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Insurance companies are close to their financial-services industry peers in terms of investment and deployment of data tools, ranging from data management to artificial intelligence. But what defines the data-driven insurance enterprise? Digital Insurance editor in chief Nathan Golia and Arizent VP of research Janet King will present the results of a proprietary survey of nearly 100 insurance leaders on building the data-driven insurer.
You'll learn:
-Where AI and analytics are being deployed across the insurance value chain
-How satisfied insurers are with the outcomes of their data-driven programs
-What to look for in a third-party data provider
Transcription:
Transcripts are generated using a combination of speech recognition software and human transcribers, and may contain errors. Please check the corresponding audio for the authoritative record.
Nathan Golia (00:09):
With me is Janet King, VP of Research for Horizon. We're going to talk about the Datadriven Insurance enterprise today. This is one of the several pieces of proprietary research that Verizon is putting out throughout all our verticals, especially in insurance, especially for digital, talking about use of data across the insurance enterprise and what goes into the most forward-thinking, forward-leaning insurance enterprises out there. The way this is going to work is that Janet is going to take us through starting with the profile and some of the background about the survey and the research, and then she'll look through the findings and present them and I will sort of bring some of the insight about what we're seeing in the insurance industry that sees how this plays out. As was mentioned, we will have an opportunity for q and a if you're so entitled towards the end of the program today. But I really love going through all these research products that we're putting out. It really gives us a good pulse on our audience because we are getting those respondents from the insurance industry. So please, when you're getting those inquiries about doing research for us, help us out because that information is how we know what to bring you value-wise in terms of coverage and other products. So Janet, please go ahead and take us through here.
Janet King (01:29):
Yeah, thanks Nate and thanks for having me here with you today and thanks for reminding people about the importance of responding to our survey invitations because it really does provide us with critical insights that we use to drive coverage in digital insurance. So we really appreciate it when you take a moment and complete one of our surveys. So welcome everybody. This particular piece of research that we are going to talk about today was completed among roughly 100 insurance carriers and brokers, and we conducted the survey in May and our goal was really to explore some of the key characteristics and best practices that define data-driven organizations today and to try to take a deeper look at where insurers are on the adoption curve for artificial intelligence and other advanced analytics tools. So again, we talked to roughly a hundred insurance stakeholders and all of them had to be directly involved with their organization's data and analytics initiative.
(02:33):
So definitely a qualified group. Taking a quick look at the respondent profile, so among those that we surveyed, just about two thirds of the respondents were from carriers and the balance came from brokers. You can see that eight out of 10 hold director level and higher titles and more than half of the people responding to this survey were in VP plus level roles. So we had a significant number of very senior level titles participating in this particular research initiative. We recruited respondents from a mix of company sizes and we ended up with 63% from firms with 5 billion or more in premiums. So in general, Nate, I think we had a pretty nice overall balanced respondent pool that I think will help us really understand some of the key trends that are shaping the use of AI and advanced analytics in the industry today.
Nathan Golia (03:32):
Yeah, for sure. And I'm looking forward to seeing some of the specifics. It's interesting you talk about getting two thirds carriers, one third agencies, I mean we are seeing agencies of all sizes, but especially some of the larger ones are implementing their own sort of enterprise wide data strategy. So definitely getting closer to the carriers as opposed to being separate from them on a strategic.
Janet King (03:57):
Yeah, exactly. Absolutely. Alright, so let's dive in. I guess we started by asking a question that would help us take stock of the kinds of priority or the level of priority I should say, that insurers are placing on data access, data management and data integration. And what we found is that the majority of organizations across the insurance sector are taking steps to improve their data practices. And in fact, you can see on this slide here that about four out of 10 report that they've taken significant steps towards democratizing data within their enterprise. So Nate, that suggests that most insurers are placing a high priority on operationalizing and utilizing data to advance their businesses. How does this track with conversations you're having with stakeholders across the industry?
