Driving Data Home

October 2015    |    By Craig Harris

Sometimes seen as the “poor cousin” to car insurance in terms of resources and investment, personal property is now a hive of activity in data analytics. Insurance companies are using internal and external sources of information to delve into more specific by-peril rating, loss cost development and operational efficiencies in areas like claims management. It all adds up to what one insurer calls an “arms race” to tap data for business advantage in homeowners insurance. 

With the recent buzz generated around data, the migration from telematics in auto insurance to advanced analytics in homeowner risk is not surprising; perhaps it’s even inevitable. Several factors have come together to push the quest for information on homeowners insurance to a different level.

“The successes in other lines of business and applications have created capabilities and awareness of the power of predictive analytics,” notes Greg McCutcheon, president, Opta Information Intelligence. “Many started with personal lines auto, claims triage and other related supply transaction costs. . . Personal lines property is a natural evolution.”

Given that homeowners insurance accounts for roughly 20 per cent of the overall p&c insurance premium volume pie, it stands as a significant line of business. With losses mounting from severe weather, water damage and other claims, there is heightened pressure on the personal property product in terms of rating, underwriting and loss reduction.

“If you look at the results of Canadian insurers over the past couple of years, you can really see the impact of weather-related claims,” according to Mary Trussell, partner with KPMG Canada and a member of the firm’s global insurance leadership team.

“The shape of risk is changing. Insured values are rising. In what might initially seem like a row of uniform houses built in the 1970s, many have likely been redeveloped and renovated,” she observes.

“One piece driving this is that personal lines property has struggled in terms of profitability, particularly in Western Canada,” says Simon Mellor, assistant vice president of pricing and reinsurance for SGI Canada. “Insurance companies are placing an increased focus on these products.”

Availability of Data

It is not just troubling loss ratios, but also the availability of data that has helped to spur change in homeowners insurance. Moving beyond internal transactional data to outside sources of information has proved highly valuable in drilling down into individual risks and offering even more thinly sliced segmentation.

“There is more and more external data available from all kinds of different sources,” notes Shelley Toyota, vice president of personal insurance for RSA Canada. “Whether you are talking about flood models, postal code data – there is more access to different kinds of data than ever before. And then the dilemma becomes when you are dealing with masses of data is, do you have the technology to manage it?” she asks.

In addition to data, insurers today have access to more sophisticated analytic tools and greater computing power, according to Chris Van Kooten, senior vice-president and chief underwriting officer for Economical Insurance.

“We are finding ways to do more things with larger data sets,” he comments. “New solutions coming out from a technology perspective put the old way of managing data behind us. Previously, you had to take all your data and structure it into a format that you can easily pull from; the new solutions allow you to just put blobs of data onto systems, not structure it.”

“Over the last few years, it has become somewhat of an arms race of being able to understand your data better than your competitors.” Van Kooten adds.

“I think we are at a tipping point,” says Keith Walter, senior advisor with Deloitte Canada, who specializes in analytics and actuarial work. “The vast majority of significant p&c insurance players have either implemented or are in development with data analytics for homeowners insurance within the next 12 months. By the end of 2015, it will be a requirement for players in this space.”

“Those that get left behind can be severely disadvantaged,” Walter points out.

In a survey of 99 insurance homeowner representatives in the U.S. and Canada released in November 2014, Verisk Analytics and Earnix, found that 57 per cent of respondents now use predictive modeling for homeowner loss cost development. Eight two per cent use a by-peril rating structure, which focuses on gathering data on individual perils to determine an accurate price for each level of risk.

Worrisome Loss Ratio

One of the prime reasons for increased usage of data analytics is to curb worrisome trends in the loss ratio, which jumped from 58 per cent in 2012 to 74 per cent in 2013 for personal property lines in Canada.

A study of U.S. insurers in 2012 showed that those with by-peril rating plans had loss ratios 7.4 per cent lower than companies using traditional rating systems, according to Douglas Wing, assistant vice president of analytic products at ISO, a source of information about property/casualty insurance risk now part of Verisk Analytics.

“The prime benefits (of data analytics) are improved loss ratios with lower loss costs, better rate for risk or pricing, optimized loss control and better understanding of the risks your portfolio faces,” notes McCutcheon, whose company works closely with Canadian insurers on predictive modeling, data analytics, property data and peril scoring.

Advanced data analytics for homeowners insurance can entail a number of head-scratching terms, such as univariate analysis, sampling, regression/general linear modeling, splines and spatial smoothing. However, at its heart, it involves finding useful data, or patterns of data, that inform business decisions in rating, underwriting and claims. By-peril rating is one of the first tangible outcomes of that process.

