Big data and analytics have become more embedded than ever in the insurance industry as insurers look for edges across the business. According to a recent survey from Strategy Meets Action, “Data and Analytics in Insurance: 2016 and Beyond,” 86% of P&C insurers in North America have a strategic initiative in place for enterprise data and analytics. And, benefits are starting to roll in from early projects in areas like customer segmentation, new business, and underwriting profitability. Now, insurers are moving on to applying analytics in more areas, like CRM, risk analysis, underwriting operations, fraud, and profitability analysis.

“The analytics capabilities that have been in place over the past several years are already bearing fruit, leading many insurers to expand into other business areas,” says Mark Breading, SMA partner and report author. “While there are many factors influencing results in areas such as underwriting profitability and customer segmentation, the analytics projects have contributed to the industry improvements in these areas.

The percentage of insurers’ budgets being devoted to analytics is also increasing, SMA found. Nearly 10% of insurance IT spending goes to data-related initiatives. And, those dollars are in some cases being matched by the several business areas that are looking to analytics to provide a boost. Insurers are creating data practices at the enterprise level to provide oversight and guidance to analytics initiatives.

“Insurers generally are becoming more sophisticated in the management of data and analytics, with more companies establishing enterprise-wide units, led by chief analytics or chief data officers, and staffed with data scientists,” Breading adds.

The formalization of analytics as a core practice area is allowing carriers to operationalize data more seamlessly. Following are several stories, from insurance company data leaders, about how insurers have taken data strategy from the outskirts to the center of their enterprises.

Sentry Insurance: Core Project Supports Data Excellence

Sentry Insurance, a multiline insurer based in Stevens Point, Wisc, represents a classic case of how insurers have granted analytics an increasingly larger profile. Sean Nimm, the company’s VP of analytics, came up through the ranks as an actuary — the business side. He works with Jim Frank, VP of IT for the carrier, to carry out the company’s overarching strategy around analytics technology and process.

“There are different skill sets and different capabilities that you need to have a successful analytics area: business knowledge, data management, and statistics, for example,” Nimm says. The challenge is trying to fill all three of those.”

The realization that Sentry’s data was increasingly valuable came about while the company was in the midst of a legacy system modernization, Frank says. As Sentry installed new administration systems, it realized it was able to leverage its internal data in new ways to get better at things like pricing.

“A lot of the buildout on the data side didn’t come from an analytics need -- we were undergoing a significant amount of core modernization and we needed to use data as the hub to blend the old and new together,” Frank says. “To support legacy system modernization, we built operational data stores [to facilitate data migration] — suddenly this data was exposed a lot more conveniently to use.”

In addition to core software from Guidewire and Accenture, Sentry is using Tableau to turn its newly gleaned insights into actionable information for end users like underwriters. “A lot of insurers are investing in data as an asset — they’re buying tools to put data in front of people. But it’s the infusion of the art into the science — who can begin to apply data more creatively — that will define success,” Frank says.

This year, the company has donated $4 million to the local campus of the University of Wisconsin to support data analytics education and get more of the artist types into the organization. “It’s not just about data scientists, it’s about building teams of employees and associates that understand what the data means,” Frank adds.

The biggest change that Sentry has noticed since formalizing analytics is the speed with which insights can change the business, Nimm says. The kinds of changes that once took weeks to respond to now can be addressed much faster.

“The world is changing so quickly, and we’ve had some opportunities to capitalize,” Nimm explains. “Business leaders are able to take historical data, create personas, and set pricing strategies based on that, and in near real time see how that improves or changes results.” Sentry’s analytics growth was facilitated by a core systems replacement project. But, Frank says, integrating analytics across the business doesn’t have the same kind of defined beginning and end that most IT projects do.

“One of the things I’ve realized along the way is that there’s no finish line with these data projects,” he says. “We build assets that have been out there for years, but Sean and his team push us on in adding to and clarifying what they need. The stabilization doesn’t have an endpoint.”

AXA: New Focus on Data Assets

Louis DiModugno is both the chief data and chief analytics officer for AXA in the U.S., a life insurance carrier. Early on, he says, his job involved a lot of evangelizing the potential for data and getting the business sides on board. Now, though, the time has come for action.

“For our first year, we were really focused around creating value with our analytics initiatives: ‘this is what we are, this is what we do,’” he explains. “Now what we’re trying to do is develop data assets that are going to be more focused on specific entities. We’re looking at data assets around customer interaction, claim and claim interaction, prospects and prospect interactions.”

AXA’s analytics organization comprises two major threads, DiModugno explains. On one side is the acquisition and management of raw data — a more technology-driven piece around compliance and security. That practice has evolved over time, he notes, as consumers and regulators have themselves become more mature about how much of their personal data is out there for companies to access.

“What was interesting is that in the past AXA has been the one that has been really adamant about people working with our data — we tell people we really need to understand their controls,” he says. “Now that we are dealing with some big data vendors, they are pushing the same requirements back on us that we had been putting on others. It’s showing the maturity of the industry.”

On the other side is the actual analytics run on the data. That team’s task has been focused on visualization and making it possible for underwriters and other concerned parties to get what the data says.

“We’ve moved away from spreadsheets — we’re using a visualization tool throughout the organization,” DiModugno says “Our next step is really helping the underwriters understand why the model come up with the score it did. People are a still little bit concerned when a machine tells them what to do.”

