[00:00] Paul Lucas: Hello everyone, welcome to Insurance Business TV and the first meeting of our leaders network. Now in this edition, we'll be combining several groups. In the weeks to come, you can look forward to meeting separate networks on transformation, cyber, professional risks, and more. But today, we're taking a selection of those groups to look at moving beyond simple automation toward AI adoption, data strategy, and structural change across underwriting, claims, and distribution, something that is of course impacting all of our businesses.

[00:32] And here to guide us, we welcome Jennifer Wilson, cyber leader at WTW, Jessica Theer, financial institutions practice leader at Stockweller and Shepley Insurance Brokerage, Joe Earl, cyber group practice leader at C3 Risk and Insurance Services, Jackie Castellis, claims advocate lead at Alliant Insurance Services, Alan Madden, senior vice president and producer at Alliant Insurance Services, Dale Kupravich, SVP operations and client service at Howden, and Emily Selk, senior director, national practice leader cyber at the Baldwin Group.

[01:10] So, welcome everyone and let's start with a little bit of an overview if we can. And I'd love to know where has technology genuinely transformed how your business operates in the last few years perhaps versus where it has just added complexity without real payoff. Jennifer, I'll start with you.

[01:26] Speaker: Yeah, so I'm with Newfront, recently acquired by WTW. Newfront is a national insurance broker focused on technology. Newfront has developed quite a lot of technology tools that are focused on creating efficiency and accuracy and transforming the way that we do the brokerage process, from creating a platform that houses all of our clients data from applications, policies, contracts, claims, etc.

[02:05] We also have developed a contract review tool that reviews contracts for our clients and makes recommendations on appropriate coverages and confirms compliance with the policies that are procured by that client as well as a coverage gap analysis that reviews quotes and policies and provides a measurable comparison. So the tools that we have developed have created significant efficiencies in our roles which I think over time we have all seen.

[02:43] It's opened up a lot of free time for us to spend more one-on-one time with our clients, which that's the goal, that's a purpose. We have not created any tools that we're not using or have not created transformation.

[02:58] But I think kind of the one surprise that we found with all of these tools that we're utilizing is that it took a lot of investment, not just from the engineers but from the staff, to get these tools up and running and that was something I think people weren't quite prepared for at the time.

[03:18] So what I mean by the time investment is we had to input all the data that these tools are using that have now created significant efficiencies. In addition to having full-time, over-capacity jobs, we now had to find time to input a massive amount of data to get these tools up and running.

[03:40] And then some of us were tasked with training the tools. The engineers are creating the tools, but the insurance experts are training the tools. So those are kind of things that we hadn't anticipated to be part of our job. But once all that investment was put in, we're now experiencing the efficiency and ease with which these tools are offering our jobs.

[04:07] Paul Lucas: So it sounds like it was a little bit of a marathon to get there, but now you're enjoying the sprint section, if you want. Jackie, tell us about the biggest impact of technology in your business.

[04:18] Speaker: I would say the biggest impact that technology has had on our business is streamlining processes not only internally but for our clients and being able to have accessibility anywhere, right? It's no longer something that we have to be in front of each other. I think COVID taught us that lesson that we're able to do a lot of things from wherever we're located.

[04:50] And also enable us and empower us to not only make better decision making in those processes but also support each other, right, as quickly as a click away of being able to support each other.

[05:02] I will add to Jennifer's complexities. I think that the people element will always continue to add to those complexities because as long as new tools are introduced without the clear direction, as Jennifer stated, that they had to upload all the data for the engineers to utilize, the purpose wasn't clearly explained.

[05:24] Proper training will only set us up for success when introducing new technology and tools that we can utilize.

[05:24] Paul Lucas: Okay, so it sounds like a little bit of a mixed bag so far. A lot of positives were taken from these technologies, but it's proven quite complex to get there. Dale, I know it's impacted the underwriting side of your business. How has that taken effect?

[05:35] Speaker: Yeah. I would say that any tools that give our colleagues a more complete view of a client's risk is a good thing. And so we've seen it particularly helpful in the property intelligence area where we're bringing together characteristics of a home such as wildfire, flood, hurricane, loss information and taking that to the underwriters where they have a lot more data about a risk and they can make decisions faster.

