Latest / In the Loop / Setting the scene: Lloyd’s Rachel Turk on AI in the London market

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Setting the scene: Lloyd’s Rachel Turk on AI in the London market · In the Loop

• 0:04
  Abbie Day: Hello, and welcome to the In the Loop podcast.
• 0:06
  Abbie Day: My name is Abby Day, one of the reporters at Insurance Insider, and I cover all things cyber, AI, and Insure Tech.
• 0:13
  Abbie Day: As part of my job, I have really fascinating conversations with market leaders about AI and its role in the insurance industry.
• 0:19
  Abbie Day: And now we're bringing those conversations to our readers and listeners.
• 0:23
  Abbie Day: So listen in to new episodes Fortnightly and join me as I dive deep into the fast-moving world of AI, talking to tech leaders and senior industry professionals about how this technology is changing the London market and how you can use it to your best advantage.
• 0:38
  Abbie Day: In this week's episode, we set the scene with Rachel Turk, Chief of Performance and Strategy for Lloyd, to get a feel for where the London market is positioned in the AI race.
• 0:47
  Abbie Day: So we are kicking off our podcast with a very exciting first guest.
• 0:58
  Abbie Day: We've got Rachel Turk, Chief of Performance and Strategy at Lloyd's, joining us.
• 1:02
  Abbie Day: Hello, Rachel, and welcome.
• 1:03
  Abbie Day: Hello, thank you for having me.
• 1:04
  Abbie Day: Thank you for coming.
• 1:05
  Abbie Day: I thought, given it's our first podcast, we could take a deep dive into all things AI and tech in the insurance market, particularly in London.
• 1:12
  Abbie Day: So, where is the PC industry, particularly in the London market, in the AI race?
• 1:18
  Abbie Day: Has it had a good start?
• 1:19
  Abbie Day: Are we already behind?
• 1:20
  Rachel Turk: I think there's a lot of positive action in the AI space in the market.
• 1:24
  Rachel Turk: I would say if you put us against personal lines, probably behind.
• 1:29
  Rachel Turk: But is the London market behind other P and C?
• 1:31
  Rachel Turk: Absolutely not.
• 1:32
  Rachel Turk: I think it's just it's easier to see how AI can get deployed in personal lines.
• 1:36
  Rachel Turk: It's just there's a lot more efficiency savings, it's much more standardized products, it's easier to deploy AI.
• 1:41
  Rachel Turk: We are seeing a lot of innovation in the market now.
• 1:43
  Rachel Turk: We're seeing a lot of trials, a lot of tests, a lot of pilots.
• 1:47
  Rachel Turk: You look at Lloyd's overall, it's been around for over 330 years.
• 1:51
  Rachel Turk: That would suggest a strong history of innovation and constantly reinventing the business model.
• 1:55
  Rachel Turk: So I have a lot of confidence that the PNC market will reinvent as AI deployment comes through.
• 2:02
  Rachel Turk: But I guess broad scale deployment, like deployment at scale, I haven't seen so much.
• 2:10
  Abbie Day: So how would you score its efforts?
• 2:11
  Abbie Day: How's the market getting on?
• 2:13
  Rachel Turk: So I think effort-wise, really high.
• 2:15
  Rachel Turk: There isn't anybody I speak to that is not thinking about how to deploy AI through their business.
• 2:21
  Rachel Turk: In terms of achievement, it's probably a little lower.
• 2:25
  Rachel Turk: I think every pilot that I'm seeing, it's just a bit harder than people imagine to make it truly revolutionize in the way that they would like.
• 2:34
  Rachel Turk: AI can then allow you to put the analytics on top of it.
• 3:01
  Rachel Turk: So I think you're allowing some companies that are slightly further behind in their like transformation journey to now miss out that step.
• 3:08
  Rachel Turk: So I think that's great because it means the industry will now be able to move forward more quickly.
• 3:12
  Abbie Day: Okay, and what's making it slightly more difficult than people expecting?
• 3:15
  Rachel Turk: Well, one is there's no lack of ideas.
• 3:18
  Rachel Turk: So lots of things that people want to do.
