DMTI SPATIAL ROUND TABLE DISCUSSION

Jonathan Scott, Doug Everett, Mary Trussell, Ben Kosic, Dennis Nilsson, Mukul Ahuja, Craig Harris, Dale Avis PHOTOS BY JOHNNY LAM PHOTOGRAPHY

June 24, 2015

LOCATION INTELLIGENCE AND INSURANCE A ROUNDTABLE DISCUSSION

It’s estimated that 70-80% of all information has a geographic component.

Nowhere is this figure more relevant than in property and casualty insurance. Insurance companies, brokers and adjusters have used location as a factor in virtually every facet of the industry’s interaction with customers.

However, new tools such as geo-coding, digital mapping, data analytics and visual dashboards, hold the promise of transforming how insurers use location throughout the policy lifecycle. It’s a powerful combination called location intelligence. We gathered several industry thought leaders together to share their insights into this emerging area of p&c insurance.

PANEL DISCUSSION

CRAIG HARRIS: How does location intelli-

gence fit into the the property and casualty insurance industry?

DENNIS NILSSON: The majority of data in insurance has a spatial component, so location intelligence becomes relevant in a number of areas. First, it helps with understanding risk.

So think about the perils we’re exposed to – whether earthquake, flooding or windstorms – the exact location of (properties insured) becomes really key. Second, combining the geographic coordinates, or geo-coding, with digital mapping enables us to have a more ho-

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listic view of our locations insured. And that has a beneficial impact on understanding risk accumulation.

MUKUL AHUJA: (Location intelligence) is

definitely a nascent, emergent and growing space in the broader data analytics story. We did a recent survey at Deloitte with insurance industry leaders around the extent they use analytics related to geo-coding penetration.

From a personal lines perspective, 10% of the players in Canada said they’re using it, but 35% indicated they’re really thinking about it.

And in commercial insurance, 25% are using it. While this is a small sample, we have seen

PANEL

CRAIG HARRIS President, Edit Insight Ltd. (Moderator) MUKUL AHUJA Senior Manager, Deloitte Canada DALE AVIS Chief Information Officer, Crawford & Company (Canada) Inc. DOUG EVERETT President, CHES Special Risks BEN KOSIC President, CANATICS (Canadian National Insurance Crime Services) DENNIS NILSSON Vice President, Leader of Center of Excellence, Pricing and Underwriting, RSA Canada JONATHAN SCOTT Director, Sales and Client Services, Intact Financial MARY TRUSSELL Partner, KPMG Canada

a few advanced players starting to implement use of geospatial and data analytics.

CRAIG HARRIS: So we’re still at the stage of experimentation?

MARY TRUSSELL: I think that is absolutely

right. The use of location for static risks is clearly as old as time. However, we are now at a juncture where people actually have the tools for not only location, but timing – that means you now have real intelligence. So you have the data, but you can actually take actions around it.

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CRAIG HARRIS

MUKUL AHUJA

DOUG EVERETT: It’s incredible to see the tech-

nology around location intelligence. The information has always been there; what’s different is how it can be gathered and how it can be used. I think it’s important that we see this as a tool and not necessarily driving the decisions. The people with experience who’ve seen these risks can use it to accommodate and make selections that are appropriate for exposure.

JONATHAN SCOTT: I think it brings a new

level of data and of information to the fingertips of underwriters in a much quicker and real-time way. This will improve their decision-making at a much quicker pace than in the past.

BEN KOSIC: Location intelligence has been around for some time in terms of the static, basic stuff. Now it’s a question of how do we jump on using it in a more real-time, transactional basis. So, clearly, location is important, whether it’s where the initial policies were taken out, where the claims have occurred or the pattern of behaviour across time.

Everybody is on this journey – the issue is just how quickly they’re going to leverage the data to its fullest.

DALE AVIS: It’s still quite early in the evolution. And it’s really within the last five years that we have seen the benefits. But the

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To me, it’s more than just a dashboarding and visualizing.

It’s really about how you draw business or analytical insight from real-time information or location-based information.

• MUKUL AHUJA

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DALE AVIS

early returns that we’ve seen so far tend to be around the visualization and turning that data into information that’s useable. It’s like the difference between looking at a spreadsheet and looking at a visual map that you can instantly grasp what’s going on.

CRAIG HARRIS: How do you define location

intelligence?

MUKUL AHUJA: To me, it’s more than just a dashboarding and visualizing. It’s really about how you draw business or analytical insight from real-time information or location-based information. What’s driving the value from a premium perspective? What’s driving the risk control in terms of concentration or aggregation? And what’s driving the operational efficiency? If you can drive insights in those three areas, that, to me, is the definition of location analytics and intelligence.

