It's become pretty obvious that you don't have to be a math whiz to appreciate what analytics can do for your organization. No longer a blend of technology and processes employed by elites to perform merely a single function such as thwarting claims fraud, analytics is now a true discipline for insurers aiming to measure, manage and respond to insights that affect both the back and front office. In fact, from enhanced risk management and product development, to fund leakage, to finding niche pockets of new business, analytics has emerged as a number-one priority for insurers, according to Lori Sherer, senior expert in the insurance practice at McKinsey & Company.

"In the past year, several major insurance companies have hired heads of analytics, in effect creating a Czar of Analytics," Sherer says. "We see this as an important trend, because it signifies that insurers are focusing their efforts on this discipline to improve their value in the marketplace."

The growing popularity of analytics is timely, as insurers are called to respond to cost reduction and organic growth mandates. As a result, many insurers see opportunities to apply analytics beyond the traditional underwriting, actuarial and claims areas. And many are placing their focus squarely on a customer-centric business model, which carries with it layers of data, from internal cross-functional areas that touch the customer to external sources such as social media sites.

"We've seen that it's less tied to the economy and more to competitive advantage," notes Stuart Rose, global insurance marketing manager at SAS.

Insurers are currently spending 9 percent of their total IT budget on data and analytics, a collective $10 billion per year, according to results of a survey conducted by research and consulting firm SMA. Further, more than three-quarters of insurer respondents confirmed plans to increase spending on data and analytics over the next three years, notes SMA's "Data and Analytics in Insurance: The Dawn of a New Era" report.

Carriers that view data as a strategic asset are finding that obtaining budget dollars for analytics may be tied to an expected cost of doing business but not necessarily one tied to the IT department. In other areas, the enhancement of data analytics doesn't require any cost justification because the technologies to manage it are already in place. Regardless of where the resources come from, however, investments in analytics are always tied to demonstrable returns.

At RLI, a specialty lines carrier, funds for analytics come from the company's internal audit budget. With $619.17 million in 2011 revenue, RLI is focused on profitable growth and bottom-line results, and requires all departments, including internal audit, to add demonstrable value to the organization.

To do so, the company spends approximately 1,000 hours per year, or roughly 10 percent of its internal audit budget, on a continuous auditing program that has data analytics embedded in the process.

"We might be called 'data cops,' but we consider auditing an essential service component to the business," says Seth Davis, RLI's VP of Internal Audit. Engaging ACL Services to effectively access files, scrub data, develop abstracts and analyze results, RLI uses ACL's computer-assisted audit tool to develop a fraud indicator approach that looks for red flags such as P.O. Box payments and missing tax ID numbers, which may highlight anomalies. To date, the company has developed 115 extracts just on fund leakage, such as duplicate payments, missed subrogation, claims events and procurement. "We identify the anomalies and send those to the appropriate business unit to pursue and collect," says Davis.

The results are impressive. "Early on we calculated a $25,000 spend for our continuous auditing efforts," Davis says. "We spent less than $5,000 for the tool and, because we are a small shop, we leverage interns working on their accounting degrees who spend 700 of the 1,000 hours per year helping us with initial maintenance, enhancement and review of the ACL scripts, further driving down costs. The return on this has been the identification of $100,000 annually in fund leakage."

Four Stages of Competency

Hyoun Park, principal analyst at Boston-based Nucleus Research, agrees that there are significant returns available to insurers that become an analytic enterprise. "ROI is different for each project in each organization," he says. "Direct benefits, reduction in labor, reduction in software and hardware footprint, all can be directly related to a quantitative number."

Park maintains that the more an insurance company spends on analytics, the higher the rate of return, yet insurers can see high ROI on even small, initial deployments. "Beyond report automation, companies can use analytics to drive continuous improvements to business processes and decision making, and as organizations become more analytic, they will earn increasing rates of returns on those investments."

Park bases his comments on a Nucleus study conducted this year of 60 deployments across a variety of verticals that revealed companies' progressions through four distinct stages of analytic competency: automated, tactical, strategic and predictive. Within each stage, survey respondents identified their average ROI.

"Insurance companies tend to be good at the automated stage of an analytic enterprise, where they can grab data, report on it, get off paper and move to electronic reports. This stage is common in insurance." Park's research puts the average ROI at this level at 188 percent.