Nathan Golia (04:46):
Well, I guess it's interesting because digital insurance is underpinned by this assumption about data. Big data used to be a thing that people did and it was sort of a subset of technology strategy. Now it is the underpinning and sort of the table stakes for doing any of this stuff. It all depends on having a lot of data. I read somewhere recently that the insurance industry has exponentially more data that it's using at every point customer at any point of the value chain than it has in the past. And obviously I know we're going to get into some of the specifics about how that's playing out and that's really the underpinning of all this now. So yeah, definitely getting more people to be able to touch data is an important component of the strategy because it's not just something that the tech organization is organizing, it's now something that the practice areas are using.
Janet King (05:42):
Yeah, absolutely. And I think your point about just the sheer volume of data structured and unstructured data that companies are dealing with really makes that such a huge challenge in undertaking. So we wanted to look at that a little bit and look at not only are they taking steps, but how are those efforts paying off? So we looked at what we thought is kind of four markers of progress at democratizing data, if you will, and overall what we see is that roughly two out of three of the decision makers that we spoke to feel that their organizations have been highly effective at the first three outcomes on this list. So they feel that they've been either extremely or very effective at unifying customer and transactional data sets at opening up access to data for employees and also opening up access to data for vendors and customers, slightly fewer, little over half feel that they've been highly effective at eliminating data silos, which means that you have nearly half who are still challenged by that. So I'm curious, Nate, whether those results seem contradictory to you at all, and by that I really mean, what do you think might be driving success at this top outcome in unifying data sets even as the existence of data silos continues to be a challenge for nearly half of those that we surveyed?
Nathan Golia (07:11):
Well, I think there's a two-pronged issue here, right? One is that relative to how things were a decade ago, there has been resounding success of the insurance industry and operationalizing data, but the baseline was very low and insurance has traditionally been very siloed and that's been maybe the sort of final hill to climb for many people where they're setting up some of these other things that might be working within a silo. That's the other thing. So if you're looking at this and you're responsible for analytics within a particular organizational function, you may have all these things working for you but still know that it's hard for you to transfer that data and the knowledge gained from it to another internal organization. So I think that yes, within most practice areas for insurance, there is success in operationalizing data, making it easy for people to get use out of it and get insight from it. But in terms of saying, well, we've got this great claims data, can we use it for in underwriting or in marketing or something, how can we apply that to other areas? I do think that is still going to be a struggle for a little bit as insurance companies overall get sort of less siloed and flatter and more integrated across internal organizations.
Janet King (08:40):
Yeah, absolutely. And eliminating data silos is so dependent on having a really good governance plan and really having a strategy for unifying those data sets, but also giving a much broader segment of your employee population access to that. And I think directly related to that is this idea of how good are you at accessing data on a realtime basis, right? That's another marker of progress I think, on becoming a real data-driven workplace, right? Is allowing people to have access on a real-time basis. And here what we see Nate, is that most of these respondents fall somewhere in the middle rating. Their organizations is good at doing that and just one in three who say that they're excellent. So what do you think might be getting in the way? What do you think might be some of the most common obstacles to enabling real-time access to data? Or if you want to flip that and look at it from the other way, conversely, what do you think needs to be present in a data strategy to really make sure you can do this?
Nathan Golia (09:45):
Well, I think there's a couple things. One is that we got 30% excellent, 55% good. That's most people who think that accessing data in real time is doing well, they're doing okay, they've got enough capability. For me, it starts with the product that the data is supporting and that could include a sort of insurance product as a policy or a sort of customer experience initiative, some sort of service experience initiative. Has that product been architected from the start to absorb data on a real time basis to make adjustments as needed? What is the function of being able to get data in real realtime? Are you able or expected to use those insights rapidly? Another thing I would also say and that I think is interesting about the insurance industry, maybe more specifically compared to other financial services is that realtime data has a couple other implications in insurance real time, the real time initiative for me a few years ago was related to distribution, making sure that agents, when they were integrating with insurance companies were getting updated product information in real time and not starting the process with a customer and then having a roadblock come up later.
