Insurance companies can use data to boost customer retention and acquisition, detect fraud, create a more satisfying customer experience, transform the contact center, find operational efficiencies, create competitive advantage and improve claims operations. It’s the underwriting function, however, that stands to gain the most from increased agility with data. Successfully leveraging big data can make an enormous difference in the underwriting process. Underwriters can use big data to gain insights, inform decisions, drive innovation and improvements, and better understand and assign risk, all leading to greater insurer profitability.
But before they can take advantage of all the opportunities and advantages big data can provide, underwriters first must deal with a host of data-related challenges, especially as data burgeons, growing exponentially, relentlessly and almost unfathomably fast. In fact, according to the 2014 IDC/EMC Digital Universe Study, the amount of data in the digital universe is anticipated to grow from 4.4 trillion gigabytes in 2013 to 44 trillion gigabytes by 2020. Data production will be 44 times greater in 2020 than it was in 2009. And, the study notes, although more than 70 percent of the digital universe is created by the actions of consumers — for example, posting a picture online — enterprises assume responsibility for 85 percent of it, including items like account information and email addresses. IDC also predicts that big data spending will grow to a massive $125 billion this year.
“Having access to timely and quality information allows for a more robust underwriting process,” says Monica Ningen, head of property underwriting, U.S. and Canada, for Swiss Re. “The ability to gather, sort and analyze massive amount of data is a comparative advantage.”
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With millions of bytes of data created each minute, big data has become a force to be reckoned with. The wealth of data from a wide and growing variety of sources — texts, social media, automotive telematics, internal company information, customer background and many, many more — can inundate and overwhelm businesses of all types and sizes. On the other hand, organizations that can successfully harness the power and use the wealth of information from structured and unstructured sources to its greatest advantage will have on their hands a powerful, game-changing resource.
While insurers of all types and sizes are being flooded with data — and every business unit is looking to get its arms around the information — at some of the largest global insurers, underwriters are leading the charge for top-of-the-line data management.
Data Management Challenges
At Swiss Re, underwriters were dealing with substandard data quality, disorganized data and process inefficiencies. “With increasingly more granular data where more’ is not necessary better,’ data quality is a challenge,” Ningen says. “With that said, perhaps the biggest challenge of all is time constraints. We are operating in an environment with increased demand in time to market, and the need for prioritization of processes, smart algorithms and analytics with an ultimate focus on pricing and underwriting is immense. We have done all of the above in the last few years.”
Meanwhile, Allianz Life faced challenges receiving and storing data in a format that enabled it to be easily accessed and manipulated in its dayto-day underwriting, says Tim Juneau, the firm’s chief underwriter. In addition, the insurer is looking to enhance underwriting decisions by developing technical capabilities that run data through an automated underwriting rules system.
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“Over the past few years, the volume and variety of underwriting cases has changed. Allianz Life is expanding its life business, which increases the volume of underwriting work. Our underwriting cases also vary in size and require varying levels of underwriting scrutiny,” Juneau reports. “As we look to be more innovative in the industry, Allianz Life is evaluating its use of predictive analytics and automated underwriting engines. We want to make the right changes so we can manage and evaluate underwriting data with less manual input. The changes we make are driven by our desire to remain competitive in the marketplace and create a stellar customer experience.”
Allianz Life believes that an innovative data management strategy offers cost savings and a better customer experience. “Having a great data management strategy decreases the unit cost per life application, decreases the cycle time and increases the consistency of our underwriting decisions,” Juneau notes. “Additionally, there are ongoing benefits that come from the ability to run detailed reports for all parts of the underwriting process. Predictive analytics help with both the evaluation of individual cases and the identification of trends that may require strategic changes to best support our business.”
Getting to a Solution
Today, it’s become essential for underwriters to optimize their data management to deal with the vast and changing data-related challenges.
According to Craig Beattie, senior analyst at Boston-based Celent, an optimal data management strategy should save underwriters both time and effort and enable them to bring business in to their organization. “A great data management strategy should save the underwriter time.” Beattie says. “It should enable insurers to better assign risk. It should promote efficiency, allowing underwriters to capture data just once and let them access the right data at the right time, and it should automate processes wherever possible.”