• 3:21
  Rachel Turk: So one of the challenges is actually prioritizing down that funnel of going, okay, we've got a finite amount of investment dollars and also, you know, because actually the tokens are quite expensive, what's we actually going to focus our time and attention on?
• 3:31
  Rachel Turk: And then if you think about how can AI be deployed in the insurance market, the initial easy use cases are efficiency gains through maybe the, if you think, especially on the underwriting side, the underwriting process, the claims process, et cetera, standardizing everything that we have and standardizing the product such that AI can run over the top is actually harder than you would imagine because the nature of the Lloyd's market and the London market is that it is bespoke client-led solutions.
• 3:57
  Rachel Turk: You know, we always find a way to say yes, as opposed to slightly more in the box, maybe take US admitted, which, you know, is going to go, here's a rating scale, this is what it looks like.
• 4:06
  Rachel Turk: And if you fall outside of it, the answer is no.
• 4:07
  Rachel Turk: Whereas when we try to find a way to say yes, that's actually quite hard to take that human wealth of experience and train an AI agent to be able to think in that way.
• 4:19
  Rachel Turk: And so that's, I think, is what making it slightly harder to deploy.
• 4:22
  Rachel Turk: So you're seeing it deployed in products that are probably a little bit more cookie-cutter, but a lot of what the London market does is by its very nature not cookie-cutter.
• 4:30
  Abbie Day: Yeah, it's hard to kind of bottle that secret source and deploy it with an AI.
• 4:34
  Abbie Day: I think when you move into especially those more specialist or niche areas, governance obviously plays a huge role.
• 4:41
  Abbie Day: I feel like it might be slightly easier to do effective governance around, like, say, the very cookie-cutter lines.
• 4:47
  Abbie Day: But when it comes to those sort of specialty or difficult lines, how big of a role does the AI governance need to play?
• 4:55
  Abbie Day: And how is Lloyd's kind of facilitating that governance structure?
• 5:00
  Rachel Turk: So I think from a governance perspective, for Lloyd's, we have the same expectations of our managing agents that you know their governance is very strong and risk management, et cetera, et cetera.
• 5:08
  Rachel Turk: And that's part of principles-based oversight.
• 5:10
  Rachel Turk: Whether they deploy AI or not, we have the same expectations that they have policies, procedures, and clear audit trail of governance decisions.
• 5:17
  Rachel Turk: So if you then take AI and put that into the mix, well, what do you need?
• 5:21
  Rachel Turk: The ones that I've seen that are the easiest are ones where the AI code, for want of a better word, is written in natural language, i.e.
• 5:28
  Rachel Turk: English.
• 5:29
  Rachel Turk: So then you could trace it through and there's an audit trail.
• 5:31
  Rachel Turk: If it's written in sort of binary one, zero zero zero one, I mean like who knows what that conversation is.
• 5:36
  Rachel Turk: Our reliance is does the managing agent, are they able to govern the decisions that AI is making or the implementation that they're doing?
• 5:45
  Rachel Turk: Have they put the policies in the procedures?
• 5:47
  Rachel Turk: Do they have the audit trail?
• 5:48
  Rachel Turk: Does their second line risk function, their third line internal audit function, are they doing the right things?
• 5:53
  Rachel Turk: That's where we're coming from.
• 5:54
  Rachel Turk: I don't think at this stage we should be doing and need to be doing more than that.
• 5:58
  Rachel Turk: We just set the expectations for the managing agents of going, deploy AI or don't deploy AI, we expect the same from you in terms of governance.
• 6:05
  Rachel Turk: Does it become a bigger issue down the line?
• 6:07
  Rachel Turk: I mean, I would hope not, because I think you should see, again, sensibly managed companies, there's no shortage of information around the risks around AI.
• 6:14
  Rachel Turk: And so again, if we're looking at it from a governance lens, a sensible management team is able to see all of those and therefore work through them.
• 6:22
  Rachel Turk: And that's what we're looking for, if we're looking for the evidence that they're aware of the risks and they're working through them.
