doing that. Let's see what happens and who will be first. But what models and data are behind, you know, risk prevention and also risk mitigation? Because I can assume it is different ones than or updated ones, at least, than you have used in the past.
Christoph: Yeah, totally. So it's all about this dynamic information, right? And making sure that you can intervene before the loss actually not just happens, but escalates. Right. Thinking about your sensors, CO2 sensors that you have at home. Right. Or you have these days, you have sensors that actually control if you have a water leakage. So I think these sensors and the Internet of Things is helping us actually to connect these sensors and bringing that all into one oversight where you can manage that and help actually individuals, homeowners, for example, to actually have a better view on their risks and actually better control on their risk. So if the leakage happens, water leak is one of the biggest losses that you can see in a household? Right. And the sensors these days are amazing. Right. But how do we get now? How do we get them installed? How do we make sure that people see that as a benefit? And, yeah, you need to bring that risk a bit closer to them. You need to talk to them. You need to engage them. So a lot of that happens. But once you have it in place, then this data can be shared. They can be part of an insurance preventive scheme, and you can get benefits for it. Right. And if you let them participate in these beneficial outcomes that we have fewer frequencies that are lower because of exactly that interventions that happened. Yeah. Well, they will participate. They will see them cash benefits. Right. Immediately. And I think this is the type of model that we are talking about. And I think a lot is going into this direction. There aren’t a lot of products out there yet in the market, but I see them more and more coming.
“We have to make sure that people see data collection as a benefit. So you need to bring the risk a bit closer to them. Once you have everything in place, then this data can be shared.”
Chris: Yeah. And this is super inspiring. I mean, just from that conversation, I could imagine like a ton of products being released to the consumer, to households, to, you know, anybody who basically owns assets, like, as you say, maybe houses, maybe, you know, rental properties whatsoever in terms of water leakage. Sure. So that is super interesting. And when we slightly zoom out from these examples and look at the impact and the anticipation of such things, so it could be economic, but there can also be societal regulatory geopolitical, as you said before, and technology change. I guess there is a need to, and I guess you said it before, there is a need to constantly assess new data, assessing trends, developments, and making sure you understand what's next with a certain probability, likelihood, severity whatsoever. So you build these models and try to understand, okay, what could happen and what is happening out there yet that maybe drives change. So when you collect this kind of data and how do you actually move from, okay, now we understood, you know, there is a trend, and it might be going this and that direction to the actual impact, new products, new business models, a change in the organization. So the first part of the question is where to collect that data. And the second question is how to move from trend to actual impact.
Christoph: So data is available in different scales and in different markets. Of course, we have been focusing on the insurance industry loss data. That's the most powerful data that we own. We have the best control over and that we would like to model out. And so that's a data asset that we have been cultivating over years, years and years, and actually offer that also as a solution to our clients, to governments, to innovators as well, right? So to help us actually build new data propositions and data models out there. So this is a service that we bring actually to market. And I think one of the best things is really flood zone modeling, for example, or we have now a new climate change model in place where we can actually model out the which climate scenarios are going to Will the flood zones change, for example, right? And that's quite important because the industry needs to understand exactly where they build their houses and what they need to take as mitigation actions to prevent future loss scenarios. Loss scenarios are going up, right, with climate change coming through. So that type of information we can provide already. And that provides a service to clients that we bring to market. I think another one which I like also is this, you know, like this automated decision-making process where you automate the claims process, right, which is consumer benefit at the end of the day.
“We have been focusing on the insurance industry loss data. A very powerful data asset that we have been cultivating over years and which we actually offer as a solution to our clients, to governments, and innovators.”
And flight delay insurance, we actually innovated. And that was a data set that we got from the market because I mean, the flight industry is actually very good at tracking flight delays. You know exactly where their airplanes are and when they're landing and when they exit. And so we used that in collaboration and built a flight delay model where we now have an insurance proposition where you can, before you board, you can actually buy online, you can buy the flight delay option. And the moment you land in your final destination, you know whether you have been delayed and by how much, right? Is it 20 minutes, 30 minutes? There's an immediate cash benefit that we then provide to the insurers, to the bank accounts. They immediately get reimbursed. No claims questions asked. We know exactly when the flight finally arrived. And it's actually quite neat. It's a neat proposition. It's fully digitalized. It's fully dynamic. And the data was there. That's why we did it.
The role of AI in the insurance industry
Chris: That's probably not something you want to provide to Deutsche Bahn, but maybe that's for a different discussion. We can see how if that's actually a loss generating business for you guys. But okay, different discussion. And we talked about this earlier. We talked about artificial intelligence earlier. What do you think? What would be the role of AI for, for example, the early detection of emerging risks? Is it already being used? And if so, maybe do you have an example of how that works?