BEN KOSIC: Good analytics has always been about developing actionable insight. So it’s a question of figuring out how to use that location information in a way that drives whatever decision making you’re trying to influence, whether it’s pricing or fraud management. So I think it’s an emerging set of techniques to do that.

DENNIS NILSSON: I would say that location intelligence is about using the spatial component – both location, but also time – to do detailed analysis. So for the first aspect, location, today we know exactly where the (insured property) locations are in terms of geo-coding. And the second aspect, the analytics, is around taking that information, doing sophisticated models around it, transforming it into insight and eventually making better business decisions.

DOUG EVERETT

CRAIG HARRIS: What were some of the weaknesses or limitations of past approaches?

DALE AVIS: In the past, insurers generally used postal codes. but there are some big differences between codes. If you take downtown Toronto, you can have one building that has four or five postal codes. You go into a rural area and you have a whole town that’s one postal code. So getting down to any degree of accuracy around that is a huge challenge as a primary tool.

JONATHAN SCOTT: Using postal codes or FSA (Forward Sortation Area) territories, you almost have to treat the entire territory as if it’s all the same. There’s no other data to kind of differentiate different points within that FSA. So by being able to get rid of the traditional postal address and replace it with latitude, longitude, elevations and so on, you can now make different decisions based on the specific property. And you can make a more precise decision about whatever it is that you’re looking at.

DOUG EVERETT: Many of the underwriters

that we deal with look (at risks) based on postal code. So in our rating formula, our accumulation formula, it says: “a certain amount of money by postal code.” However, postal codes were developed for sorting and delivery of mail; they really aren’t based on what we’re actually looking at in terms of specific location.

DENNIS NILSSON: Events happen on a more granular level than postal code. So think about flooding or earthquake; these don’t stop at the postal code boundary. So location intelligence gives us the possibility to examine this on a much more granular and detailed level than ever before.

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BEN KOSIC

CRAIG HARRIS: Is it a challenge to get that

more granular or precise measurement, geographically?

JONATHAN SCOTT: The integration with legacy systems is a challenge that I think all insurance carriers face. On a larger scale, as you start to introduce these tools on a national basis for the larger carriers, you have to ensure that there’s a good and consistent level of accuracy in the data. It can’t be a small pocket here or there. So I think one of the big challenges is to find one good set of data that you can use nationally.

BEN KOSIC: I agree. It’s not a tool and technology issue; it’s a data quality consistency and completeness issue. Can you source that data and then start to use it in creative ways? And I think getting the frontline collection to happen, or acquiring and integrating third-party data sets to supplement and get that level of granularity, I think those are the challenges that are being looked at right now.

DALE AVIS: The data itself tends to remain fragmented and it’s not easily shared. There are two layers: the data internal to the company and then it even gets worse when you start talking external sources, such as social media and anything like that. And you have to find that right correlation. What are you going to marry that data together based on? Because you can get totally wrong decisions if you don’t relate the data properly.

CRAIG HARRIS: What specific areas of insurance can benefit, such as underwriting?

JONATHAN SCOTT: It makes the determination of concentration of values much easier to do, more dynamic and in real-time. So you’ve got, location intelligence on all of your

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DENNIS NILSSON

JONATHAN SCOTT

current clients and now you can quickly see, “Well, let me look at a 50-foot radius from a specific end focus. What does 300 look like?

Where are the fire hydrant or fire halls? Is there a river flowing through this?” And now you can bring all this together specifically by having geo-coded information.

DENNIS NILSSON: In terms of risk under-

standing, the key is that we get the precise location. Think about flood risk. Today we know that flood risk not only happens on a postal code level, but you need to be more specific in order to have a precise assessment of the risk. So, the combination of geo-coding and digital mapping enables you to have this holistic view of all your risk.

CRAIG HARRIS: Will it improve the combined ratio or drive increased profitability?

JONATHAN SCOTT: It’s difficult to say with accuracy how much of an impact it has. But take the Alberta flood event as an example.

If you could introduce this kind of improved technology and recreate the flood, then you would know whether your model worked.

While floods will never happen in the same place and the same magnitude, you’re probably in a better position to identify blind spots now and take action on those.

MUKUL AHUJA: From a portfolio manage-

ment perspective, it really helps if you think about: are we selling as much of a particular type of product as we should in a particular area? If you’re in an earthquake zone, do we have the right penetration in that market?

From a sales and marketing perspective, is there an approach from location intelligence that can help inform some of those decisions?

DENNIS NILSSON: This insight and all these

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MARY TRUSSELL

So in our rating formula, our accumulation formula, it says: “a certain amount of money by postal code.” However, postal codes were developed for sorting and delivery of mail; they really aren’t based on what we’re actually looking at in terms of specific location.

• DOUG EVERETT

capabilities will definitely be to the benefit of our customers because we will have better risk understanding. And in the event of a claim happening, we can be proactive in our dialogue with our customers.