(11:09):
Some people might see that as getting a lot better and they might be judging realtime data that way. There's also the concept of realtime data transmission that does sort of inform a product, but the ability to just have realtime data that is in your product, whether it's something like telematics or some of the other initiatives around smart home, which I know we'll talk about a little bit more later, or in life insurance with fitness and health trackers, they might be saying, well, the perspective of real-time data might be, Hey, are we integrating customer's data into the product and giving them feedback? And the answer might be, yeah, we're doing a good job, so it might not be thinking about it. Not everyone is thinking about the same one. That gets back to the question about silos. It's are people all speaking the same language across the insurance enterprise?
(11:59):
They talk about data and analytics and the means of access and the speed of access. So all in all though, I think that you are seeing an insurance industry from these first few responses, especially, excuse me, from these first few responses that does feel the improvement in its processes from the ability to ingest and analyze data. More so than I definitely when I started covering this industry several years ago that they felt they had a lot of that time was just like, Hey, we still got big filing cabinets full of stuff. We're not even close to having our data set yet, and now they're ingesting all sorts of third party data. They're getting data in digitally native forms or digitizing documents, and it's all paying off in a big way as a lot of other things have been implemented.
Janet King (12:51):
Yeah, I know. I thought the same thing when I looked at the data on these first few slides is that it paints a pretty positive picture or at least the right trajectory of progress that we're making, and to your point, certainly accelerated by technologies and advances in things like telematics and internet of things and sensors and all of that has really, I think in so many ways added to the data influx challenge that you stated at the beginning, but is also at the same time made it more possible for insurers to make decisions quickly and to make maybe better decisions by having all that data. So it's both accelerated the complexity and also in some respects enabled better outcomes too. So it's really interesting.
Nathan Golia (13:37):
Jumping in, oh, sorry, I just want to jump in on that one. Just closing out this first part of it here before we get to some of the specifics, because I think if you think about it, it makes sense, right? Insurance companies had these long contracts that were done on paper or on very early computing systems. Getting those to into a modern architecture was going to take time. But I think what's happened is that a few things about one is that there has been success in translating old data, old and unstructured data into usable forms.
Janet King (14:11):
The
Nathan Golia (14:11):
Other is that the reason they were doing that was because they felt, well, this is our information we have on our customers, but their customers have been able to basically resubmit all the data that they had given another form a lot easier or had third party API data access that's allowed them to get that data without having to get it from just the customer's initial application or something like that. And that's also helped catch up. This was a big early in the big data era in insurance. There was that idea that it was going to take so long because all this data was unstructured and oh my God, we're never going to get it done. And not only have, maybe they've gotten it, some of it done, it's been easier to get it done and they've replaced it with more relevant and updated information that is more applicable to the current conditions that their policy holders are facing. And I think that is also a reason that there's a positive view. Oh yeah. It wasn't just like, oh, we got a slog through all this stuff. It's like we plug in and we get it Now it's a matter of on the previous slide, this operationalizing it, how do we use it? Who's able to use it and for what? And I think we're going to talk about that a little more in the next half here.
Janet King (15:21):
Well, and I know we saw in some of the other research we did around digital transformation and just some of the stuff we were doing coming out of covid is that customer expectations have also so rapidly changed that that's really made a lot of this work probably a higher priority and more of a necessity for folks to remain competitive. Customers are no longer just looking at other insurers. They're looking at their best consumer experience as a way of guiding how they feel about their experience with their insurance company too. So,
Nathan Golia (15:55):
And the other thing insurance companies have is a perspective that is, I mean, sometimes we get these things we talk about the insurance industry is slow to adapt or has a reputation of being such. I don't think that's the case anymore. I think insurance companies are psychologically ready to advance. Now what they're looking at is, okay, but what lessons can we learn from the banking industry going through the FinTech wave and other industries that have had to absorb and change how they look at data? It's more of a consideration than a unwillingness that they're making sure that they're set up for the future for a future state that isn't maybe quite articulated yet, but will be eventually.