“The whole point of analytics is to try to understand the information that your company has at a deeper level, whether that means doing it by peril or through different ways of grouping the data,” Mellor says. “What you are trying to do is extract new and actionable information with the data, and breaking it out by peril is one way of doing that.”

SGI Canada moved to an “individualized rating environment” for personal lines insurance in January 2014.

Toyota explains that the peril of fire has been eclipsed by other risks, including inside water damage, outside water damage, ground up water damage, hail, hurricanes and earthquake.

“All of these perils are emerging. And it is forcing us to ask: what other data is available? How do we couple external data with our existing data? Do we have the technology to actually pull out the insights?” Toyota says.

By-Peril Rating

“By-peril rating is something that makes a lot of sense from a pure pricing perspective, but it also helps in giving consumers some options,” notes Van Kooten. “For example, if they live in an area where they get sewer back-up every year, maybe they can opt out of sewer back-up coverage because it is so expensive.”

“By giving them more information about it, they can start to manage some of that risk themselves and take action to reduce their exposures,” Van Kooten points out.

“Insurers are able to improve pricing because they have a better understanding of by-peril exposure, both likelihood and severity of specific losses,” McCutcheon observes. “Knowing that risk can never be completely eliminated, applying loss control or targeted underwriting action to specifically identified properties improves the return on investment for such initiatives.”

Greater accuracy in pricing is not the only potential benefit of data analytics in personal property insurance. Operational efficiency in areas such as claims management may emerge as a significant breakthrough for insurance companies, according to Walter.

“Insurers are trying to find ways to intervene in and improve the claims handling process for better overall outcomes,” Walter says. “We see things like the risk of exaggerated claims and the opportunity to do a better job working with the client to reduce claims costs – these areas are very ripe for improved data analytics.”

Data analytics can also lead to more innovative products in homeowners insurance, Mellor observes.

Innovative Insurance Products

“Looking at data in new ways I think ultimately contributes to the evolution of the product and new offerings,” he notes. “This creates more choice for customers, which could include loss prevention.”

“Once companies start to manage their own data effectively, you start to look for external data sources that might supplement the information you have,” Van Kooten says. “There are a lot of things you can do with that. It can go into your pricing, it can go into product design, customer experience and providing additional tools so that customers and brokers can understand what is happening with their risk.”

Innovation may find a more likely breeding ground in personal property lines, as opposed to the more rigid side of auto insurance.

“The interesting thing about homeowners insurance is the opportunity to introduce new approaches,” Walter says. “Auto insurance tends to have more regulatory control. You have more flexibility in personal property. And insurance companies are rolling out new methods and new technology.”

While insurance companies talk about enhanced transparency of pricing through data analytics and by-peril rating, this could be a difficult sell to consumers who see a spike in premiums due to repeated claims activity. Clearly, there will be winners and losers in the personal property data game. One of the main challenges will be how to communicate those changes to customers.

 “We have to translate the science into a language that customers and brokers understand,” Toyota says. “Sometimes, we can get caught up in our own sophistication. At the end of the day, we need to spend more time asking – what does this mean to brokers in terms of their ability to better serve the customer?”

“Instead of saying, ‘here is sophisticated black box underwriting,’ we need to boil it down to: ‘this is the outcome, here is how you can explain it to the customer to help manage the risk and premium,’“ Toyota adds.

Customer Communication

 “I really think it is going to be about how companies can find a way to use (data analytics) to enhance the customer experience, rather than just pad company profits,” Van Kooten says. “There is a lot of work to be done by the insurance industry on making insurance products and the entire experience more user friendly.”

“Having good data and generating high-quality analysis doesn’t provide value unless the company changes the way it does business. It has to be in the interests of all stakeholders, and most critically the customers,” Mellor comments.

There are several obstacles that line the path of data analytics and meaningful business results for homeowner insurers, according to sources. Broad industry challenges include the availability of analytic resources, both human and technology, access to verifiable data and the ability to modify old rating structures and underwriting processes to reflect new analytic approaches.

“There needs to be a steady state of investment, but also an underlying process to make it work,” Toyota says. ”There are different types of resources that go beyond pure actuarial work. The external challenge is making sure that our broker partners and customers come on the journey with us. The internal challenge is making sure the benefits that we are deriving out of data analytics can pay for the investments, so we don’t have to pass it on to consumers.”

Trussell notes that a global KPMG study called “Transforming Insurance” identified integrating data analytics into existing systems as one of the top challenges facing p&c insurers.

“It is really about turning theory into practice,” Trussell says. “It is becoming much easier to migrate data and because of that we are seeing insurers step up to that challenge. So instead of having fragmented legacy systems, they are investing in unified platforms to give them a much better grounding to analyze data.”

