The FISMA Focus IPD

From: Insurance & Technology

Anthony O’Donnell

While insurers are still in the early stages of adopting big data for underwriting, those that can hit the target now are likely to outpace their competitors — if they can get the insight into the hands of decision makers.

Insurers have long seen data as a source of competitive advantage. But data alone is worthless — it’s the insights derived from the data that matter, says Kimberly Holmes, SVP of strategic analytics with XL Insurance Group(Bermuda). And with the emergence of big data, she notes, the possibility for deriving insights is increasing dramatically.

Yet, for those insights to have an impact on the business, they have to have the attention of senior underwriters. “The data analytics mean nothing without the decision makers embracing it,” Holmes insists. “We see a lot of our competitors create models that don’t have an impact because the underwriters don’t use them.”

To foster a more collaborative approach to the analysis of large volumes of data, XL ($45.1 billion in total assets) currently is implementing Cary, N.C.-based SAS’s Visual Analyticstechnology. Holmes describes the solution as a powerful communication tool for creating a partnership of exploration between the analytics team and underwriters. She says the tool will bring the expertise of the decision makers and other stakeholders more deeply into the analytics process by demonstrating the meaning of data more readily and inspiring further exploration and insight.

“It’s really a demonstration of the expression that ‘every picture is worth a thousand words,'” Holmes adds. “The key to getting people to embrace new insights and change how they make decisions is that they believe in their gut that this insight is true.”

Rewriting the Rules of the Game

The world of insurance is changing at an exponential rate as volumes of available data rapidly expand and sources of data proliferate, Holmes asserts. As a result, roles within the insurance enterprise will change, along with the terms of competition. “Commercial insurance will become more efficient by creating more automation in decision making and how we access our customers,” Holmes predicts. “Those changes will happen more rapidly in smaller-account business, but we need the right technology and data to take advantage of that.”

Holmes characterizes XL as one of the few carriers in the commercial insurance domain to act on this vision. “We expect investments such as SAS Visual Analytics to create enormous competitive advantage and shareholder value for XL,” she relates.

But few insurers are at the point where they are ready to talk openly about their big data-related initiatives, acknowledges Benjamin Moreland, a Hartford-based senior analyst with Celent. The world of big data constitutes a paradigm shift for carriers, many of which continue to struggle with issues in their traditional transactional data, he notes.

“Carriers continue to have trust issues with internal data,” Moreland reports. “Many insurers are not used to using data for operational status and decision support because of their skepticism. Also, business-line-specific data orientation has resulted in inconsistencies in reports, leaving C-level officers to ask, ‘Which report should I believe?'”

Insurers that can take advantage of large amounts and types of data early on will be able to do better on pricing and customer segmentation, Moreland says. Their challenge will be driving data into the decision-making process. “Senior leadership often makes decisions on anecdotal evidence,” he notes. “Their instincts may be strong, but they have to determine the worth of those instincts based on whether the data supports it.”

Big data isn’t just a matter of the volume and source of data, but also the speed at which it is processed, Moreland emphasizes. In the past carriers could crunch numbers over time, distribute reports and then make decisions; today many more decisions need to be made in or near real time. Whereas traditionally underwriters may have reviewed overexposure in a given area in hindsight, Moreland observes, “The task for IT today is to bring opportunities to underwriters and other decision makers to support decisioning as events are happening.”  Effective handling of big data, he suggests, will also enable an increasing range of automated underwriting decisions in near real time, such as preventing the writing of new business in an area with the potential to be struck by a developing weather event.

Company size will be a factor in how — and how quickly — a given insurer will adopt big data-related capabilities for underwriting and other purposes, implies Martina Conlon, principal, Novarica(New York). Larger insurers, she says, have made far more progress in the use of large volumes of data — for example, telematics, geo-spatial data,  mobile information, social media data, automated information from weather services and even click streams of visitors to their websites.

“Very large carriers are leveraging big data and operationalizing  automated analytics in their business processes,” Conlon relates. “Below the top tier, most are dealing with more basic issues, such as implementing solid core systems and trying to establish a baseline business intelligence infrastructure for an integrated view of their data, as well as trying to marry-in structured external data.”

The greatest big data barrier for small carriers is cost of entry, as both initial costs and maintenance are high, according to Conlon. Second-tier and smaller carriers also struggle to find the right talent to adopt big data capabilities, he adds. “Lower-tier carriers don’t have the resources to determine whether such initiatives are worth it,” Conlon explains. “Bigger firms can afford to invest in the analysis to make the business case.”

Vendors will help smaller carriers punch above their R&D budget weight, suggests Conlon’s Novarica colleague Greg Wittenbrook. Vendor products will begin including big data-related functionality or enabling capabilities, Wittenbrook says, and more data providers will emerge.

Life-Changing Technology

Whereas personal lines P&C insurance has led the big data trend, commercial lines are approaching a breakout stage, and those commercial insurers that embrace big data will gain market share at the expense of laggards, predicts Tony Pavia, VP, Capgemini (New York). But big data also is driving changes in the life insurance industry, according to Pavia. “Term life underwriting is dramatically different than it was five or six years ago, because of the use of aggregators of data,” Pavia says. “Across the industry carriers are going to fall behind because they lack the  culture to embrace change. Over the next five years you’ll see a real separation between those that are rapidly migrating to new  environments and those that are not.”

Life insurers are looking for new opportunities to leverage data to decrease the cost and intrusiveness of life insurance underwriting, which involves the administration of various medical tests, notes  Novarica’s Wittenbrook. Insurers may be in a position to take advantage of the general trend of consumers to share personal data in exchange for discounts, as well as the move to broader medical records and the availability of social media data, he says. “However, there is a ‘creepiness’ factor related to an insurer knowing too much about a client, and insurers also need to deal with constantly changing laws and regulations governing what they can and cannot access,” Wittenbrook cautions.

Newark, N.J.-based Prudential Financial ($961 billion in assets under management) is very sensitive to the “creepiness factor” and is steering clear of it, according to Mike McFarland, VP of underwriting in Prudential’s individual life insurance business. “There are potential components of predictive modeling that some people find disturbing, but we are not doing that; we’re using traditional risk points but in a  different way,” McFarland stresses. “We’re looking for more economical ways to issue life insurance, which is expensive because it utilizes this very expensive resource we call an underwriter.”

McFarland refers to the emergence of vendors that collect traditional types of lab data and perform risk analysis through a scoring system. But other sources of data also are emerging, such as those that can predict the likelihood of diseases for individuals of a certain age. “Data that used to be hard to get is now aggregated and available in a very usable form,” McFarland says.

Still, Prudential is moving very cautiously with its use of big data to support underwriting, McFarland confides. He  describes the company’s interest in big data as being like “a kid in a toy store” and says the carrier has gone beyond  experimentation, devoting a great deal of resources in its  development and testing of predictive modeling for underwriting. “Predictive modeling will evolve — we could plug it in and make it work tomorrow,” McFarland states. “The question is, Can we make it work at the right price point? It has the potential to be the better mousetrap, but will it really catch mice? We don’t know.”

According to McFarland, Prudential will probably perform several more months of analysis before making a decision to deploy predictive modeling for underwriting. “Whether we’ll roll it out remains to be seen,” he says. “There are several factors: impact on product pricing, whether or not it reduces expenses required to reach an efficiency threshold, the ability to produce the  margins we decided we wanted for that business, and whether we could gather support from our reinsurance partners.”

McFarland adds, “As soon as one or two companies take the leap of faith for a given product or age group, then others will follow, because it will be necessary in order to compete.”