The Exchanges

Q is the Internet of Things, um, and it seems that insurance companies are the, very closely related in terms of, um, actually doing something with all the data exhaust that's coming from all those, uh, connected devices. So, maybe, maybe any additional details on telematics? Is that, is that, is that some specific hardware that you guys install in cars? Is that a mobile app? Do you work with the

A Exactly. Well, it's a partnership. And so, uh, currently we're using, uh, specific devices that we'll go ahead and attach into a car's, um, uh, maintenance port. And so we can go ahead and we can milk information from the car, speed, geolocation, all that information. And that, again, it helps us to look at patterns as to where people are driving and when they're driving in those areas. Is it a congested area? Are there a lot of stop signs or, or, um, are there a lot of children in those areas? And so all that information, It helps us to really understand from a pricing standpoint, ah, and the risk standpoint to really make sure that our customers are having a, a good experience and, and getting the best, um, benefit from, from our products.

AI assessment note: “currently we're using, uh, specific devices that we'll go ahead and attach into a car's”

Q Great. Um, you alluded to this a little bit, but tell us a little bit about the, I guess the data, The tech stack, the data infrastructure, what, what do you guys use? Is it, and particularly in the context of a gigantic company like AXA, does, does US have a different tech stack than the rest of the world? How does that work?

A Well, because of a lot of the regulatory environment, one of our challenges is that data can't flow freely from continent to continent, ok? Ah, and so one of the things that we've done is we've essentially built three main, ah, data Analysis hubs around the world. So we've got one in, uh, the, uh, France, uh, in, in the Paris location. We've got one here in, uh, in U.S. to support all the Americas, uh, and then we're, uh, in the process right now of building one in Singapore as well. Um, so the one that we've got here in the U.S., it's, uh, it's primarily a Cloudera Hadoop stack, uh, that we've, uh, uh, used a blueprint that was essentially blessed by our, our brethren over in French, in France, Um, and with that, we've got R and Python and Spark. Uh, we use some Dataiku along the way, and so all these products kind of come together to give us a, a capability for not only our actuaries, but also our data scientists to, to access the data that they need.

AI assessment note: “it's primarily a Cloudera Hadoop stack... R and Python and Spark”

Q Let's, let's talk about, um, this part, because, so interest companies are the quintessential data-driven companies. The very core, I mean, actual science, science is fundamentally about math and statistics. Uh, as, as, uh, Has this permeated the rest of the organization, and, and do you see actuaries evolving in how they do their work?

A So, so what we found is that, quite frankly, we had some data scientists essentially within our midst, and so the actuaries that we had within our organizations, especially some of the younger ones, it was a very nice conversion for them to be able to pick up R coding, Python coding, and really start to understand And how to utilize some of these tools a little bit differently around these large data sets that they were very comfortable with to begin with. And so utilizing their background in probability and statistical and math, statistical analysis and math, it's really been a great opportunity for us to grow, uh, this actuarial science space into the, the big data science space, and really the two are coming together nicely for us.

AI assessment note: “a very nice conversion for them to be able to pick up R coding, Python coding”

Q Great. Alright, so maybe tell us a little bit, uh, maybe some examples of, of what What, uh, uh, you do in terms of using data and big data in terms of business objectives? I think I, I read some stuff around customer churn and some, some examples, I guess.

A So we're always looking for opportunities to enhance the customer experience. Uh, so one of the things that we've been doing, uh, in, in France and, uh, in Europe specifically is that they've been looking at, uh, auto, uh, claim fraud and specifically looking at it from a The main point of the repair shops that are doing the repairs, ah, and so by doing an analysis around that, they're able to essentially find where are the areas that are good, bad, and indifferent, ah, and really help our customers to make sure that they're going to the right, ah, claim shops and getting really good service. Ah, we also have a, a big, ah, initiative in Europe as well with telematics right now. So in the property and casualty space, specifically in auto, we get a much better, ah, capability to get better pricing based on how How people are using their automobiles and, and their daily driving habits. Here in the U.S. we've really been focusing around, ah, our life insurance products, and specifically in predictive underwriting. We're really trying to make sure that our underwriting capability has a level of consistency associated with it. So you can imagine you're relying on people who have been gone through, who have gone through this underwriting, training all their, ah, all their, ah, And now all of a sudden they've got to make a determination on whether someone's a good risk or not, and try …

AI assessment note: “specifically in auto, we get a much better, ah, capability to get better pricing”

Q And, um, uh, how did, how did that relationship start? Like, did, did people come to you, and you listened to what they had to say, and they happened to meet your need, and then you did a pilot with them? What, what should a startup expect when, when starting a relationship with you?

A So I'll tell you, you know, I'll be the first to say we're a young organization, right? So we don't have a lot of experienced data scientists on our We have, ah, a lot of, we have a good team, but our team, ah, on the maturity scale is still growing, and so when we, ah, have the opportunity to have someone come in and show us a new way to do something, or they've got a, a new product or some, ah, opportunity, ah, to partner with us, ah, we'll definitely take the time to, to have a look and see, ah, you know, is there an opportunity to use this? What we found in most cases is that we have an opportunity We have an opportunity to talk to somebody, and as we're talking to them, we start thinking of all these use cases of where it is we could essentially apply them, and that's where our, our, our product, our, our partnerships have really grown.

AI assessment note: “as we're talking to them, we start thinking of all these use cases”

Q And I think you were doing some interesting work, uh, at the call centers as well. Did you have an analytic strategy around call centers? I read that somewhere.

A So we're always trying to, again, provide the best service for our customers, and so in the call centers, we've been looking at opportunities to come up with a next best, um, product for them to make sure that they are, they've got good coverage, uh, and, and again, the intent for us here in the US, we're very much focused on just the life and wealth management So we don't, we don't handle the property and casualty space here in the U.S. yet. Um, but, uh, in this, um, life and wealth management, we're always looking to make sure that people have their money, uh, essentially invested in the right areas, whether it's an accumulation product, whether it's a protection product. Again, we're really looking out for our customers and making sure that they've got the, the right balance of, uh, of, uh, products to protect them.

AI assessment note: “in the call centers, we've been looking at opportunities to come up with a next best”