A great data management strategy can significantly benefit the underwriting function, according to Swiss Re’s Ningen. “A great data management strategy helps sustain high-quality underwriting over time,” she says. “As underlying data becomes larger and more complex, beyond structured data, having a solid data management strategy allows us to tap into areas where we are aligned with our clients’ need, that intersection where we can be smarter together and provide value-added to the original supply chain.”
According to John Anderson, senior managing consultant in the Strategy and Analytics practice of IBM Global Business Services, the underwriting process has changed in several ways over the past few years. Anderson says it’s become essential for underwriters to have the ability to gain real-time access to data insights, to organize data for easy access, to be able to convert unstructured data into consumable insight, and to institute a closed-loop data integration to give underwriters and the claims department the means to see how policies have worked out in the past.
“A good strategy enables you to capture not only the successes, but the non-successes,” Anderson notes. “It’s also become essential to know what data to keep. With big data, you can gain millions of bytes of information every minute. It’s critical to know what’s really valuable and what’s not. Technology makes it possible to capture and store all this new data at phenomenal rates.”
But there’s a key question: Do insurers want to become repositories for massive amounts of data?
The wealth of data is supposed to benefit underwriters, but, Beattie says, too much data or the wrong data is distracting. “What we’re actually doing is flooding them with data, hiding some of the details that might be useful to them, giving them extra work to do and preventing them from bringing business in to their employer, which is what they’re there for,” Beattie points out. “At the same time, they’re being challenged to bring in profitable business, which means having access to the right data at the right time with what you’re looking at. We’re not there yet, and underwriters are suffering as a result.”
Richard Ward, director of strategic consulting for Pitney Bowes, agrees that overabundant data can sometimes be a liability rather than a benefit. In fact, more data can slow down the underwriting process if an underwriter is not looking at the right data. “While more data is better, unless it’s consolidated down to friendly usability for an underwriter, it can be counterproductive, more damaging than helpful.” Ward says. “If it’s not a consistent set of appropriate data, it might take longer to make a decision, and an underwriter might not make the most effective decision.”
Bringing all the data together into a consumable format, where an underwriter can look at just the data that’s pertinent to the type of business he or she is underwriting, has become an organizational challenge, Ward explains: “Many carriers still run 40-year-old legacy policy administration systems designed to administer policies and manage the claims process. However, these core systems were not designed to handle all the data sources that have been created over the past several years, and are certainly not capable of utilizing this data in the underwriting decision-making process.”
A complete rip-and-replace core systems transformation is not a viable option for many insurers. As an alternative, carriers are looking at complementary platforms that can be integrated into their existing policy administration systems, technology that can enable insurers to take advantage of new and emerging data sources. Small- to mid-sized organizations are typically more able to take the leap and find complementary technologies from the vendor community, while the larger enterprises tend to want to build their own.
“The smaller carriers have easier and cleaner consolidation efforts since they don’t necessarily write the number of lines of business that the larger carriers do,” Ward says. “A nimble and agile approach is an advantage to smaller carriers because, quite frankly, they’re competing for the same customers these days. In the past, the big sophisticated risks went to the big-tier carriers. Today, the mid-tier and smaller firms can also compete for those same types of risk, as long as they’re nimble and agile in their thought processes in how they utilize data, and if they can use technology to complement their core systems.”
Collaborate for Success
One key to successful underwriting, says Vinod Kachroo, CTO and global head of industry solutions for TCS’s Insurance and Healthcare group, is enabling underwriters to collaborate, whether within a single office or with colleagues across the globe. “Enabling and combining the next-generation workplace will truly enable carriers to create a collaborative underwriting environment,” Kachroo says. “So today, you have an underwriter assigned to a case. In the future, I want to involve several underwriters to collaborate get the case done. You might have the best resource somewhere around the globe in the enterprise.