Janet King (16:38):
Yeah, and maybe I think it also kind of says that insurance can be at the forefront of that change. So let's look at where insurers are placing their bets and leveraging advanced analytics to improve decision-making. So what you can see on this slide is at the top of the list for functional areas where they've actually implemented or deployed ai, machine learning and other advanced analytical tools are claims processing and risk management. Those are one and two followed by operations and underwriting. So are these the areas of the business, Nate, that you would expect to see insurers focusing on? And related to that, are you surprised at all that only 24% have deployed these advanced analytics tools for fraud detection? I often see that as an early focus for AI and other verticals.
Nathan Golia (17:37):
Well, I guess that, yes, actually, it's funny that you sort of anticipated my answer here because I was going to say that that seems a little low based on what I know, but then I thought about it more, let's maybe start at the top here. Claims, well, why do they want advanced analytics? And then of course getting to AI and ML for claims because that's the moment of truth cycle time and claims is really important, which is the time from when a customer files a claim to when it's resolved. It's hugely important for customer retention and automation and claims is tricky though because people filing claims do want to feel that there is a person listening to them, that there is a person who understands that they're going through a painful moment in their lives and they need support as much as they need a repair or some other sort of recompense.
(18:28):
And so claims is being automated in ways that have to take that into account and that's why we see a lot of deployment service people are trying different things to try and get that claim processed as quickly as and efficiently as possible, but with just the right touch of human interaction so that the customer doesn't feel left out going through the other parts, risk management operations on the writing, my guess is those sort of combine into one sort of general thing. How fast can we get a policy process? Can we get the risk profile? Can we get it underwritten, can we get it operationalized? And I think those, that's the other end. So claims is the thing. Once you have the product, this is all getting the products with your hands and of course want to make that as easy as possible. Excuse me, because that's important for customer acquisition.
(19:21):
Don't want people falling off just like anyone who's running an online or even in person point of sale, they want to get that stuff taken care of as soon as possible. So let's get to the 24% on fraud detection. Why is it just sort of middle of the pack to so low? Well, fraud detection, first of all, insurance fraud increases has increased recently in times of economic uncertainty that happens and there's a chance there that what some companies doing are saying, okay, we're going to pause our attempts to operationalize, do some sort of AI or machine learning based automation here, and we got to keep that with people. The other one might be that fraudulent identifying suspicious applications, or excuse me, claims that AI application might be happening in the claims processing side and happening on the underwriting side, not in the fraud detection unit, not in the SIU or things like that. And so the application of it to a specific case is not happening within the fraud detection organization happening before it gets there as a way of identifying and sending it to them. That would be how I would read that even though it may be all part of a sort of more unified fraud platform, like the fraud platform might extend into underwriting or claims, but actual in terms of where it's identified via analytics or AI is done before it gets to the actual SIO.
Janet King (21:04):
Yeah, some of that workflow to your point then, yeah, built into the claims and risk management cycles already. So that's a really good point. I think that's a very fair point. We asked a similar question, Nate, about in what lines of business are you deploying AI and ML and other advanced analytical tools? So the others was around what kind of functional areas, and this is what lines of business and it reveals that general liability and personal property are the areas of business where they're most often making these analytics kinds of investments. Any thoughts on that or are you seeing any kind of, I'm curious if you have any insight into specific use cases that you might be seeing for those lines of business in particular?
Nathan Golia (21:52):
Well, I was surprised that if general liability and personal property, when 46%, I would've thought commercial and personal auto would've been at 70 or something because I do. I feel like that's happening more there. Now, what might be happening here is that if we're asking about advanced analytics, personal auto and commercial auto, were early adopters of data-driven automation and insurance. Very easy to sort get it onto the earliest type of technologies with success and they might not be advanced, and that might be people giving us what we really want to hear, which is how much have you advanced this? Because that is going to be the case. Something's always going to be first and that first implementation is not going to be with the same technology 10 years later. Commercial and personal auto are actually getting close to 20 years of the ability to buy almost instantly online using just the earlier kinds of data analytics that were available to insurers.