Christoph: So we are using AI as well, right? That's true. That's true. But it needs to be transparent AI, right? So the regulator, we are highly regulated in the industry. So we cannot just use any type of data out there, first of all, to actually fit the AI model. And once it's done, we need to make sure that it's ethically taking action that we can stand behind. And I think that's the critical part of it. So a non-transparent AI wouldn't work for the insurance industry. It has to be a transparent one. And it's one that we can defend actually the pricing model behind. And that we can also stand in front of our clients and say, look, this is best in class. But what AI does this once you have a lot of data available, it really is best in class in predicting models. And that's what we do, right? The risk industry is about predicting the future. And so, of course, we are using these. But it's always the latest and the most advanced that we try to get into and making sure that we are safe and sound when it comes to regulatory requirements and our consumers that are trusting us that we take the right risk assessments.
“We cannot just use any type of data out there, to actually fit the AI model. Since we are a highly regulated industry, we need to make sure that it's ethically taking action and that we can stand behind.”
Chris: So that's interesting. So it sounds as if overall Swiss Re is actually in the midst of the transformation process from where you are today or have been in the recent past to a tech-enabled, data-empowered, risk knowledge prevention mitigation company. And that's interesting because oftentimes organizations obviously struggle with ambidexterity. So making sure that your company balances the needs for innovation for transformation, transforming itself, but at the same time needs to keep the core, needs to preserve the core of the company. In that case, need for stability and risk management in the insurance industry. So how does Swiss Re approach that? How do you balance the need for innovation, but also the need for stability, operational efficiency, obviously, and also risk management in the insurance industry today?
Christoph: Yeah, a very good question, right? So it is always a balance, right? You're correctly saying that. You have to balance this out. So we do have, of course, a lot of actuarial processes in place that are standard, that have been defined by the regulator. So innovating is not trivial for the insurance industry, particularly when you want to actually get the benefit of AI and new business and new data proposition models and so on, taking that into place. And I think that's the one that is moving into the right direction at that pace, right? So and I think it's fair that we take the right time to make the adjustments and make sure that we actually get the whole industry moving into this direction. And I think the regulator just needs to be part of it. But then when it comes to innovating the rest, right? So we are trying to really innovate also the whole workflow process, right? And that starts from how do you engage with the consumers? What is the best platform out there? And we are re-insurers, right? But we are helping our insurance clients to actually get that engagement right and give them the right tools. So there are lots of things you can do in the prevention space, for example, as we discussed, right? We have a wonderful tool now with mental health app that is helping our end clients actually to manage their health and wealth status, right? And making just sure that we give them the best advice. And these are devices then that are also chatbots, giving information to consumers and that stuff. So you can do a lot on that front of engagement with the clients. I think on that front, we can innovate a lot at the end of the process when it comes to the claim. Again, that's another big opportunity where you can automate a lot so that the claims process is smooth. Once you have it, there’s nothing more disturbing that you have your claim, and you want to have your pay now from the insurance company. And there comes a huge document that you need to file, right? A horrible lengthy process. So there we can do a lot and that has been already done a lot. A lot of it can be digitized. And if you're looking at the innovating companies out there, yeah, they use video. For example, actually just do a snap chat video of your claims, send that in, it's analyzed. Maybe you have to provide even a statement in Word that makes it really convenient and easy actually to submit.
“You can do a lot on that front of engagement with the clients. I think on that front, we can innovate a lot at the end of the process when it comes to the claim.”
Chris: These type of things, they are there to innovate and a lot of it can be done and more to come, I would say. But you obviously would need to protect against fraud, right? Because just imagining it's going to be pretty easy for an artificial intelligence model to fake a video of that. Like, you know, see my house burn. Okay, maybe not my house burning down, but maybe a water leakage, something small, right? And then actually say, well, okay, sure, now you have the video. Oh, oh, oh, it's really bad. So send this to the insurance industry and get your claim, and it's actually never happened. So also there, you know, we need to make sure you actually, you know, prevent against these fraud cases, obviously. So that is like cat mouse problem, but it will be interesting to see how to cope with that. But that's just new challenges, you know, arising from new technologies and new developments. So obviously there will be a lot more of that stuff coming soon.
Christoph: Yeah, no, you're totally right. I mean, if there are loopholes, the consumers will find the loopholes quickly. Yeah, I think that's true. And you need to be on your toes, right? You need to make sure that you protect yourselves from fraudulent claims. That's okay. But in principle, we also trust our insurers, right? I mean, there are the few fraudulent claims. Don't wait out on actually the big trust that we have in our clients, and they have in us. And that's the majority of the population.