“Underwriting is a fun job,” he adds. “It is a job where you make a decision based on data, analytics, and insights and experience. But the reality is, about 80 percent of the time underwriters are not underwriting. Underwriters can spend months gathering and collating data, feeding the data into analytical models and looking at the results. Insurers need to change the underwriting model to enable underwriters to focus on decision-making and to leverage their experience and optimize their value and time instead of merely gathering, collating and entering data.”
Today, technologies enable carriers to automatically gather and combine both structured and unstructured data; underwriters can then feed that data into analytical models and develop what-if scenarios. “That’s really what underwriters should be doing,” Kachroo says, “looking at the client, the industry, the client experience historically — and, based on the insights, doing a what-if analysis and determining how to define and underwrite the risk, defining pricing models and devising loss control models to enable a win-win for both client and carrier.”
According to Kachroo, it’s essential for carriers to create a knowledge base and an enterprise learning mechanism so if a similar case comes up in the future, underwriters can learn from the previous experience of other underwriters.
The Human Touch
Data management tools are indispensable to underwriters, but human beings will always be an integral part of the underwriting process. With increasingly advanced capabilities, technologies can make underwriters more efficient and effective, but seasoned underwriters bring a wealth of experience and insight to their jobs and will always be in demand.
“Technology is the enabler,” TCS’s Kachroo says. “It’s about how you can use the technology to help achieve your goals.”
Even the most sophisticated technology cannot replace living, thinking human beings. “For complex and unique cases, insurers will always need an underwriter,” Kachroo explains. “The goal is to change my underwriting model to enable my underwriters to really focus on underwriting and not have to gather data, collate the data and enter the data into a system, and instead make it possible for underwriters to focus on decision-making so you are able to leverage their experience and optimize their value and time.”
According to Celent’s Beattie, a great underwriting data management strategy should enable a good underwriter to do his or her job and not waste time doing things that don’t add value. “A great strategy is about efficiency; not replacing underwriters, but rather making them better at what they do,” he says.
In fact, while automated underwriting tools are increasing in popularity and effectiveness, there will always be risks that require a human touch to consume and analyze the data, says Juneau of Allianz Life. Technology is a tool for improving underwriting, not for disintermediation of the staff.
“As the use of systems is developed and deployed in the underwriting space, carriers will likely allow their systems to make more and more underwriting decisions based on each carrier’s individual risk tolerance and its confidence in the system’s automated decisions,” Juneau points out. “But the industry will continue to need experienced, professional underwriters to ensure quality outcomes and use the business analytics to refine the automated underwriting decisions. To create an appropriate automated process, we need to use an underwriter’s risk-assessment, problem-solving and relationship-management experience. Underwriters are necessary for continuously reviewing the data and automated decisions to be sure they accurately match risk with pricing.”
Allianz Life’s data management and analytics strategy is rapidly evolving and currently focuses on data governance, data quality, information architecture that defines data needs, information lifecycle management to control costs, data frameworks for storage and retrieval, master data management to manage customer and reference information, and business intelligence analytics to improve decision-making.
According to IBM’s Anderson, technology should help reduce the manual effort on predictable outcomes. “The cognitive reasoning an underwriter goes through while triaging the risk, knowing how the company has pricing for this, knowing what the company’s appetite is for a certain type of risk, knowing what an underwriter’s book looks like — these are all considerations in any underwriting decision,” he points out.
Over the years, underwriting has become very technical with better data and tools, notes Swiss Re’s Ningen. However, she adds, the ultimate decision still requires underwriting judgment: “Better analytics give the underwriter more insights, perhaps even more comfort around the mean, but one must be realistic; not all risks can be measured, and not all terms and conditions can be analyzed. Data can lead you down a path, yet the underwriter defines that path.”
Optimizing underwriting data management can keep insurers profitable in the long run. “Successful data management creates cost efficiencies and predictable outcomes that help increase sales, market share and profitability,” Juneau points out. “Companies that don’t embrace new innovations in underwriting data management may have more difficulty staying competitive.”