[06:09] It improves how we submit business, identifies potential underwriting concerns, and we could advise the client on risk mitigation related to it to make their homes more insurable and add value to what we're offering them.

[06:29] So in that particular area, it's helping us in all areas, but we want to concentrate on one area at a time and I just feel like property intelligence for my space has been really significant.

[06:43] Paul Lucas: And of course when we're talking about insurance, no conversation is complete without some discussion of AI and automation. They are widely discussed. But in your opinion, what is the honest state of adoption in underwriting, claims and distribution right now, that's perhaps a little bit separate from the hype? Emily, any thoughts on this?

[07:02] Speaker: Yeah, I think that when we look at AI and automation, and we look at the traditional process of procuring insurance, it's very symbiotic in nature. We are working together on the brokerage side, with the underwriting side, and often with the claim side as well to make sure that there's a transfer of information, a synthesis of information.

[07:21] And now we're building these in silos. What brokers are doing is different than what underwriting is doing, which is different than what claims is doing even in the same organization.

[07:29] And so rather than creating this free flow of information that puts us in a position where we can better understand a client's risk and have more meaningful conversations around them and create a more efficient process where everything's getting to clients faster, we're giving them better information faster, we have to build these with each other in mind.

[07:47] And I don't think that we're doing that yet. I think we're still in this place where we're so siloed because we're still trying to understand what is the practical application of all this to our businesses.

[07:58] Paul Lucas: Jennifer, it looks like you're nodding along.

[08:01] Speaker: Yeah, everything Emily just said is spot on and I'm seeing it in real time. Not just are the brokers and carriers adopting AI at different levels, but the brokers are adopting AI at completely different levels.

[08:19] So the underwriters are receiving submissions from different avenues, different technology standpoints and at some point we all have to get together, like Emily said, and figure out and streamline the process so we're all working within the same technologies.

[08:39] Paul Lucas: No conversation about technology is complete without touching on data at some point and particularly the better use of it. So just talk about how data is changing the relationship between brokers, MGAs and carriers, particularly around risk selection and speed to quote. Alan, have you seen any impact here?

[08:58] Speaker: Yeah, absolutely. I would say on the sales business development side, when you're working with your clients and you're going through renewals, we're seeing AI come into play. I'll give you an example.

[09:12] Like monoline work comp carriers, we're seeing a lot of them offering AI-driven solutions both on reducing claims and setting them up for lower premium on the get-go.

[09:27] So on the hope that introducing AI concepts that are real time, having cameras in facilities, wearables, what have you, giving real-time feedback versus a loss control, risk control situation every quarter, we're seeing that being adopted more and more in that work comp space for the carriers.

[09:53] Also, when we're looking at property submissions going to London, they're using AI to do automatic selection for risk. So there are line slips now that are purely AI-driven and that's coming to the brokerage world as well.

[10:10] So Alliant itself has a slip that is AI-driven that matches capacity. So you're seeing a lot of capacity being guided and segmented to different risk via AI, not really an underwriter involved.

[10:32] And also one of the things is that because there is so much more information and data out there, for us on the broker side, we have to present the risk profile and the story of our clients more importantly than ever because underwriting has such a massive amount of information.

[10:58] We have to say, look, yes, you're seeing this or you might have a question about this, but this is what they actually do. This is reality. So it's kind of taking more the human approach, the human intelligence, to counter some of that artificial intelligence that you can tap into.

[11:17] Paul Lucas: Okay, I like that it's sort of merging that human approach as well. So, Joe, from your perspective, just tell us, are you seeing perhaps underwriters use AI to actually find clients, to identify clients as well?

[11:30] Speaker: Absolutely. Underwriters are very tech-savvy. They are using Yelp and Google and all this stuff. For the last 10, 15 years, they're looking at anything they can find as far as big data. And now they're using AI as well.

[11:45] So AI is a great tool for underwriters. They can put the name of the company in there. They can put the application in there and do some open research on this company.

[11:56] In addition to that, they have tools that they've acquired in order to take big data, take the information on the application and give it a risk score and go from there on where they're going to price it, if it's going to qualify, and if they even think that they can win the business.

[12:14] Paul Lucas: Yeah. And Jessica, from your perspective, would you agree with Joe? Do you think that AI is proving useful in terms of getting information to underwriters?