"The battle comes at the tactical stage because analytics is tied to integration and quality," Park adds. "It's bringing in new or multiple sources of information and making sure it's all consistent and meets a format that can be easily used by users and customers and by various claims and field agents. In essence, it's making sure they all learn the same story." Insurers that fall into the tactical category can increase the returns on their analytics investments from 122 percent to 348 percent, adds Park.

RLI classifies itself in the tactical area. As part of each audit during the planning phase, RLI conducts trending analytics to track key ratios, including profit margins, return on assets, working capital, debt-to-equity ratios and more. "It's more trying to understand where we want to focus," Davis says. "For example, is there any one location, underwriter, specific product or sub-product that is showing results that are abnormal?"

Davis cautions against ignoring important soft benefits, citing the assurance that is provided to RLI's executive management team. "For us, the benefits far outweigh the costs over time. It's one thing to look at 30 items and try to extrapolate that to the population, but to be able to look at the entire population is a great assurance to management of what's out there."

SAS's Rose asserts that the secret to analytics investment and return is in aligning with management to make sure you all share the same agenda. "You can't just move forward with an analytics agenda without knowing where you are going in terms of business focus. You should walk before you run, but seek to realize the business value quickly."

Carriers that have used analytics to align daily operations with the goals of senior management can count themselves in the strategic category, says Park. "Insurers at this level experience an average return of 968 percent as a result of more pervasive use of analytics, and the embedding of analytics capabilities into non-analytic processes."

Since joining the company in January, Jose Trasancos, SVP, Sr. Personal Lines Officer at Utica National Insurance, a multiline carrier, has been leveraging the company's existing resources tied to a traditional risk modeling analytics program in order to broaden its reach to that strategic level. "You can achieve tremendous leverage in process environments such as claims, customer service, underwriting and policy processing, which stand to gain increases in both quality and efficiency through the application of analytics," he says.

Under Trasancos' leadership, the mutual company, which reported $725 million in direct written premium last year, is moving beyond point solutions to adopt a holistic approach designed to build analytics into its technology platform and embed decision support across a variety of functional units.

"Our approach is one in which we examine and measure what we do, how we do it, how often we do it and identify what the acceptable variation around a mean is," he says. "In the context of optimal economics, we become a much more efficient organization and deliver better service at the same time."

Fortunately for Trasancos, the tools necessary to accomplish this approach were already in-house when he arrived. "It's more a matter of orienting the culture to the value and the leverage associated with applied analytics... going into the future as we grow. As we are able to demonstrate the value of this approach, I suspect our staffing and other resources will reflect that."

In its quest for process and financial optimization, Utica has employed SAS solutions and data quality services along with decision support tools such as the Quadrant batch comparative rater and Towers Watson's Rate Assessor.

Trasancos says that some of the initial returns are largely soft, but nonetheless important. "The environment is in transition, and the organization is increasingly clear on the importance of data and our ability to improve and evolve."

Embarking on risk model conversions that have yet to be implemented, Trasancos' team is also focused on improving a traditional rating model for its auto product. "We are already seeing and expecting significant returns in our ability to control those financial performance requirements," he says.

It helps that Utica's management team views data as a strategic asset. "A lot of the things we'll use in our production environment tomorrow are being prototyped in end-user space today," says Trasancos.

If all goes according to plan, Utica hopes to evolve its program to one that leverages predictive analytics as a normal course of doing business. Reaching that stage should net Utica an average analytics ROI of up to 1,209 percent, notes Nucleus.

Not surprising, Tier 1 insurers, such as Progressive and Geico, have already proven an erudite understanding of the power of predictive analytics, leveraging huge investments in high-performance data warehouses and data engines that can do the hard-core "big data" work to harness big business. And as an early adopter of SAS Visual Analytics, XL Group plans to streamline big data and harness in-memory analytics to create an expedited, replicable predictive modeling process. With an average ROI of 1,209 percent, insurers in the predictive category can expect to enjoy higher returns with projects such as Web-based customer sentiment tracking and demand forecasting as a result of data integration with distributors, Park says.

"In our study results, I was surprised to see investments in big data and predictive modeling already providing these returns," Park says. "It's interesting to see companies looking at big data approaches that drive value, assigning specific business value propositions to the tasks they are trying to complete and, as a result, have been able to succeed in quantifying those results."

McKinsey's Sherer says it goes back to the old saying, "it isn't about the data, it's about how it's applied for economic value."

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