BIG DATA'S BIG GUNS: QBE NORTH AMERICA
QBE North America, which is headquartered in New York and provides general insurance and reinsurance through five specialized business units, named Gina Papush chief data and analytics officer in September. Papush brought with her a wealth of data experience from across financial services — she previously ran data functions for Citigroup’s mortgage unit and also worked at GE Cards — and she says that insurance is well-positioned to take a leadership role. Before big data, “QBE had a number of analytics occurring within the firm, and we have a very strong financial and actuarial function where there is a history of that,” she says. “Now that we have a centralized function or federated model, we’re driving the strategy and speed of innovation. It’s allowing us to create more scale and move faster and focus on top priorities for the business.” Commercial insurance, like life insurance, requires a long underwriting process. But business owners demand faster service, Papush says, and that’s where big data comes in — access to as much data as possible helps get policies done faster. And, she says, insurance can learn from the example of the mortgage industry, which over-relied on automation and reduced underwriter involvement before the financial crisis. Insurers, instead, should use analytics to make their underwriters even better at their jobs. “The mortgage business got into a bind, because it is a complex lending decision not unlike a complex insurance coverage decision, she says. “Whereas that industry went away from disciplined underwriting, what we’re talking about is injecting insight and intelligence to make better decisions. People expect decisions on complex commercial decisions faster than ever before. It’s very important that there is a strategic focus on leveraging analytics and data to drive business impact.” Source: Insurance Networking News, May 2015
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BIG DATA'S BIG GUNS: NATIONWIDE
Multiline carrier Nationwide is taking the lead not just on effective use of data and analytics in its customer segments, but exposing the next generation of data professionals to the exciting opportunities afforded by the insurance industry. “I flew jets in the Navy up until 2002,” says Mike Kozub, SVP of customer insights and analytics for Nationwide. “And I think insurance is a fascinating industry for a couple reasons. First of all, you’re managing risk. You want to grow, but you can grow the wrong way. So there’s real high stakes when it comes to making decisions. “And more recently, you have channel conflict,” he continues. “Plus, you’re interacting with consumers who largely don’t want what you’re selling, and at the time that they get to enjoy the fruits of what you deliver it’s a negative interaction [because they’re filing a claim for a loss].” Like MassMutual, Nationwide has partnered with a local college — Ohio State University, the university with the third-largest enrollment in the country. The Nationwide Center for Advanced Customer Insights (NCACI) gives OSU students in advanced degree programs the ability to work with real-world data to solve some of the biggest insurance business problems. Faculty and students from the marketing, statistics, psychology, economics and computer science departments work with Nationwide to develop predictive models and data mining techniques aimed at improving marketing and distribution, identifying consumer behavior patterns, and increasing customer satisfaction and lifetime value. The NCACI opened in 2008 and employs eight to 12 students at a time for about 20 hours a week, says Chris Nicholas, VP of customer analytics for Nationwide. “The feedback we get constantly is that students can’t believe the diversity and complexity of the problems we get to work on,” he says. And students quickly learn that as they develop solutions, “if we come up with a robust answer, it will be put into practice.” An increasing percentage of Nationwide’s data science team comes from the NCACI every year — including the executive-in-residence who manages it, Mike McCaslin. He was a social psychology major in college. “What I liked about Nationwide was that it had an academic approach to the analytics work, so even if people we try to bring in on the team aren’t initially interested in insurance, this is a chance to work on really difficult problems” and learn that the insurance industry has robust opportunities, he adds. For a company betting big on customer acquisition — Nationwide spent $352 million on marketing last year, the sixth-most of P&C insurers, according to SNL Financial — hiring and retaining the best data professionals to target the right customers with the right message is crucial. “Folks have gotten the support of the customer analytics team and success becomes a self-fulfilling prophecy,” McCaslin says. Source: Insurance Networking News, May 2015 BIG DATA'S BIG GUNS: CNA
Among its many data-oriented initiatives, CNA is applying big data technology to workers compensation claims and adjusters’ notes. “That is a classic, unstructured big data kind of problem,” says Nate Root, SVP of CNA’s shared service organization. “We have hundreds of thousands of workers compensation claims, and claims adjuster notes, and there is tremendous value in those notes.” Root says the insurer recently began identifying workers’ compensation claims that have the potential to turn into a total disability, or partial permanent disability, without the right sort of attention. By examining the unstructured data, CNA has developed a hundred different variables that can predict a propensity for a claim to become serious, and then assign a nurse case manager to help the insured get necessary treatments for a better patient outcome, get them back to work and lower the overall cost of coverage. For example, the program can find people who are missing appointments or who are not engaged with physical therapy and should be. “We are not prescribing medical treatments,” Root says. “We get the nurse case managers involved to work cases they ordinarily would be working, but three or four months sooner.” CNA is using a variety of technologies, including mainframe-based financial information, Root says, as well as Guidewire ClaimCenter. “We have enabled text mining off the claims adjusters’’ notes, we run that through our predictive modeling approach with SAS and R, the open source statistical analytical software,” he adds. The algorithm runs at specified times in the claims’ life span, and if flagged, those claims enter a different triage routine. “We have 20 different categories of claims. On some we use ‘red’, ‘yellow’ and ‘red.’ The red claims are obvious to anyone. The green are expected to remain non-serious. And the yellow ones could go either way. That’s where we want to spend the most attention,” Root explains. Root says this is merely the latest big data application for CNA, which also has been using big data techniques on underwriting, pricing and market analysis, to identify where opportunities could be developing. CNA also is continuing to explore the vast amount of data available for purchase. “It is fairly staggering,” Root says, “as is discerning what’s predictive and what’s just noise.” Source: Insurance Networking News, May 2015 BIG DATA'S BIG GUNS: JOHN HANCOCK
For a long time, life insurers danced around the issue of how wearable technology could impact the way they evaluate the riskiness of applicants. But the technology was of clear interest to an industry that has struggled to attract new buyers due to its typically onerous underwriting process, involving collection of blood and urine samples as well as a long wait time. “It bothered me that we asked for so much information — practically inches and inches of paperwork — and at the end of the process all you get is your risk classification,” says Brooks Tingle, SVP of insurance marketing and strategy for John Hancock “For all they’ve given us, we don’t give them a whole lot of insight.” So Tingle and his team set out to find a way to leverage the wealth of data collected by wearable technologies, including the popular FitBit and recently released Apple Watch, to give something back to their customers. The end result was John Hancock Vitality, a new life insurance product that offers up to a 15 percent premium discount to customers who track their healthy habits with wearables and turn that information over to the insurance company. New buyers even get their own FitBit to begin tracking. “Life insurance underwriting had always been a one-time event — the insurance company had only one opportunity to assess you,” Tingle says, which he says scared off some people from even bothering to apply. But now with the potential to improve their rate as they improve themselves, “customers don’t mind giving up some data if you’re transparent about what data you’re asking for, and they’re getting real value back for it.” It’s a watershed moment for life insurers, Tingle explains, because the industry is drastic need of revitalization. “The ability of this data, and predictive modeling, to offer the client a more streamlined and more personalized underwriting process, is crucial,” he says. “We’re thinking: How do we reinvigorate things not just for John Hancock but for the industry, and make our solutions more relevant to the consumer?” Source: Insurance Networking News, May 2015 BIG DATA'S BIG GUNS: AMERICAN FAMILY INSURANCE
American Family Insurance has several initiatives around big data, including programs for more intensive testing and efforts to leverage unstructured data from claims notes. In September, American Family Insurance licensed APT’s Test & Learn software to enhance customer engagement and increase support for agents. “This is a statistical tool that enables us to create and analyze statistical tests,” says Justin Cruz, strategic data and analytics vice president. Historically, most predictive analytics was focused on pricing, however big data and statistical testing now are creating opportunities for companies to harness marginal value out of their operations, Cruz says, ensuring that the insurer is making the best decisions possible in a variety of areas. For example, call-routing techniques affect wait times and, ultimately claims satisfaction. The insurer also tracks how claims are handled, and by whom, and whether agents are involved in resolution. Using APT, the insurer can isolate variables and accurately determine the success of one design vs. another for various products, geographies or demographics, Cruz says. “We’ve always been focused on providing value to the customer, but we are zeroing in on doing that in a more rigorous fashion, using data, using technology to prove whether we’re impacting the customer in a positive way,” Cruz says. Unstructured data, such as that collected in call center transcripts, also can be studied to better understand what approaches are best for different situations, he says. “Hadoop and other tools enable natural-language processing and sentiment analysis,” Cruz says. “We can look for key words or patterns in those words, do counts and build models off textual indicators that enable us to identify three things: when there could be fraud involved, where there might be severity issues, or how we can get ahead of that and plan for it,” Cruz says. “We couldn’t have done this before, and you never could have analyzed that unstructured data. It just was almost virtually impossible in a relational database to do that.” Customer communication, web design and direct mail are other areas the insurer is, or soon will be, using APT, Cruz says. “You can look at sub-factors within two or three types of direct mailers and ask questions like ‘Do we see greater lift in these geographies vs. those? Or, from younger vs. older customers, or customers that came from these insurance companies vs. other insurance companies.’ It enables you to assess these factors one by one in isolation.” Source: Insurance Networking News, May 2015 BIG DATA'S BIG GUNS: MASSMUTUAL