DiModugno says that the company isn’t quite ready to give up underwriting decisions to a model, but does see the speed facilitated by analytics being crucial going forward.

“Where we really focus is to take the data that we’re getting through our normal applications and speeding up our decision process,” he says.

CUNA Mutual: From a Different Perspective

CUNA Mutual, which provides a range of insurance and financial products to credit unions, recently hired Harsh Tiwari as its first chief data officer. Tiwari joined the company from Capital One bank and said that the difference between his new job and his old one comes from the roots.

“Capital One was founded on the principle of data. The two founders spun it out of the Bank of Virginia, taking the credit-card portfolio and said they could use database marketing to run the business,” he explains. “But insurance as an industry came up with an actuary practice that says, ‘I cannot precisely predict risk for any one individual, so I will collect a large group.’ Data means I can take the variations that appeal to smaller and smaller groups. It’s a different perspective.”

As CUNA has made the decision to bring analytics to the forefront, Tiwari is applying some of the lessons he learned in his previous position.

“The first step is actually building an infrastructure where you bring in meaningful data. Many data warehouse projects stop without doing anything. How do we do this in a smart way that continues to see the value? And once you see the bottom line dollars, what’s the next step after that?” he says. “I will work with IT in terms of building the infrastructure, and I will work with business to make sure the data is doing work for them.”

Tiwari says that when trying to get buy in from a variant group with established best practices, it’s important to facilitate their adaptation.

“How do you actually bring in a new capability, this thing called data and analytics, and embed them across the company so that you can shift the trajectory we are on?” he asks. “I am a big believer that data is something everyone can use. You need to hide the complexity a bit. I don’t need everyone to use SQL. How do you develop tools to get to the actions they need to get differently?”

American Family: A Less Linear Approach

American Family Insurance’s analytics strategy has matured in the couple years since its division was launched, according to Eduardo Fontes, who was named research and analytics director this year.

“We have been boosting predictive modeling capabilities and using less generalized linear models (GLMs),” Fontes says. “GLMs are still widespread across our business, but when it comes to machine learning, non-linear techniques transfer and analyze data more quickly and capture different signals we never have before.”

The new technique means that data like customer phone calls and claims data are analyzed with an open-ended feel. Leveraging machine learning means that the data can take its own path to find the optimal workflow for AmFam associates.

“We recognize we don’t have a lot of touchpoints. That’s fine,” Fontes says. “We want to make the basic obligations better. New data [from calls and claims adjusters] allows us to standardize workflow. We use this data to really streamline the claims process.”

American Family is also applying its new-look analytics to usage-based insurance. The company is invested in digital signal processing technology that Fontes says “limit the amount of time it takes to tell a driver how well they drive.”

“We generally ask ourselves, ‘Do we have the internal capabilities to do what we want to do and can we learn the skills in the short term?’ If not, we use a vendor. Vendors speed up the learning process,” he says. “We are currently leveraging and learning from the [UBI] pilots to do a deep dive.”

Gen Re: The Power of Predictive

Guizhou Hu has been VP, chief of decision analytics for Gen Re’s Individual Products Division, based in Stamford, Conn., for a year and a half. His latest accomplishment was helping to incorporate Mortality Assessment Technology (MAT) he developed in his previous position, at the healthcare company BioSignia, into the life and health reinsurance process. MAT, Hu describes, is a statistical methodology to build prediction models — a crucial component of Gen Re’s strategy.

“Combined with advancements of machine learning technology, we have developed prediction models which outperform any underwriting models in the marketplace,” he says. “Not only can we use them to improve our own facultative underwriting decisions, but we can also offer models to our customers as a value-added service.”

Gen Re’s decision analytics team has been in place for five years, Hu says. Charged with improving data gathering and data management processes for both internal and external data, it collaborates with IT and business units like underwriting and actuarial “to maximize the advantages provided by available data.”

“The key driver for us has been the competitive advantage that can be gained through improved underwriting via analytics,” he says. “Going forward we will focus on additional products and other functions including claims and marketing.”

Munich Re: A Future Focus

Munich Re’s chief data officer, Wolfgang Hauner, says the company is looking to innovate more, leveraging new partners from the startup community to add to its analytics portfolio.

“Having senior innovation executives co-located at Plug and Play’s Silicon Valley facility has given us the opportunity to learn and experiment with new technology and provide valuable insight to entrepreneurs about the insurance vertical,” Hauner says. “Our goal here is to understand the future of risk and identify new opportunities for our global reinsurance and specialized primary insurance business.”

Analytics serve to help Munich Re “push the boundaries” of insurability, Hauner explains. “Our central center of competence is connected to various analytics units and helps us to bring big data approaches to our core business and de-centralized analytics units,” he says. “More specifically, some of our new platforms are geared towards a faster evaluation of data, while in other cases big data has influenced our approaches to understanding risks. This applies to all lines of business and has effects across a wide range of fields.”

In addition to Plug & Play, Munich Re is partnered with Hortonworks in Germany, which is helping the company develop a Hadoop-powered data lake. “Our cooperation with Hortonworks is an important part of our infrastructure design that will allow us to gain more insight by combining internal and external data,” Hauner says.

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