• 6:26
  Rachel Turk: I don't think we want, from a lawyer's perspective, to be prescriptive about how they do that.
• 6:30
  Rachel Turk: We want to rely that the management team is able to think that through themselves and demonstrate to us that they've put the policies in place.
• 6:35
  Abbie Day: Okay, and is the EU AI Act that's coming in soon going to make any difference to that, do you think?
• 6:40
  Abbie Day: I would hope not.
• 6:41
  Rachel Turk: The whole point of principles-based oversight is that it's outcomes.
• 6:45
  Rachel Turk: It's not prescriptive rules based, you must do X, Y, Z.
• 6:49
  Rachel Turk: It's like the outcome is that your governance needs to be strong, whether it's governance over AI, governance over risk culture, whatever it may be, that shouldn't make any difference.
• 7:01
  Rachel Turk: So ultimately, principles-based oversight is looking at you as a managing agent, you work out how to demonstrate to us the outcomes that you have achieved, rather than us saying, tick these five things.
• 7:13
  Rachel Turk: So that's going back to minimum standards.
• 7:15
  Rachel Turk: And so that's why I think if we have to really shift the onus from Lloyd's saying, this is what you need to do, to the managing agents going, This is how we have demonstrated the outcomes of good governance.
• 7:26
  Abbie Day: And we've spoken a bit about the kind of efficiency benefit that can come from AI.
• 7:31
  Abbie Day: But I'd like to dig into really, for an underwriter, what the real value creation is with AI.
• 7:37
  Abbie Day: I think it's quite easy to look at technology for kind of innovation's sake.
• 7:42
  Abbie Day: What do you see as the real value creation?
• 7:45
  Abbie Day: Is it the efficiency benefit or the potential to improve decision making, data augmentation?
• 7:49
  Abbie Day: Where does that value sit for you?
• 7:51
  Rachel Turk: So I think it's probably all of what you've said.
• 7:53
  Rachel Turk: So if we take the underwriting life cycle, it is still an inefficient process-driven.
• 7:58
  Rachel Turk: So submission comes in from a broker, and then you might not have all of the data that you need.
• 8:02
  Rachel Turk: And so then you have to go back to the broker and say, well, actually, it's missing X, Y, Z attachments.
• 8:06
  Rachel Turk: Could we please have this?
• 8:07
  Rachel Turk: And then actually, oh, I need the loss run, and then I, you know, whatever it may be.
• 8:11
  Rachel Turk: So that's quite inefficient.
• 8:12
  Rachel Turk: So if AI can see a submission that it comes in and then talk to the broker and go, you know, actually, we're not even going to put this through to the underwriters until we have all of the information that's required.
• 8:22
  Rachel Turk: And all of that can be done pretty much if the AI is on the other side of the broker, that's seamless.
• 8:26
  Rachel Turk: It's not email traffic going backwards and forwards.
• 8:29
  Rachel Turk: Then you end up with a oh, we've got a fulsome submission now, which has taken, you know, likely seconds rather than days.
• 8:36
  Rachel Turk: And then you take that through and you go, actually, so then is this a submission that's sitting within our likely risk appetite?
• 8:42
  Rachel Turk: So if I'm now, if I'm an underwriter and I have a hundred submissions to work through, and I've only got time to work on 20, I want to work on the 20 I'm likely to bind, not the 80 that I'm not.
• 8:51
  Rachel Turk: So can I triage those submissions much more quickly?
• 8:54
  Rachel Turk: Can AI triage them and go, these are way out of risk appetite, a hero wants to work on?
• 8:58
  Rachel Turk: That feels like a huge drive for efficiency for the underwriter.
• 9:01
  Rachel Turk: So that the underwriter then the human overlay is being used to its best effect.
• 9:07
  Rachel Turk: So then you go into the efficiency piece.
• 9:09
  Rachel Turk: And then again, simple things, some of the pilots we've seen fill in the rating model, look at driving better decision making.
• 9:17
  Rachel Turk: I mean, the the classic example is when you get a hundred engineering reports, the best underwriter in the world doesn't open all 100.
