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Alan Strange on Outstanding Claims with the Price Writer Ep 9
35 Plays1 year ago
Today on Outstanding Claims I am hearing from Alan Strange from Sophro. We hear how he started by taking calls from customers and now sets the prices and leads in data and analytics. The conversation touches on:
🗣️Alan's transition within the call centre.
✍️The switch to underwriting roles, offering hands-on experience and decision-making opportunities.
🛠️Emphasis on self-improvement and leveraging new tools for data analysis.
Stay tuned to learn more about Alan's vision for pricing.
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Transcript
Introduction to Data Requirements
00:00:00
Speaker
I suppose the key thing that I've been pushing along the way is to make sure that the people right in the front end, all the way through the process, understand why I want the data that I'm asking at the back end.
Who is Alan Strange?
00:00:12
Speaker
Today I'm joined by Alan Strange from Sofrae. Alan has a very successful career pricing products like after the event legal expenses, travel and breakdown. So that's me Alan. Hello Alan and welcome to the show. Hi, thank you for having me on.
00:00:28
Speaker
Thanks a lot for being on today.
How Did Alan Enter Insurance?
00:00:30
Speaker
So the first thing I want to ask you is, how did you get where you are today? I gave both away for most people. I fell into endurance. I did my degrees in banking and finance. And whilst I was looking around for a job in bank, I started temping at ICT London, Edinburgh. In Worthing, they had their head office there. So one of the big employers there. And I was in the Motop call centre.
00:00:54
Speaker
I never really thought about insurance before, but I enjoyed the work and so I decided I would stay there. So I started off answering queries from run occurs, dealing with underwriting questions. And really I've always been interested in statistics and data. So looking for other roles where I can enhance that area. And the job came up in the call center during the shift planning and call forecasting. I took on that role and building regression models in Lotus 1, 2, 3 in those days.
00:01:23
Speaker
Freeexcel, and then I really enjoyed doing that, but missed the hands-on underwriting side. And so I looked at internal vacancies within the company.
Why Choose Underwriting Over Actuarial?
00:01:35
Speaker
I got a couple of roles, one as an actuarial student or another in the household line of business, in the underwriting team, doing the performance reporting analysis there.
00:01:46
Speaker
I looked at both of them and thought I really enjoyed the sort of hands-on underwriting. I didn't want to just be stares as it was, just doing the numbers, passing it down and say, here you go, follow our instructions. In the underwriting team, I got to deal with queries from the clients, make underwriting decisions. I managed to do relationship. I did the performance reporting for various large schemes. So we, we underwent a number of large build society schemes, lots of data there, building up triangulations, seeing how things pound out, looking at the rating reviews.
00:02:16
Speaker
So I really enjoyed my time
Career Transitions and Restructuring
00:02:18
Speaker
there. Then along came Noyes Union and the client company and they wanted to move all of the head office functions to Noyes and I didn't very likely be located to Noyes. So I started looking around, saw another role. And one of the clients at London Adding threat was a company called Intermediate Shifflings Limited. You did a lot of what ifs.
00:02:39
Speaker
pricing work, they did some software where you could put in your criteria and it'll tell you what the whole market charge to that bit to do the benchmark pricing. And I often without any vacancies going and went and joined them, building PEL's models for households, say the flood models, substitute models and so on, doing some lifestyle profiling, building rating structures for various insurers.
00:03:04
Speaker
again really helped me with statistical techniques and building these models from the data, where to find the data, what to do with it. Enjoyed that, but the problem was you'd build these rating structures for the insurers, they'd go away and apply them, they'd never come back and say, thanks we made lots of money, or even worse, sorry we lost lots of money. I guess we probably would have heard of losing lots of money, but knowing the results of my work,
00:03:28
Speaker
so I wanted to get back into insurance. So I then went to Europe Systems in Heywood Heath as a development underwriter initially, which was looking at motorbike down, travel insurance, home emergency, sort of niche add-on type product.