(22:52):
Liability and property are maybe next in line. Personal lines is pretty easy, is a lot easier. Those tend to be more standardized. The information tends to be more up to date. And of course seeing small commercial weight at the bottom is where insurance companies that are invested in that line of business are right now because it is the kind, first of all, it's a sector, I'm sorry. It's a line that has seen a lot of attention in the past few years between covid and environmental disasters and everything like that. Everything that's at it's hitting these business owners, these small commercial policies very hard. And it's almost making it even more important that those policies are designed in a more hands-on way. The problem is that's very, very labor intensive and insurance companies aren't always able to do that, and they're looking for ways to get more of those customers in the door while still giving them the right kind of coverage.
(23:59):
Small commercial business owners, these are policies that where the risks are vastly different. Think about the difference in insurance needs between a hair salon and a restaurant in the same strip mall. You could have a accountant's office or a doctor's office and they're all in the same location, but they're all such wildly different businesses that the kinds of data that go to help you write something like personal property. Where is it being number one? Where is it and how old is it? Isn't the whole story there. And you have to get so much more information from these business owners that it's complex to advance. I speak from experience being married to a small business owner,
Janet King (24:50):
Absolutely. It's just vastness in volumes of data too that these different organizations have, which AI really depends on right large data sets, I guess regardless of use cases. What we're seeing is that insurers seem to be taking a multi-pronged approach to developing the tech that they need to improve their data-driven decision-making capabilities. So what we see here is that about half are investing in enterprise-based software that has data management and AI powered analytics kind of as part of the major solution set, or they're using best of breed kind of off the shelf solutions, but we also see a significant number that are co-developing solutions with vendors or InsureTechs or building solutions in house. So what do you think some of the factors are that insurers should be thinking about when they're going through this buy versus build decision, right? When they're thinking about whether they should purchase an off the shelf solution or invest resources in building their own, what are some of your thoughts around that?
Nathan Golia (25:55):
Well, I think that where we see the most co-development or in-house buildings with the bigger companies, they have a proprietary view. They also have a wealth of data that they have access to. They don't need to be pulling stuff in from third parties in their feel. They have a huge data set that can be operationalized and give them a competitive advantage, which is where we see that decide the decision to build. They also have the desire to architect from the ground up a data-driven insurance product, which I think is an important thing to note here, that companies that are deciding to build, they're integrating their technology strategy much more closely with their product strategy. They want to have it so that it works for the needs of their products and that they also could see it as a potential thing that they can then license themselves to other, use another revenue stream that they're licensing out something they built and they sort of get into the software business in addition to the insurance business.
(27:06):
The buying part probably is easier for companies that are less complex or smaller or even just starting out. In the case of some InsureTech, we know that they just like, Hey, we need a data platform that does this. And they're sort of finding something off the shelf that matches their product needs. Whereas maybe for some of the older companies that they're trying to get something that's more tightly integrated, A new company can say, well, we're all just starting out. We can find something that works and make it work for us in a way that's easier
Janet King (27:43):
And they have fewer IT resources, right? So the newer companies have fewer staff, fewer legacy data sets to deal with. Whereas to your point, those larger, more established enterprises have a lot of legacy data sets. And so there could be some also fear of vendor lockin for some of those bigger firms who want to have flexibility to do what they want moving forward
Nathan Golia (28:09):
Or just proprietary ideas in the first place. And thinking that's if you're building that stuff internally, you're also generating a practice area that will be data-driven and also understand insurance, which I think is a really important thing to understand about what some of these companies are able to do. You you're bringing in a pipeline of people, you're establishing a career path that will be very specific and very, you won't be pulling people from all over the place, let's put it that way. They're going to have people coming in who want to work on insurance, who understand that's the case and have establishment there that is allowing 'em to work with data and insurance specifically. I might not be articulating that exactly how I hope, but I think that,
Janet King (29:02):
No, I think
Nathan Golia (29:04):
There's something to be said like, Hey, you want to be a data scientist? We have a real world application for you. Or you can go work as a data scientist somewhere else. You're going to be hopping from project to project and work-life balance might be a little different. There's insurance companies who are looking at it that way too. Bring that expertise in and reward them and they'll have that expertise and skill in the organization for years.