Turning Theory into Practice

Van Kooten says Economical Insurance is in the process of changing its legacy policy administration system to allow for easier integration of data analytics. “Going to a space where the industry is really interested in quality of data and collecting more data, most insurance companies are finding that their legacy systems are not getting the job done,” he notes. “I think that will be a game changer as well.”

Access to reliable, clean data is a necessary precondition for sound analytics, but some say inaccurate information still plagues insurers on personal property files.

“Believe or not, address quality and related data has been a specific challenge for some carriers,” McCutcheon notes. “Deciphering an address to match it with other data sources and to be able to pinpoint its exact roof top location can be a challenge when dealing with years of renewal business or legacy data.”

“Postal codes and municipalities have been scrubbed and standardized in some cases, but rural addresses, streets and valid unit numbers still pose problems for carriers,” McCutcheon adds.

Another potential pitfall is whether new data analytic approaches will be embraced by insurance companies rooted in traditional rating structures.

“One other challenge remains the adoption and acceptance in trusting the output provided by predictive models and solutions,” McCutcheon says. “Old ways of pricing risks rely heavily on the insured, or the broker or the agent to address many subjective questions about the home, such as the quality of finishing throughout. “

Insurers that move to data-based predictive modeling remove the ‘art’ aspect of rating and underwriting to a more scientific approach. “Now, predictive analytics can remove these subjective questions and instead draw from a greater pool of structured data,” McCutcheon adds.

The need for change in how data is analyzed and used in the business requires leadership from the top of the insurance company, according to Walter.

Executive Sponsorship

“One of the most important issues is executive sponsorship,” Walter says. “How does this fit within the organization? Who is the sponsor? Who is driving it forward? It is a cultural change issue and it does require executive leadership.”

Walter also cites Deloitte research into how companies in all sectors of the economy integrate data analytics into practical business decision-making. “A key part of this is that winners are those who are able to achieve bite-sized success,” he notes. “It is not a ‘one-hit’ or ‘do it all at once’ approach. It is about ongoing improvements to the business that are most likely to be sustainable.”

Mellor echoes these comments in his description of SGI Canada’s approach to data analytics in personal property insurance. ”This is an ongoing exercise; it is not something that you launch and you get perfectly right the first time. There is incremental learning as you continue to implement analytics,” Mellor notes.

The evolution of data analytics in homeowners insurance may hinge on the ability of insurance companies to seamlessly integrate new techniques into their business pricing and underwriting processes –while also keeping customer needs at the forefront.

Toyota issues a word of caution on what she calls the potential dangers of “micro” segmentation and a “hyper-analytical” approach.

“We have to make sure we have the customer needs and preferences in mind,” she says. “You can keep segmenting and segmenting to a micro level. At some point, you have to step back and make sure the customers’ needs and wants have a dimension in what could be a hyper-analytical environment.”

“We are trying to find that optimal balance between data analytics and customer needs,” Toyota points out.

“I think it is about being agile in terms of being able to integrate data analytics into the business,” Trussell observes. “The interesting question is: will it be the larger players who succeed or can the smaller players be more nimble?”

Analytics now Table Stakes

“Insurance companies of all sizes now have higher quality data and more analysis and expertise,” Mellor notes. “So there has been a flattening of the playing field. Predictive analytics is no longer the domain of just the largest, most sophisticated companies; it has really become table stakes.”

“There are a number of first movers, from all tiers, who will drive implementations fiercely into their operations early on,” McCutcheon observes. ”Based on the interest we are getting, the increasing property claim costs, the natural evolution of existing strategies and the need to protect against anti-selection, a dozen or so more will adopt some type of analytics on property in the next two to three years,” he adds.

With early adopters on board, many sources say the competitive advantage will narrow as quick returns gradually diminish in value.

“Companies will gain advantages in the short-term through data analytics, but these will close relatively quickly as others adapt,” says Walter. “The focus tends to shift to finding new and valuable sources of data.”

These “new sources of data” could involve smart home applications and data from personal property monitoring devices.

“Some of this is in its infancy, so there is so much potential for data we could use, says Van Kooten. “You look at all the smart-home monitoring devices people have now, such as security systems with built-in water detectors and remotely controlled thermostats. Insurance companies would love to have access to that information,” he adds.

While insurers seek out new forms of data for competitive advantage, Walter contends that it is the expertise of data analytics itself that will separate successful insurers in personal property lines from the also-rans.

“The long-term winners are those who will see data analytics as a core capability of the organization,” Walter concludes.

“It is not a new product or new initiative. There are opportunities across revenue generation, claims cost management and operational efficiencies. The winners will be those that regularly invest in opportunities across that whole spectrum by building a core capability within their organization.”

Please note a version of this trends paper has previously been published in an industry publication.