[12:23] Speaker: Absolutely. Yes. We've partnered with a platform that allows, especially in the financial institution space where it's highly regulated, a lot of financial data available in places like the Securities and Exchange Commission website.

[12:39] We're able to drill down on the data and the information that the underwriters need through the application process and it precludes having the client go through that information and try to provide the information that they think the underwriters would like or are interested in in their underwriting process.

[12:59] So it drills down on the data very quickly. We're also able to show the client where the areas of concern could be through this platform so that they can provide additional color, additional documentation, etc., to take the back and forth of the underwriting questions from the onset.

[13:18] From there, we're able to get the data and the quoting way more quickly than we were in the past because all of it is in a format that the underwriters are able to underwrite very quickly and then provide the quote.

[13:31] And then as we build very large towers, there's transparency as to where we are in the tower. There's not a lot of back and forth regarding where the pricing is coming in. They're able to see it in real time.

[13:42] So it just takes a lot of the questioning out and the transparency. It implements the transparency back into the process and it's just a win-win for all parties.

[13:54] Paul Lucas: Yeah, really interesting to hear about all these sort of positive applications of AI and technology in general, but I'd love to find out on the flip side as well, perhaps where the biggest barriers to technology adoption are in this industry right now. Is it legacy systems? Is it talent, regulation, culture, and how are those organizations working through those barriers? Jackie, what are you seeing?

[14:16] Speaker: Thank you. I will say to me the biggest challenge is change itself. No matter how many people say they embrace change, people get very comfortable in their routines and the programs that they use and processes that they have.

[14:33] So with that being said, I think adopting new technology requires more training and willingness to embrace a different way of working. And I think most organizations that are most successful don't implement new technology. They invest in helping their employees understand why the change matters.

[14:50] It's not just saying, "Hey, this is the new platform we're using today." It's understanding how this is going to benefit the employee, that return on investment for your employees.

[14:58] Paul Lucas: This is such a great point, isn't it, Emily, that Jackie is making there. I mean, I do my job perfectly well. Why do I need to change it?

[15:06] Speaker: Yeah, absolutely. I think that there's just such a lack of understanding around AI and the functionality of AI. So it's not just about how do we apply AI to our everyday work, it's the granularity of understanding how we apply AI to our everyday operations.

[15:24] Does that granularity reach the C-suite and the leadership team that's making the decisions around AI? That's not always the case. Sometimes it's just kind of being thrown out there.

[15:35] We understand what AI is on kind of a conceptual level, but we don't understand the day-to-day practical application of it. So I think the implementation is pretty messy right now.

[15:43] I think we have a lot of tools that are being thrown out there. We're using them in ways that maybe are appropriate or maybe are a little less appropriate and some of that is understanding what AI can do, what AI can't do.

[15:58] But also having people making the decisions understanding every single step down to the most minute level of what we do in our operational functions so that we can automate where we can and really use the human intelligence in a meaningful way where we can as well.

[16:15] Paul Lucas: And to Emily's point, Jennifer, just to go back to what she said there, that C-suite buy-in, that's crucial, isn't it?

[16:21] Speaker: Yes, absolutely. And this is kind of touching upon what Jackie and Emily both said. When we rolled out our technologies, the engineers were so excited and threw these new systems at us and said, "Okay, great. Now go at it. Do your job more efficiently."

[16:42] And to Jackie's point, it was really difficult for some people to just adopt the change. Then we had to collectively as a company take a step back and say, okay, what can we do to encourage folks to embrace change?

[17:01] What we needed was the C-suite to jump in and make sure that everybody understood how to use the tools, when to use the tools, and create the guardrails, but then incentivize staff to adopt the new procedures because change is difficult for a lot of folks.

[17:19] And rather than doing a companywide training, we put together very small groups of training so people would be more comfortable asking questions and trying things out and getting more comfortable with the process.

[17:34] So I think it's a lot of things. It's understanding the guardrails, but it's also figuring out how to train the staff so they're comfortable using it.

[17:43] And then the last point I'll say is this is an industry that's centuries old, mired in paperwork. So legacy systems are absolutely a challenge for this industry. So that's going to be a barrier for sure.