Data Sophistication in Insurance Pricing
00:03:43
Speaker
And it was surprising to me just how little youth they made of the data that they were capturing. It was pretty much, you want to motivate down policy, here is your price. You want to travel insurance policy, you're going to Europe, you're going to the rest of the world, you're going to America. That was pretty much the sophistication in your pricing. Maybe it did announce a broad age ban, so over 65, under 65. But there was so much data there where you could break down
00:04:10
Speaker
injury or an accident. What type of illness did someone have? Big differences between medical cots and spines, even with the EY-11 cots and spines, cots and spines. They could vary significantly. So I was trying to build that level of sophistication, so I persuaded them to get me a license from SPSS. I built up a team of analysts there to start adding more sophistication to the products. And then one day my boss came along and said, do you want to come to a meeting this afternoon?
00:04:40
Speaker
Okay, what's it about? Do you want to come to a meeting? Okay, that's interesting. So sat down there, so we had our external actuaries in as well, and we had a central client in talking about this weird product called after the event legal expenses insurance. I'd never come at a loss. And the late afternoon, listening to what this product was, how it worked, that was good. And at the end of the meeting, my boss said, Alan here is going to be fighting this product.
00:05:05
Speaker
than what brilliant that you just heard of
00:05:13
Speaker
I mean, this was back in about 2001, so this was a new product. It wasn't for a lot of days without. So I then spent the next few weeks, do you want to find anything I could on this product and try and figure out what I was going to do, how I was going to price it. And so it's deep learning curve, but it was fun and interesting. And because of that, buying it up, heading up the fantasy
00:05:37
Speaker
at Europe Systems, but also any other and new products that came along would be passed my way.
Joining a Startup in Gibraltar
00:05:43
Speaker
So we did that for a few years, but then Europe Systems, as a wider company, decided they wanted to go back to basics, focus on what worked
00:05:53
Speaker
for the company throughout your and the world. And they don't think they're an assistance company, they're in their insurance. They certainly don't do this niche little legal expenses insurance cover they're working in and the worlds that no one else in the world. So they were looking to call back to Matt, whereas my boss was keen to do his own thing. And he brought me along as he went to set up his own insurance company in Gibraltar.
00:06:15
Speaker
called LAMP Insurance, and so I left the aid in 2005, and I'm the writing director of LAMP, and we focused on Link-Link Mentors Insurance. We also liked the athlete medical health care and various other niche products that came along that we felt
00:06:32
Speaker
with better knowledge and in the market, or more data than the market, we could do without massive overheads and call centres. So we were doing things like gap insurance, motor warranty, we supported a household MGA. So they kind of niche where we apply knowledge rather than resource to make it work.
00:06:53
Speaker
And so again, I've got a bit of variety there in pricing make sure from some day one there we were building our own technical systems because there isn't a lot of shelf system that works with these kind of lines of business so at least I could develop make sure that the data I wanted back and I was capturing sound and
00:07:11
Speaker
And I had access to that, so putting it off as the secret server so I could do what I wanted. I didn't want someone to come along and say, here you go, here's your standard reports that you get every month, and I'll write my own report. Give me the data. Because if I want to change it, I don't want to talk to someone in IT and wait three months for them and prioritize it. And then I can build it myself. I'd rather have too much data and aggregate it than not having enough on anything. How on earth am I going to capture it now?
00:07:38
Speaker
So I had a lot of fun developing that. I moved into role as CIO after a few years there, so focusing more on the back end pricing and analysis and less on the front end underwriting. And this was around the time that Microsoft were heavily invested in this area, so we had power pivot was becoming.
00:07:59
Speaker
and merging into Power Query and Power BI managed to come across that quite early in the days. So back when they were launching the start and been learning that ever since with all the developments that have come alongside that with Power Automate, Power Apps and so on.
Transition to Software and Technical Systems
00:08:15
Speaker
So it's looking at new technique and applying those with the various lines of business that we were writing.
