Dynamic Deployment and AI: A Dynamic Duo for Price Transformation
Popular culture has its share of well-known dynamic duos: Batman and Robin, Starsky and Hutch, and Mickey and Minnie Mouse, to name a few. Insurance’s own dynamic duo, dynamic deployment and AI, enable real-time data analysis, personalized policy pricing, and risk assessment, resulting in powerful price transformation for today’s carriers. Special guest Dawid Kopczyk, Co-Founder and CEO of Quantee, unpacks how dynamic deployment and AI provide competitive, risk-adjusted pricing with host Amanda Ingram, Sapiens’ P&C Product Strategy & Marketing Manager, in our latest podcast.
Amanda Ingram|Dawid Kopczyk
Amanda Ingram: Hi everyone and welcome to the Sapiens Insurance 360 podcast! I’m your host, Amanda Ingram, P&C Product Strategy & Marketing Manager, and I’m so pleased that you’re out there listening today. This is where we discuss the latest news, trends, and issues from across the insurance solutions and technology spectrum. So let’s start with some good pairings, Batman and Robin, peanut butter and jelly, silver and gold. Just a few dynamic duos whose great pairing made for an even greater product. The subject of today’s podcast is yet another dynamic duo that makes for an even better product, Dynamic Deployment and AI, which together makes for a powerful price transformation for today’s insurers. And to tell us more about this powerful combination is today’s guest, Dawid Kopczyk, Co-Founder and CEO of Quantee, a company which offers SaaS solutions for insurance pricing. Dawid is a qualified actuary and a fellow at the institute and faculty of actuaries in the UK and has held many previous actuarial roles at insurers across the UK, France, Poland, and Germany. Dawid, welcome to the podcast.
Dawid Kopczyk: Hi everyone. Thanks, Amanda, for inviting me to the podcast!
Amanda Ingram: Well, let’s get started. Dawid, we’ve got a lot to talk about. So speaking of another famous couple, are AI and machine learning, and we always call it ML, these days, still viewed as buzzwords in pricing or are they now fully integrated and delivering measurable value?
Dawid Kopczyk: That’s a good question. I believe there are two sides of the same coin. So yes, the AI is a buzzword, especially these days, and particularly when you are thinking about the usage of something called GenAI in insurance pricing. On the other hand, we have I think, have [a] handful [of] examples that AI and machine learning are already delivering value in the insurance pricing. So on the buzzword side, there are always too high expectations when it comes to what technology can do in the next year, whereas the past experience shows us that it takes around five to 10 years to fully utilize the benefits of any technology. So it was like that with [the] e-commerce bubble at the beginning of 2000, and my guess is that similar things are happening with GenAI and AI. If there is a vision that AI will calculate all of the prices for insurance pricing without any help of the insurance pricing teams and it will do that on its own, even in the next three years, then I think it is [the] completely wrong picture. But we see already the first usages of AI supporting underwriting decisions. We can also see the examples of usage of the AI in the insurance pricing, but it’s not going to be like that. It’s going to be fully automated in the next five years or even 10 years, and if we concentrate on these positive sides, then as I said, AI already delivering the value, but this is not in a fully automated mode. This is always supporting the pricing teams. So we see it firsthand because actually AI and machine learning techniques are used in Quantee software, and the insurers are gaining the competitive advantage thanks to that. So examples from the UK market are insurers investing in machine learning models to better predict, predict risks, sales discounts, policyholder behaviors. So they use already sophisticated modeling techniques that includes AI and machine learning algorithms to drive the results better, to improve the loss ratio combination that’s actually working. AI is also spinning up tedious tasks like preparing risk factors, clustering big data sets like for telematics, spotting patterns, [and] also in the portfolio monitoring tasks when it comes to the insurance pricing. So we can see also the value [brought] by AI, with automating the tedious tasks and that makes the pricing actuaries, pricing analysts, it makes them free to do more scenarios, to do more analysis, and more scenarios and more analysis. It drives the better results. So to sum up, so the answer is that yes, the AI is the buzzword, we have to be careful with that. On the other hand, we can see already the value that AI brings, and I believe that the insurance companies that will not invest the proper time to actually investigate how to use this AI and machine learning properly, insurance pricing, they will fall behind or they will easily lose the competitive advantage.
Amanda Ingram: It’s a fascinating topic, isn’t it Dawid? I mean, I’m just back from a three-day conference and everybody is talking about AI and the impact that it’s having. And I totally agree with you taking the time to understand its capabilities is essential. But do you think that insurers have shown a clear shift when it comes to deploying pricing models over rating tables? And how do you think that’s driving change, particularly when we look at the inclusion of AI in some of those processes?