Last year, looking to ramp up its big data and analytics capabilities quickly, MassMutual hired seven graduates from an ambitious new program the insurer created in collaboration with several colleges in the Springfield, Mass., area. MassMutual executives, led by SVP of analytics and research Gareth Ross, worked with faculty and administration to combine extended coursework for graduate students with real-world projects at the company, including machine learning, lead scoring, and natural language processing. “At their core, insurance companies are vast decision-making engines that take and manage risk. The inputs into this engine are data, and the capabilities created by the field of data science can and will impact every process in the company — from underwriting to claims management to security,” Ross explains. “Within a decade, I believe that this will be a core function of all insurers.” Participants work in a startup-style office in Amherst, Mass., rather than at the company’s Springfield headquarters, to foster a more collaborative, startup-like environment. Now a year older and wiser, MassMutual is taking the program to another level. The life insurer announced earlier this year that through its program, it would work with Smith and Mount Holyoke colleges — traditionally women’s schools — to “support women in fields of science and engineering” even if they don’t go right to work for MassMutual. And it’s not just the women’s colleges that are increasing their engagement with the program. Ross says that he has spent much of the time since the program started trying to engage professors at a higher level. (Other colleges involved in the program include Amherst College, Hampshire College, and UMass Amherst.) The company hosted a data science salon for professors to discuss major issues in the field and get more involved with helping develop the academic-corporate partnership. “It’s a very collaborative field, and we wanted to have a close collaboration of the corporate goals and the body of knowledge represented by the academics in the area,” Ross says. MassMutual says it will hire up to 10 students per year, and Ross said he had received about five times that many applications in February. “It’s so hard to find to seasoned talent, but the first eight months were a resounding success,” Ross says. “This has proven itself to be valuable and we are scaling up. In fact, we recently launched Haven Life, an online insurance agency that uses an algorithmic underwriting tool and series of related decisions that was created in collaboration with our team of data scientists.” Source: Insurance Networking News, May 2015 BIG DATA'S BIG GUNS: PROGRESSIVE INSURANCE
Few insurers are as well-known for their big data efforts as Divakarla’s own Progressive Insurance, which has pioneered insurance telematics and web-based quoting and sales, all of which lean heavily on big data technology. To serve that strategy, the insurer has invested considerably to build a big data infrastructure and a culture. While many insurers think about big data as if it were an overlay, Divakarla says data is integral to the insurer’s mission: “Big data, small, it doesn’t really matter: We want it to be in our fabric.” Divakarla is responsible not only for helping build and design the insurer’s big data infrastructure, he also educates and motivates business users to leverage and contribute to it. “I’m the supply man and the demand person at the same time,” Divakarla says. “I’m generating the use cases, helping folks to get in there, test it, and then move on.” And that requires talent, but not just a handful of formally trained data scientists, Divakarla says. “They could be using data science principles but still be an analyst. It’s like a welder that also has good woodworking skills.” Among the many big data related initiatives at the company is the Business Innovation Garage, Progressive’s latest expansion of efforts to improve employee engagement and the customer experience by bringing ideas to prototype and production. Most recently, efforts have centered on customer engagement and mobile applications for quoting and insurance telematics. Agents increasingly want mobile enablement, and not just the ability to quote, but to bind and sell policies on smartphones and tablets. “Our quoting platforms that we’ve had for many years were not necessarily built for that,” Divakarla says. “We had to kind of retool our way into these systems so they can be more modern. Up until a couple of weeks ago, we had a quoting application that was using Adobe Flash. We sunset that. The new mobile application has been serving a lot of the desktop customers for a while now.” He describes the construction as “modern stack,” including HTML 5, a responsive web design framework, JavaScript and more. In the pursuit of leveraging big data and small across the enterprise, Divakarla stresses the importance of eliminating “technical debt,” the eventual consequences of poor system design. “We look at the entire architecture for everything and we say, ‘OK, this piece goes this way; what other pieces have to move?’” Divakarla says. “Those are sort of key pillars. None of these are meant to slow down the train.” Source: Insurance Networking News, May 2015 Big Data vs Fast Data
Before Big Data reaches its maturity, we are seeing the arrival of yet another IT techword - Fast Data. I read an interesting comment below recently: "........ many companies are so focused on warehousing and pulling the insights from big data, they forget about the importance of getting fast data insights to be more relevant to their customer in timely fashion." “Seven-day-old data is like a seven-day-old doughnut. No one wants to consume that” I will compile some articles on Fast Data to share with you shortly. Stay tuned ..... |
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