00:08:21
Speaker
Unfortunately, managing to find actual problems, a lot of the afternoon event premium was deferred, so you didn't get paid until the case is concluded. You had the defendants who were fighting how much premium they had to pay, so holy not the cash flow, and essentially we ran out of cash.
00:08:37
Speaker
So looking around for a new role, I've got Tolkien, a mutual friend of mine, to an MGA that was setting up in the legal inspector space called Software, and taken on there as head of underwriting and RSS. And again, the advantage there was they did have a technical system that had been used elsewhere, back then developed for ATE insurance, and it was easier then to add all the extra bits I wanted to that rather than start from scratch.
00:09:04
Speaker
So again, making sure I capture that data, but I didn't want to be just boxing to one particular line of business. I'm positive that I also had the opportunity to get involved in an intellectual property, MGA as well as a chief data analytics officer. Again, not a lot of data out there, kind of find what I could and apply the best I could to what we were trying to do. So the USPTO, there's a lot of data there on litigation in America, on patents.
00:09:31
Speaker
So putting that information, how to match that onto our data, how to apply that to build our pricing model. So it's all been very interesting and yeah, picking up what I can with, with pricing techniques along the way, working alongside external actuaries, help done in, in various roles, working with the regulator, with solvency to came in, looking at what needed to go into that. So whenever we had the, the actuaries in for a quarterly, there's there for you that bring up on the big screen, there's cheese and type.
00:09:59
Speaker
This is how we got to our mindset, and I was getting along the way, and copying down what they did, and it was like a replicator, which had the advantage that instead of every quarterly result, you would force the company's results to match what the actuaries were saying. You could end up with a big jump, up or down, depending on how the people could perform in the end.
00:10:20
Speaker
and I wanted to smooth that so I would take what they were doing on a quarterly basis quite as best I could in the interim months so it made a much smaller job at the end when you had the reserve review and hopefully it gives better idea of where the result was going to end up. And what's it like working in an MGA where you're the only pricing person?
00:10:44
Speaker
They were positive than negative. And on the blood side, then I can just get on with it, build my own thing, and start to go through various levels of my life. And in me, I keep my fingernails applied. I'm doing the data engineering, I'm getting the data, I'm cleaning it up. I'm doing the analysis, I'm applying the results of that analysis, rather than in a particular sign, I'm just doing a bit of it. So yes.
00:11:07
Speaker
So there's advantages to being able to have the view right from the start to the finish. The disadvantage is very sort of sounding bored, or if I get diked, I thought, Mr. Gengle perhaps rather than another team meeting members to look at and tell some ideas off and suggestions.
00:11:25
Speaker
And also, again, being a small company, you can't really justify the expense. You show any things that you see out there and think about making life so much easier. But for a niche player, a small company, you're never going to justify the cost. I was quite lucky in my career as well. I started in quite niche products where I got to do everything start to end.
00:11:49
Speaker
And then as I moved more senior, I actually moved into the bigger product. So it was then I started running teams that I already knew how to do every part of the job. But what would you say, Alan, is your mission for general insurance pricing?
00:12:05
Speaker
So I suppose the key thing that I've been pushing along the way is to make sure that the people right in the front end, all the way through the process, understand why I want the data that I'm asking at the back end. Because if we don't understand that, you end up not capturing any data at all with a leave it blank. And then you try to get around that by making it a mandatory field. And I have an example of this at a previous company where
00:12:29
Speaker
When I started doing some analysis of the age of the customer, it was amazing how many customers I was saying they took well. And we turned it up, basically the person who was inputting with, so they didn't have the information, you just put in his own data. And so you ended up with rubbish out because it's not captured. And you just need to explain at the funding why I want this. They may not see it for what they're trying to do, they're just handing claims. We don't need it for that role.