Janet King (29:29):
It makes total sense. I think you articulated that very well. So let's dive in a little bit deeper into the industry's adoption of artificial intelligence and machine learning specifically. So I'm going to throw a couple stats at the audience and then ask you to comment on it. But what we found is that four in 10 are actively engaging with artificial intelligence and machine learning, and most of those folks are pilot testing those capabilities or I think what you can see here is that a significant number are pilot testing, and about four out of 10 are just deploying on a limited or two out of 10, sorry, Nate, limited use cases. So you've got roughly 40% there and then you've got another 60% who are still investigating or building a business case for artificial intelligence and machine learning. So we're still kind of on the early nascent side in terms of people who are really have moved past pilot testing and are starting to really actively deploy artificial intelligence across the enterprise.
(30:40):
I think in keeping with that, there are a number of things that are prohibiting more widespread deployment of artificial intelligence, and one of them is a lack of talent, which we touched on just a minute ago when it comes to even building out some of these solution sets. So feeling like they don't have the talent within the organization to implement ai, high cost or another frequent objection, difficulty finding the right solution partners to really make those AI implementations work. And then last but not least, kind of coming in at four and five are data quality issues and concerns regarding accuracy and reliability of algorithms, which is something that I've seen these kind of track with what we've seen as obstacles and other verticals as well. So I'd like to ask you more specifically, we're seeing the widespread adoption of artificial intelligence and machine learning is still really in the early stages across the industry according to this data, and perhaps it's in part to these kinds of obstacles. What do you see as some of the more pressing barriers here, and what do you think the industry can do to mitigate some of these challenges?
Nathan Golia (31:57):
Well mean if you look at the first three things here, and then think about what I just said a minute ago. Yeah, insurance companies, it's not about believing in the technology that there's potential there or that it's not something that they're ever going to use, which there was a bit of that earlier on, I mean maybe even before my time reporting on this, but there was definitely some people like, no, we're always going to do this a certain way. That's not the case. Insurance companies do want to advance or do want to digitally transform it and input these things, but they want to make sure that they're doing it in a way that is sustainable and also going to be successful. And if you have, look, don't have the talent within the organization, doesn't mean you don't have a person who understands it or person who's advising you.
(32:48):
And if that person is saying, look at this, we don't have enough people to implement this, it costs too much and we could probably hold out for ballot partner. There's one, two, and three right there. And that's what that person's job is in the organization to establish a strategy. Insurance companies are very focused on AI strategy and doing it in a way, like I said, that is sustainable and isn't going to have it be a detour away from other things that they're trying to do. And so as you can see things like you don't see things like we just could not find the right use maybe or don't see the need. That's a minority view, pretty much
Janet King (33:27):
Even insufficient data sets is really a minority now. I mean, to your point, there's so much data, but it's more data. But it's about making sure that that data is all integrated together and that the quality is there. Going back to the governance and data management issue.
Nathan Golia (33:44):
And I think you start to see the governance come up in those middle areas, right? ROI or accuracy and reliability of algorithms. Let's also should talk a little bit about bias and ai, the concerns about running a foul of regulators down the road. That's one of the reasons why they might not feel that a solution is correct at this time. They may not feel that it's taking into account the fact what they're expecting from a risk management point of view down the road. So I think that looking at these indicates to me that insurance companies are taking the strategy and governance around AI seriously and are not going to implement something super quickly that they're looking for give and take with the AI and ML providers, or they're looking to build it internally because they want to make sure that it's done. And that again, gets ified. They've been able to see how these digital transformations have gone across other industries.