Chris: It certainly is. Absolutely. So let's try to, you know, close and summarize some of the discussion points we have. I have two final questions for you based on the conversation that probably could go on for another hour or maybe two or even a week. But let's try to bring it out and summarize some of the things we discussed. What I'd like to do is, you know, can you summarize three key actionable recommendations that you would want listeners to take away from this episode?
Christoph: It's a good one. So I will probably start with the point one is that we live in an increasing interconnected world, which requires really partnerships, access to data in a compliant manner, of course, that fulfills our laws and regulations that we have in the insurance industry. But it really needs that interconnected partnership. That's the number one. So number two would be I think AI is essential when it comes to best in class modeling, forecasting and so on. To train our risk models, but they're only as good as the underlying data is. Right. If you have bad data, it's not going to work right. You have to have trusted data. I think we all need to work on these trust databases that we can actually build our models on. And then the last point, it would be… look, there's the insurance industry is about ensuring the unforeseen risk. If we can model in 100 percent accuracy what the loss outcome is going to be, then you don't need insurance. But what you need is you need actually you need people who actually help you to these forecasts because historical data is not going to tell you what the future bring. But what our experts and their interconnected networks bring to that discussion on how the future is going to play out, that's what you need to quantify and bring into a proposition that helps you actually forecast in the best way beyond foreseen risk.
“If we can model with 100 percent accuracy what the loss outcome is going to be, we don't need insurance. But what you do need is people to actually help you to make these forecasts because historical data is not going to tell you what the future will bring.”
Chris: Yeah. Yeah. So the last one is really powerful. Obviously, sure, historical data cannot help you project or predict the future for sure. And that's obviously how many people these days try to even create artificial intelligence model for the financial industry, stock prediction and the likes. Obviously, let's see. I don't know if that really works because it's the same principle. Like, sure, why should the stock market or any capital market actually work the same way as in the past and then just predict into the future? Same in your case, obviously, you know, past risk events certainly will not translate the exact same way in the future. And it's not it's not linear. It's certainly not linear. It's also not following a very specific mathematical algorithm or model. So, sure, you need the human insights basically in the expert insights more specifically. But the combination is very powerful because if all the data, and you have the qualitative insights as well from the experts, that's probably a unique position that you're in and where you can base future offerings and support for the insurance, but also insurers, but also for, you know, other groups. I totally agree. OK. Yeah. And then finally, Christoph, if you look back on your professional career, I would be interested to understand what your greatest Innovation Rockstar moment was so far, at least.
Christoph: The greatest rockstar moment. Interesting one. I would say lately was one of what we've worked on for years on actually getting a forecast right for Swiss Re. And finally, when we convinced our senior management stakeholders that this was the one that could be trusted. And it was not because of the AI model that we had in place. And we had an online model that was great. Right. But it was really getting this expert opinion and expert judgment into this forecast and being able to convincingly show that this is the best in class we could do. Come up with the best model outcome. I think that was brilliant. Right. It took me five years to get there. But it was for me, it was an achievement because it really bridged the gap of modeling and forecasting outcomes in a way that we could sell to our senior management stakeholders.
Chris: No doubt. And best in class certainly is on the same level as rockstar. So that's a great rock star moment. Congratulations to that. And that's it for the episode. Christoph, thank you so much again for being my guest on this episode. It was a pleasure to listen to you. As said, probably we could turn this to an entire series of things. But, you know, Christoph, I'm pretty sure you also have things to do and have a busy schedule. So let's stay with that. Thanks for being here. Really enjoyed this episode.
Christoph: Thanks a lot for having me with you. It was really an interesting dialogue and discussion. Thanks.
Chris: All right. And to everybody listening or watching, if you like this episode or if you have any question, you of course could address to Christoph directly at any point in time. I'm sure he's, for example, on LinkedIn. And if you enjoyed this, simply leave us a comment on this episode or just drop us an email at email@example.com. Now, that's it. Thanks for listening. Take care and bye-bye.
About the authors
Dr. Christian Mühlroth is the host of the Innovation Rockstars podcast and CEO of ITONICS. Dr. Christoph Nabholz is Chief Research Officer and the Head of the Research and Engagement Team at the Swiss Re Institute (SRI).
The Innovation Rockstars podcast is a production of ITONICS, provider of the world’s leading Operating System for Innovation. Do you also have an inspiring story to tell about innovation, foresight, strategy or growth? Then shoot us a note!
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