00:12:53
Speaker
doesn't matter they need to see the wider picture. So it's just making sure all the way along as much as possible we can capture the data that I need and they understand why I'm asking for it to try and get that eye in so that they don't find if there is a way around it someone will find a way around.
00:13:10
Speaker
Yeah. Do you know, I can remember a similar story where it was the the postcode of where the loss event occurred. It doesn't matter which line of business it was on, but there was a really strange number that were all at the same postcode, which turned out to actually be the office of the claims handlers.
00:13:29
Speaker
Yeah, so I see that as well. Absolutely. And what would you say then is your vision for the future of general insurance pricing?
The Role of AI and Multi-skilled Teams in Insurance
00:13:39
Speaker
The key bit is making sure, as well, if you are capturing all this data you actually use. So I've done some work with an MGA in the past, taking up lots of space, how they weren't using it. I suppose it was a good thing to imagine where to start. But there is a fact that I thought, why hold it if you're not going to use it?
00:13:56
Speaker
So make sure that you are utilizing what you've got, understanding why you've got it, what you can do with it. That is going to be greater use of AI in the sort of the data wrangling aspect. A lot more tools coming in to help you with that. A lot more click and drag rather than the hard coding that it can make to shape that data. And then it's coming to a more sort of unified solution so you don't have data in various bits. So Microsoft Fabric is a great example of that.
00:14:24
Speaker
and where they're building together a solution where you've got your data lake and then you put your warehouse on top of that, you can do your pipelines, you've got your notebooks for your analysis and then you've got the front end power being able to do the visualization. So you're not replicating data, you're not taking it from one system to another, you can all have it in one place. But as I was saying, it's one of those tools where as a small company, I'm not quite sure that you can justify the cost. I know it does go down depending on how much processing power you need.
00:14:54
Speaker
But that kind of thing, trying to not pull all your data into one place and have the whole process so that any changes, you're not trying to find 50 different places where you need to amend your code. You don't have that knock-on effect. Change it once and it should flow through so that you don't end up with rubbish coming out and cutting your hair out trying to work out where it's going wrong.
00:15:16
Speaker
Yeah, excellent. Okay, well, thanks ever so much for your time this afternoon, Alan. It's been great having you on the show. Is there anything else you'd like to add? You know, the other thing I'd like to say about the way things are going is it's less siloed than we used to have. It all used to think you would have these with dearest actuaries, you would sit separate and then you'd have the underwriters, you would be separate somewhere else. And they all did, it was never really any sort of fussing over. Whereas now you have,
00:15:44
Speaker
so killing scientists and data engineers, and certainly in a smaller company where you need to do a bit of everything, it's great so that you're not just focusing on part of the chain. It helps when you get a wider picture of what's going on and why, and it also helps when the model comes out with something that, to an underwriter, would look at it and just say, that can't be right.
00:16:04
Speaker
that kind of rounded picture to be able to see where it's gone wrong. You may not know why, but at least starting point that can't be the right answer. I don't know why it's not, but I know it's wrong. And then certainly should help improve pricing going forward and certainly the development that we've seen more change towards using data, personal lines in good at itself to many.
00:16:26
Speaker
That commercial lines is far more focused now on their data. And no longer a case of, we need that specialist underwriter who's been doing it for 20 years. You can't possibly do anything with data. There is a shift now to utilizing the data alongside the skill of that underwriter who can then take that and apply it and say, I see what that means to be a particular risk. Lending that no regular skill set into a unified solution.
00:16:55
Speaker
Yeah, absolutely. It's interesting the breakdown of those silos and the multi-skilled that are coming in under the same banner where we've got the data engineers, the scientists, deployment specialists, and so on all together. And subject matter experts, absolutely. People that actually know about the things we're ensuring and can help us with setting the prices. Or indeed, people that are good at pricing and know about what we're ensuring as well.
00:17:23
Speaker
Brilliant. All right. Thanks a lot, Alan. Excellent. You have a good rest of your day. Thank you.