Janet King (34:53):
Maybe one good way to look at this too is to look at where AI and other advanced analytics are being successfully applied. So one of the things that we did in this piece of research is we honed in specifically on underwriting as a use case. And we asked in which the following areas are you deploying AI or other advanced analytics in the area of underwriting? So this is not just AI specifically, but just other advanced analytics investments. And what we found is that they are deploying these solutions for a number of tasks in the workflow, including estimating the value of a new or existing customer to identify unknown risks. So things that maybe people didn't previously disclose to obtain industry benchmarks. That's another one to help with risk scoring and kind of rounding out the top four is to leverage new technologies like telematics. So we talked a little bit about telematics and sensors earlier. What stands out to you here in terms of where these investments are going to support underwriting, which we know is a significant area of focus.
Nathan Golia (36:02):
I definitely think that the first two here are really closely tied, but when you're estimating the value of a new or existing customer, what you want to know is, well, what's their risk profile? And so you go look at some sort of, you go look at some new imagery, some better imagery that you got from a flyover satellite imagery and you see something or you're looking at some other things that are basically black and white. Is this happening? Yes or no? Right? And then can that is, that's the kind of thing where there's not a lot of uncertainty in whether or not the algorithm understands what's going on. Especially nowadays people might say using the solar panels thing, it's like, well, is it accurate? Am I getting the good? I mean, you would not believe what I've seen over the past few years in terms of the resolution and the intelligence being applied to the images of global, I'm sorry, geospatial data.
(37:03):
Geospatial data, not globals, spatial data. Geospatial data is really, really incredible. There's been a lot of advancements there because it's the kind of thing that can be implemented without much concern for it, and it's getting better all the time. Yeah, and then leveraging new technologies. I think that the AI is being built into these next generation insurance policies, and that's sort of the goal is to get them, it's a new kind of insurance product and AI is going to be part and parcel of that. So it's part of it from the start, the things that are down on the bottom here,
Janet King (37:41):
Regulatory audits.
Nathan Golia (37:43):
Yeah, I think that's that'll come, but it just isn't as top of mind right now,
Janet King (37:53):
Right? Yeah. I mean there's definitely this focus kind of the front customer facing parts of the lifecycle right
(38:05):
On this chart, which is interesting. But the good news is, so we're seeing some really good progress. I mean, if you think back to the beginning part of this chat where we were talking about that early data. So we're seeing insurance companies really lean into and becoming leaders in using data to advance transformation and drive those businesses forward. And when we asked these stakeholders to tell us how effective these investments have been at advancing certain business objectives, we're seeing payoffs come through. So we're seeing a solid majority who report that those investments have been highly effective, so extremely or very effective at advancing a number of key business outcomes, like improving not only customer satisfaction, but agent and broker satisfaction, improving risk selection, faster, quoting speeds, improved loss ratios. So I think this is really encouraging. Do you have any closing thoughts around this before we sign off today?
Nathan Golia (39:10):
No, I think I want to say is the reason you see it at the top and with agents and brokers being in there, that has been the complaint about insurance companies. When I started covering this industry about a dozen years ago, that was the complaint, never get an answer quickly. And that's what all these digital transformations have done. They've revolutionized the ability for insurance companies to get into contact with their key stakeholders, their customers, agents and brokers, and get those products and the answers to people. I think that it's good. I know agents are, there's a lot of agents, there's a lot of brokers. They have a wide range of views, but generally they are definitely in a much better, more advantageous position in terms of competing with a direct writer than they were when I started covering this because the agents have been brought into the fold and are being brought along the digital transformation path of the insurance companies. And that goes to everything else underneath that risk selection, quoting speed. When it's happening direct or through an agent, it's happening at pretty much the same speed, and I think that's going a long way per satisfaction.
Janet King (40:27):
I agree. So any final comments before we close today, Nate?
Nathan Golia (40:38):
No, I think, yeah, I just want to thank you for fielding the survey. I thought that I really liked how we got some very specific answers to some of these questions that will help us guide our coverage and yeah, I guess we can sign out here.