[17:58] And then it's the generational gap, right? I think whether it's internal or with client base, some technologies are not appealing to a certain client base and we have to be attuned to that as well.

[18:16] Paul Lucas: Yeah. And I'd now like to ask you all to gaze into the crystal ball somewhat if you don't mind. Let's look three to five years out. What does a genuinely transformed insurance business look like and what has to change structurally to get there? Joe, any thoughts?

[18:37] Speaker: I think in the future we're going to have three types of companies. We're going to have companies that use AI as an excuse to lay people off. We've seen that in some Fortune 500s.

[18:47] We're going to see companies that use AI in order to use their staff better. I think we're seeing a lot of that right now where we're augmenting our staff so that they can do more roles. We don't have to hire any other people.

[19:01] And then we're going to see the innovators that are using AI for exponential growth. They're going to be hiring AI-enabled people. They're going to be training their current people to use AI.

[19:12] And those are going to be the winners moving forward. I don't want to be super pessimistic, but I do think that we may have a break in the next generation where people that have grown up with AI have been using AI throughout school and throughout college.

[19:35] They've really been outsourcing their critical thinking. I think we need to be able to look ahead on that and make sure that we're hiring people that can still use their brain as well as use AI.

[19:48] Paul Lucas: I'd like to have a little bit of a word of caution from Joe there. Dale, from your perspective, what does a transformed business look like?

[19:56] Speaker: Yeah, I agree with Joe. The human aspect is always going to be important and we're going to need to have decision makers.

[20:05] But I see five years down the road that AI will be embedded in people's everyday work, but to handle more administrative tasks and low-valued things so that we can concentrate on the relationship with the client.

[20:19] I think the transformed broker will not necessarily be using the most AI tools, but they'll have a model where data, tech, and human expertise all work together and I think that type of broker would be successful.

[20:36] Paul Lucas: Alan, from your perspective, are you actually already perhaps seeing some transformation within your business?

[20:42] Speaker: When you look at kind of hype versus reality, I think we're coming out of hype trying to get reality going.

[20:56] So one thing we did on AI just as a tool, Alliant actually told all employees, "Hey, don't use ChatGPT right now or Claude or whatever until we are able to study it, understand the liabilities of it, put the guardrails in, and then we'll appropriately roll it out to the organization."

[21:20] So, we've done that and now we're in early days. I think about a quarter of us, call it 4,000 out of the 16,000 employees, have a unified ChatGPT Alliant platform and we can use it.

[21:36] Everybody had to go through training, get certified to use it. Every month there are seminars and webinars that kind of show you how you can use AI within the business better and help it in your daily workflow whether you're in production, sales, admin, account managers.

[22:00] So it's a slow but thoughtful process. On the flip side, Salesforce Gold is kind of an AI-enabled version of Salesforce that really brings to bear a lot of AI-driven tools onto that platform.

[22:24] So it's a great client management tool, prospecting tool, forecasting tool, a tool that allows our management team to see where we might need to hire in the future.

[22:37] And so that's kind of the here and now, but it's incorporating a lot of AI tools from third parties in helping us manage the business and do a better job for our clients.

[22:51] Paul Lucas: Yeah. So I'm sure they'll appreciate the shout out as well. Alan, and Jessica, finally, from your perspective, how do you see this impacting business going forward?

[23:00] Speaker: Yeah. So, I think within the next three to five years, we're going to see a flight to quality. And I think that's twofold.

[23:08] I think that's in the way that organizations protect and, we hear the word guardrails a lot, provide guardrails around the information that is being inputted into these AI platforms and the security around it.

[23:25] And I think there could be potential issues with some organizations failing to do so. So I think there'll be a flight to quality for those organizations that put security in an elevated state.

[23:39] And then the second is I think there would be a flight to quality to find those that are really taking all of the operational efficiencies and the data that AI provides and being able to counsel and consult clients appropriately.

[23:56] I think everyone will be able to place coverage easily and it's just a matter of doing what's right and in the best interest of our clients. I think there will be just overall a flight to quality.

[24:10] Paul Lucas: Definitely a quality-first edition of this leaders network. Great thoughts and a fascinating start indeed. Remember a series of these groups will be meeting on a regular basis. So keep it right here on Insurance Business TV.

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