• 9:23
  Rachel Turk: They open probably for the top 10 locations, maybe.
• 9:25
  Rachel Turk: But there might be something hiding in location number 40 that's actually a problem.
• 9:29
  Rachel Turk: If AI can read all of those, flag what the issues are, the underwriter makes better decisions because they've got more data and the more information.
• 9:35
  Rachel Turk: And therefore your pricing is better, your loss ratios are better, etc.
• 9:40
  Rachel Turk: etc.
• 9:40
  Rachel Turk: And then all of this is much quicker.
• 9:43
  Rachel Turk: So then you respond to the broker more quickly and you have a better chance of finding the deal.
• 9:47
  Rachel Turk: If you've got the right price and you're quicker, that's pretty helpful.
• 9:49
  Rachel Turk: So all of that from the underwriting life cycle, I think there's various elements there.
• 9:52
  Rachel Turk: Some is just pure efficiency, some is data augmentation, some is just providing much better risk analytics so that the underwriter is more informed and able to make better decisions.
• 10:02
  Rachel Turk: That's where I think AI can come through.
• 10:04
  Rachel Turk: If you take it onto the claims side as well, a long time ago now, when I was back underwriting, but e-discovery costs for big claims were running at sort of a million a month.
• 10:13
  Rachel Turk: Big numbers.
• 10:14
  Rachel Turk: And they quickly run through retentions and then to primary layers on casualty claims.
• 10:19
  Rachel Turk: If you can have AI running across all of those claims files and spotting issues and working out defence strategies, etc., that has to be that has to be more efficient from a claims.
• 10:29
  Rachel Turk: I think there's a huge wealth that you could do from a claims point in terms of claims efficiency, but then better claims outcomes.
• 10:34
  Rachel Turk: I don't think we should just be using AI for the sake of efficiency.
• 10:37
  Rachel Turk: I think you're missing a trick if it's just about efficiency.
• 10:40
  Rachel Turk: I think the data augmentation, the ability then to make better decisions is really key.
• 10:45
  Rachel Turk: Maybe it's an age thing and that sort of being a Luddite, I am still of the view that underwriting, it's always been half art, half science, is the way it's always been described to me.
• 10:53
  Rachel Turk: The science piece, AI absolutely can make better.
• 10:57
  Rachel Turk: So the data augmentation, pulling in different external data sources, whatever it may be, the half-art piece, I think that is still a huge element in this industry.
• 11:06
  Rachel Turk: That is human interaction, relationships, knowledge of what your market is doing, knowledge of trends that are happening elsewhere, knowing what the right price is, which might not be the same that the technical rating model says, but you just know from a lot of experience, that I think AI will struggle to replace.
• 11:26
  Rachel Turk: But a lot of the rest, AI can help you make better decisions much more quickly.
• 11:32
  Abbie Day: I'm interested that you mentioned both the claims and the underwriting side.
• 11:35
  Abbie Day: Is there a way that AI as well can maybe bring the market out of that siloed way of working, bringing the claims information into the underwriting and the other way around?
• 11:44
  Abbie Day: Without a doubt.
• 11:45
  Rachel Turk: Claims really is the forefront of lost development trends.
• 11:50
  Rachel Turk: So they see all the lost development trends.
• 11:51
  Rachel Turk: And if you're not having those reflected back into your underwriting, your pricing model, you're really missing a trick.
• 11:56
  Rachel Turk: You need reserving as well.
• 11:57
  Rachel Turk: So that lovely triangle of reserving claims and underwriting data should all, if that flows nicely, you're much, much more informed underwriters.
• 12:04
  Rachel Turk: What London Market, Lloyd's, and actually I think most insurance companies have struggled with, quite frankly, is the claims data and the underwriting data.
• 12:13
  Rachel Turk: It's quite hard to match them together.
• 12:15
  Rachel Turk: Sometimes it's really stupid things like take an example of Alphabet, which is Google.
• 12:21
  Rachel Turk: So Alphabet in underwriting files will be known as Alphabet.
• 12:25
  Rachel Turk: But in a claims file from 10 years ago, it could still be as Google because it changed its name.
