Dr Emma Briant, Senior Lecturer at University of Essex Audio files & transcripts • Andy Wigmore - Data and Al in insurance (1:20) • Andy Wigmore - Work with Mississippi (0:47) • Andy Wigmore - Using actuaries (1:54) Data and Al in insurance Wigmore: Now, you talk about data. So, you have a lot of data when you're an insurer and that data is— there's layers and layers and layers. You know, you have, urn, ah, lifestyle data— of course you do. You have, urn, credit check data— of course you do. It's all that data. If you put that together, the way you can actually then make risk against an individual is incredibly strong. So our ratios— in other words, you always say, "Right, if we take on this customer, what are the chances of them making a claim within a twenty-four year— uh, a twelve-month period or twenty-four months? It is high, because they're so low social demographic grouping, they don’t have a proper job, they have to take out a loan to pay for their car insurance." You can make a risk assessment of that, so it makes it more expensive. But imagine if you had the ability to say, "This person is absolutely brilliant. We know, as an insurer, it's worth giving them a good price because we know they're unlikely to make a claim, they're solid, they've got a good job, you know, they've got a family life, blah, blah, blah, blah, blah." So that, in artificial intelligence terms, is the holy grail in insurance. So that was a by-product of what we discovered, brilliantly. And that's all about data. That is all about data. So, urn, that was— that was the upshot. So, we've set that up in Mississippi. It's been going for nine months. We've been testing, for twelve months now, I think, testing all the insurance against it and it's extraordinary. Work with Mississippi Wigmore (cont.): ...the narratives. And we started creating the news stories. Briant: And you start to learn what works and what doesn't. Wigmore: Yeah, you do. You completely learn— it's self-learning. And our actions— Briant: And you are building, essentially, a psychological model of how people function. Wigmore: So, what was the upshot? Let's say the referendum's just finished. What we discovered — we were actually quite bloody good at artificial intelligence. And we've applied what we learned in the referendum to our business model for insurance. So, for risk and, urn, making sure you get the right person who's not gonna be fraudulent, not gonna make claims they— you know, you know so much about them. So, we've started an operation in Ole Miss University in Mississippi, which is the centre of artificial intelligence in the world, who knew? Briant: Is it? Okay, I didn't know that. Wigmore: And, and, and the guy that runs it, he's like the most extraordinary data scientist. Using Actuaries Wigmore: Some of the things they did tell us, which were, which we did copy— there's no question about that— is about, you know, these small clusters. They say, "You need to find out where these people are and what matters to them." And what we were able to deduce from that— and remember, urn, ah, as an insurance company, you have actuaries that work for you. Actuaries are brilliant, they're mathematicians. So if you give them a problem and you say, "Right we want to— here's, here's some stuff. What do you think? Give us probabilities." They came up with the probabilities of the areas that were most concerned about the EU. And we got that from our own actuaries. We had— we had four actuaries, which we said, "Right, tell us what this looks like from our data," and they're the ones that pinpointed the twelve areas in the United Kingdom that we needed to send Nigel Farage to. So there it was, it's like a slug. Briant: And that's his tour. Wigmore: That's his tour. You go and those are the only people you need to concern yourself. Why? Because this solid base all over the other parts of the country, you kind of knew a lot about and, actually, you were kind of confident— Briant: That’s the groundwork stuff. What about the social media side of it? Because you did do that stuff. Wigmore: Oh yeah, we did, absolutely, yeah. Briant: Obviously, you must have had some data marketing people, I mean, and also— Wigmore: Our own. They're our own. Remember, our marketing people from the insurance company. Briant: So who did you use? Can you tell me who they were? Wigmore: Yes, us. In our offices. They're still working there. Briant: You? Wigmore: Yes. I— they're all still working there. All that's still in Bristol. We had a call centre, which did the outbound and inbound telephone calls. We had two marketing guys, who do all our marketing for our insurance companies, which is, you know, comparison websites, Arron's— help develop one of those— the GoCompare websites and things like that. You know how those algorithms work. When you're trying to get the cheapest car insurance and you're trying to judge risk, which is what actuaries do, you're doing the same type of thing: you're judging risk, you're judging people, based on what information they give you.