MARY TRUSSELL: The other thing we haven’t

touched is the role in loss prevention and risk management. And I’m actually very interested in the extent to which you’re seeing some insurance buyers using this sort of location-based data. And they’re able to buy in a more sophisticated manner. I was thinking particularly about some of the marine and trucking risks.

CRAIG HARRIS: What can location intelligence do in terms of claims management?

DALE AVIS: I think the early wins were around the “workforce management” component. So whether it’s a catastrophe or even just a normal situation, we have been able to assign the claims to the right adjusters where the claims are coming in. We did some early work a couple of years ago where we took the predictive path for a hurricane and mapped that against policies. And then we were able to determine which cities we need to concentrate the response for adjusters. In catastrophic events, it’s really critical because

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We did some early work a couple of years ago where we took the predictive path for a hurricane and mapped that against policies. And then we were able to determine which cities we need to concentrate the response for adjusters.

• DALE AVIS

you’re in that rapid response mode so you need the information visually right in front of you all the time.

MUKUL AHUJA: We recently did a real-time demo for a client in the U.S. on a hurricane path and the ability to pull up real-time news events and social media feeds. It basically was saying: “What’s happening in that state or in that city or in that jurisdiction in real-time?

What’s the news reporting, what’s social media tweeting?” So you can have the latest information on the impact. That’s where location intelligence can be really powerful.

MARY TRUSSELL: Yes, there’s a really nice hail

app in Slovenia of all places. So that whole part of southern Europe is very hail exposed.

And even for your personal auto insurance, you’ve got an app in your car, on your phone.

I think this could be relevant to Alberta, for example.

DALE AVIS: We’re just starting to get into things like predictive analytics around reserving. So there’s going to be additional uses beyond just “where is the claim and what’s the severity of the claim?”

CRAIG HARRIS: What about the application of location intelligence to fraud detection?

BEN KOSIC: I think there’s a whole number of

applications, whether at the policy or claims

stage. On the policy side, there is address misrepresentation type stuff that goes on where the public is using location-based information knowing that premiums are really high in a certain area. On the claim side, we are looking for anomalous or statistically unusual behaviour. Is there a prevalence of a certain set of circumstances occurring in a particular area? So doing any kind of analysis like that is hugely beneficial as a supplementary exercise to normal fraud investigative techniques, especially for premeditated or organized crime.

CRAIG HARRIS: Is it about data or design in fraud management?

BEN KOSIC: It starts with the data capture.

So where are you getting the data? But it’s all sorts of policy and procedure decisions around what are the new rules for using that data to make decisions. You may get an insight that suggests something. So, on an individual claim you can have sophisticated investigators using that insight in different ways. But I think if you’re looking at the (greatest) impact, you have to embed that throughout the organization. So it’s also a design issue.

JONATHAN SCOTT: There’s also a learning curve in terms of changing the way that you’re looking at this new data and how you’re going to make decisions going forward.

If all of your historical analysis and research and all of your current internal processes are based on FSA territories, now you introduce this new way of thinking about the information. To a certain extent, you almost have to redo all your historical in this new model so that it can make sense and you can make some better decisions.

MUKUL AHUJA: When you think about high volume transactions and you’ve got a whole number of policies to look at on a daily basis

and adjudicate and underwrite, how do you make it easy, simple, intuitive and real-time in order to enable that and really scale it? That’s the challenge at hand because we’re still a ways away from making that happen.

MARY TRUSSELL: That really does require

leadership from the top and cultural change.

So we’ve been doing quite a lot of research around innovation and insurers. And around half the insurers that we’ve been surveying will tell you that their business model has already been disrupted by technology. But are they doing anything about it

DOUG EVERETT: From my perspective, it is

about growing into that technology. And I’ve seen it being used very effectively. But this sounds like a vast amount of stuff that could be misused if it wasn’t properly put together.

It’s so much easier if people are trained to use this (location-based data) as a tool to combine with other experience.

CRAIG HARRIS: What does the future of location intelligence look like?

MUKUL AHUJA: I think it’s about figuring out how to inform and impact the specific individual’s interaction and engagement with the organization. And how do you get to this local-based pricing or high-value customer targeting or segmentation. It’s how to become more effective at that specific individual engagement.

BEN KOSIC: I’m hearing people talk about the movement towards highly personalized product. So very, very micro rather than bulk segmentation or large segment product development. So I think this is where all sorts of data have got to come in, in order to price the risk appropriately.

JONATHAN SCOTT: The common language here is latitude, longitude. It’s GPS location, right? And if you can translate whatever set of data you have into latitude and longitude, then you can have anything talk to each other. I think that opens the door to just about anything.

DENNIS NILSSON: These are just the early stages of location intelligence. I wonder where this conversation will be in five years time when we discuss underwriting, claims and fraud?

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