• 12:29
  Rachel Turk: The same as like Meta and Facebook.
• 12:30
  Rachel Turk: So in an underwriting file, so then you're sitting there going, well, how do I match it up?
• 12:34
  Rachel Turk: Because actually it just is not matching.
• 12:35
  Rachel Turk: Whereas AI can sit there too.
• 12:37
  Rachel Turk: You've got that even just a suddenly easier way of matching your losses.
• 12:40
  Rachel Turk: That's why we've always struggled, sit there struggling with name matching of going, well, the company changed its name, but obviously in the claim, it's still as that original claimant, but the underwriting files, it's evolved.
• 12:49
  Rachel Turk: That's always been a huge problem.
• 12:51
  Rachel Turk: AI can fix a lot of that.
• 12:53
  Rachel Turk: And so suddenly you've got much better flow between claims data, underwriting data, and that ability to then just spot early lost trends that can then help change pricing models, your technical rate accuracy is better.
• 13:05
  Rachel Turk: That all feels like it's where the industry should be going and should be hugely beneficial.
• 13:09
  Abbie Day: That's really interesting, that it's not necessarily a huge technical overhaul, it's quite a simple thing that AI can do that really makes a huge difference.
• 13:17
  Rachel Turk: Yeah, I think with AI, it's a huge transformation that's required.
• 13:20
  Rachel Turk: I think if you can iteratively test and do small tweaks as you go, just like name matching, which makes quite a big difference, that's easier to do.
• 13:29
  Rachel Turk: And it also proves the use case rather than going, I'm going to do like a three-year transformation using AI, it's just they don't tend to work.
• 13:36
  Rachel Turk: You're like, we're going to do three months to do this and then move on over the next three months, you're much more likely to deploy at scale, much more quickly.
• 13:42
  Abbie Day: Okay.
• 13:43
  Abbie Day: And looking ahead, what does good or great look like for you?
• 13:46
  Abbie Day: What should a market using AI well look like?
• 13:50
  Rachel Turk: Look, I don't think there's a single answer of what good looks like.
• 13:52
  Rachel Turk: I think good is up to each individual managing agent as to how they want to deploy enhancing underwriting insights, speed of underwriting decisions, portfolio management, whilst I think keeping that human judgment element, enhancing data quality, robust governance, and that's to me is good.
• 14:10
  Rachel Turk: But again, I come back to I think AI, what good looks like is strengthening human judgment, not replacing human judgment.
• 14:18
  Rachel Turk: And that to me is really key.
• 14:20
  Abbie Day: Okay, and it would be great to know what you think are the biggest challenges that the market faces with integrating AI going forward.
• 14:27
  Abbie Day: Is there anything that it really needs to overcome to reach that good point?
• 14:30
  Rachel Turk: I mean, I think part of it is a willingness to try.
• 14:33
  Rachel Turk: I think with the challenge that I see from a lot of managing agents I speak to is working out what to focus on.
• 14:38
  Rachel Turk: It's like it's a classic prioritization problem that they have.
• 14:41
  Rachel Turk: There's again I said there's no shortage of ideas, but you've got to focus on a few things.
• 14:45
  Rachel Turk: And then the I think the other challenge, honestly, for the market is so obviously we spent this time talking about AI deployment at the managing agents.
• 14:54
  Rachel Turk: They've also have to all think about AI deployment in the end customer and how that changes the underlying risk profile and that AI liability that is going to be around the market and what are they going to do with that.
• 15:05
  Rachel Turk: So, from an underwriting lens, the underwriting of AI liability is a totally different topic from how AI is going to be deployed at the managing agents to improve efficiency.
• 15:15
  Abbie Day: And it's definitely a topic we're going to cover on the podcast.
• 15:17
  Abbie Day: So stay tuned.
• 15:18
  Abbie Day: Rachel, thank you so much.
• 15:20
  Abbie Day: It's been absolutely fascinating talking to you about AI and the London market on the podcast.
• 15:24
  Abbie Day: Thank you for joining us.
• 15:24
  Abbie Day: No worries, thank you very much.