Dawid Kopczyk: I think the short answer is absolutely, yes. So insurers, there’s a clear shift toward deploying the pricing models faster and faster, and doing that more sophisticated that ratings. And to be honest, most of the customers that we are talking to are actually coming with the same issue. So they have the outdated rating tables approach. They wanted to change that. They see that they are losing the competitive edge on the market, and they want to react to the changing environment, to the movements in the prices in the market. They wanted to react with something more sophisticated than a set of the rating tables, which is deployed rarely or you are rarely limited in terms of the pace of the deployment of the pricing updates. And I think there are three main drivers [as to] why it is changing. So I think I mentioned, I read the first one, but insurers realizing that they’re just simply falling behind the market, they’re constrained by the legacy systems when it comes to deployment of pricing app base to the distribution challenge. So that’s the reason number one, that’s the driver number one. Driver number two is that the pricing department usually has already the ideas [on] how to respond on what is happening in the market, how to respond to the regulatory change, or how to respond to the competitors’ movement in premiums for a specific segment. So they already know how to react, they know how to change the models to be better, but then they’re somehow stuck and they just cannot deploy these new ideas. They cannot connect this to the distribution channels or to the policy admin systems, and that actually drives them to make a decision to use something more sophisticated from the IT perspective. And the third thing is that usually there are blockers in terms of how fast pricing teams can use new data or new risk factors, whether they are limited to a simple set of the rating tables or they can use more complex structures and models and how often they can perform these changes. So these blockers are usually imposed by some IT-like systems and I think it’s becoming a strategy of many interests right now to remove these blockers. The key challenge. It’s…
Amanda Ingram: Fascinating, isn’t it?
Dawid Kopczyk: Yeah, it is. And..
Amanda Ingram: Go ahead Dave, sorry to interrupt you.
Dawid Kopczyk: I think insurers are starting to realize that. I think the key challenge is that the real clue is to completely link the design phase of the pricing models with the deployment. So you cannot really tell apart those two words and they have to be integrated. This is extremely difficult to build such a solution. And I don’t think that insurers are software companies that are, maybe they are capable to build that, but then there’s a different story to maintain that. And I believe that here it’s where Quantee comes into the play. It perfectly integrates with the distribution channels and policy admin systems like Sapiens to deliver value constantly and make, just allow the pricing teams to design whatever the pricing models they wish to design and then deploy it to the distribution channels.
Amanda Ingram: I think pricing and certainly pricing modeling and actuarial processes are quite complex. Anything that helps improve and allows that agility within pricing and actuarial modeling is really important. Of course, we’re aware that profitability remains a primary objective for insurers, but does deploying actuarial models, pricing models help improve profit margins? What’s your take on that?
Dawid Kopczyk: Yeah, this is an excellent question. The short answer is again, yes, so how to link being more profitable with being more agile? And I think we can spend some time to explain that. Firstly, we know that the more precise and personalized individualized models we have, the better we are when it comes to loss ratio and combined ratio in reality. Now, if we have limited ourselves by a set of the rating tables and kind of a slow deployment process, then we just cannot deploy these things because it’s usually very difficult to convert some sophisticated pricing models to the set of the rating tables, because we lose accuracy, we lose this segmentation benefits thanks to the more sophisticated models, and then we will be definitely falling behind in terms of the business KPIs in the market. That includes loss ratio and combined ratio. So if you have a reduction of the time to market to deploy such a precise and personalized model, then that’s basically your profitability goal. And secondly, the speed of deployment. So this is especially relevant to the European markets or LatAm markets, how frequently we can update our pricing engines gives us the agility to quickly respond to whatever is happening in the market. So if we have the system behind the insurance pricing that allow us to quickly respond to quickly change the pricing models, then we are able to correct underperforming segments in real time. We can stage A/B testing for specific segments, and we can react to the competitive movements. And this way we can drive our profitability up thanks to the speed of the deployment.
Amanda Ingram: I think the market is such a dynamic place now, isn’t it? Insurance market itself, consumers are a lot more savvy. Everybody’s looking for the better price, and with insurers developing newer and newer or more directed and focused products, this agility becomes really important, doesn’t it? And I think that growing demand for agility and flexibility in the pricing process is really starting to come to the fore now. So how does an external pricing engine such as Quantee help to deliver those benefits?
Dawid Kopczyk: Before funding Quantee, I was an actuary, so actuaries, they wanted to always tweak something. So they’re never fully satisfied with the external software provided. There’s always a better strategy to do something more customized, more flexible, and we knew that already, that this flexibility is actually one of the requirements and it has to be incorporated into any software that you sell to the insurance business. And so with this growing demand for the agility and flexibility, I think this is already a clue that I mentioned earlier. So the key challenge is that you have to link the designing of the pricing model, and it has to be really flexible. It has to be really allowing to design any pricing models that pricing teams design to answer to any market changes, like changes in the prices of competitors or regulatory changes. And you have to link that with the deployment to the distribution channels and policy admin systems. And if you know how to do that, then you can truly unblock the pricing teams to build whatever they wish and drive the profitability results, loss ratio, and combined ratio.
Amanda Ingram: It’s fascinating. We could spend hours talking about this and it’s been really great talking to you today, Dawid. Thank you very, very much for those insights and your perspective on that. It’s really becoming quite a hot topic. AI is touching pretty much everything I think now, but we really do appreciate you taking the time to speak with us today and to share your insights with our audience. Thank you.
Dawid Kopczyk: Thank you, Amanda, and thanks for listening.
Amanda Ingram: And to our listeners, of course, thank you so much for tuning in. We always love to hear from you, so if you do have any comments or you would like to follow us on social media, please reach out to us on our channels and don’t forget to subscribe to the podcast. We’ve got a lot more and many more exciting subjects ahead. So stay tuned and be on the lookout for our next segment of Sapiens Insurance 360. Bye for now!
