Managing risk. It's a fundamental function of an insurance carrier. How well property & casualty insurers understand risks and exposures, and how well they price policies based on risk intelligence can have a tremendous impact on the bottom line.
In our increasingly electronic world, the data needed for risk analysis is abundant and growing. There are more than 4 trillion gigabytes of digital data in the world today, and growing daily. In fact, according to IDC, we can expect 44 trillion gigabytes by 2020.
Tons of available and projected data
The challenge for many insurance companies is what to do with all the available and projected data. If managed smartly, data can potentially be leveraged to make better risk evaluation, increase underwriting discipline, better predict market needs and outcomes, and help avoid bad decisions. But how can insurers optimize data? And specifically in underwriting, how can they deliver useful and relevant knowledge to underwriters without overwhelming them with waves of impractical or inoperable information?
To achieve intelligent, data-driven underwriting, insurers often face three hurdles: (1) setting up the infrastructure to handle the many, often disparate, data sources; (2) building the analytics framework to understand the data; and (3) implementing the workflow to expedite the data to the people who are making the decisions. In many cases, those roadblocks persist, despite much attention and resources to streamline risk assessment processes.
In recent years, many insurers have also made significant investments in policy management systems, vastly improving the efficiency of issuing, servicing and renewing policies. But parallel investments in underwriting management systems have been slow in coming, in part because of the perception that the full range of functionality to properly and precisely assess and underwrite business already exists within the policy management system.
I'm often asked, "What underwriting functionality do I need that's not already in my policy administration system?" The simple answer is "a lot," especially in today's highly complex and competitive P&C market, and even more so in commercial and specialty lines of business.
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Well-designed underwriting technology
Underwriting management systems allow insurers to better evaluate, manage, and price risk. And well-designed underwriting technology is built to integrate with, and augment, policy administration systems—by providing the workflow, collaboration, engines, and the analytic tools needed to drive sound, data-driven underwriting decisions. In fact, far from being redundant, underwriting management systems enhance the capabilities of the policy administration system, especially for complex risks and in support of strategic, account-based book management. The two platforms can work seamlessly together, drawing risk evaluation knowledge and risk management efficiencies into both underwriting and policy servicing processes.
Clearly, many insurers have already realized the need to develop their data management capabilities, particularly as more and more data sources become available. And most insurers, if they haven't already done so, will need to invest in data-powered systems that make their underwriting and policy servicing environments more automated and agile. The modern, best-of-breed generations of those systems can put the data in the right context, allowing underwriting and policy service teams to absorb only the pertinent data that assists in making the right risk analysis—fast.
Automation can make underwriters more efficient, valuable
Automation is sometimes associated with diminishing the role of the underwriter. In reality, data-smart systems can make underwriters more efficient and valuable, to the direct benefit of the insurance enterprise and policyholders. Consider that underwriters frequently spend much of their time looking for the data they need, instead of applying their considerable skills and intuition to making astute underwriting decisions. Data-driven systems allow underwriters to spend more time actually underwriting. Additionally, the acumen of the best underwriters can actually be captured and promulgated throughout the enterprise via rules-based underwriting technology.
Today, new data sources are everywhere. Just about everyone has a mobile phone, and the insurance industry as a whole is doing a very good job of creating mobile applications for reporting a claim, paying a bill, or managing a policy. But the real power of those devices will likely evolve and expand as data collection devices to provide better, deeper perspectives on risk. The amount of information and analytics that mobile phones could provide is boundless. Many insurance companies are exploring and operationalizing such rich sources of data. Through mobile devices, insurers are gaining valuable insights into behavior and risk, and often doing so without triggering privacy and personal or proprietary information concerns.
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Unstructured data, such as images and social media output, are also examples of expedient data sources. Smartphones, surveillance cameras, telematics, the "Internet of Things," satellites and drones make data capture much more prolific and ubiquitous. But very few underwriting and policy management processes have the innate capacity to include varied data sources that correlate with risk and exposures, sometimes even at the basic level of images of the assets being insured. Such limitations are diminishing as it's becoming much easier to capture, access, and add various types of data and images to the risk-assessment process. But insurers must also assess the capabilities of their underwriting and policy systems to absorb and operationalize the spectrum of rich data.
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Dedicated underwriting systems can also create better underwriting discipline and visibility. For example, to combat fraud at both the front and back end of the policy lifecycle, technology and data-driven risk evaluation can provide the means to correlate information—from credit reports, background checks, ID verification tools, social media footprints, and other data sources—during the risk intake and evaluation process. In an underwriting workflow driven by data and technology, expert risk assessment discipline and visibility is institutionalized to help reduce exposures.
ID risk and exposure trends
Meanwhile, the real power of data-driven underwriting is in the exponential boost in analytic capabilities. In the insurance industry's vast and varied books of business, revealing trends and patterns can often go unnoticed. Sophisticated fraud rings are being uncovered and broken up using pattern recognition tools driven by intelligently correlating and analyzing data repositories. Considering the variables and permutations, fraudulent patterns are often difficult to spot and follow without analytic tools. Fraud is often the dramatic illustration of data-enabled risk management. But analytic tools can look at historic, environmental, and behavioral information in conjunction with a variety of other data sources to help identify all types of risk and exposures insurers may not otherwise recognize.
As an industry, insurance is becoming much more mature in collecting, analyzing, and leveraging data. It's not just about hoarding and centralizing data. The frontier of risk management innovation is in the responsiveness and maneuverability of data to be more fluid and accessible where and when it's needed. For its strategic relevance to insurer productivity and growth, the front lines of P&C underwriting and policy service are critically dependent on data and analytics refinements. Modern underwriting platforms that seamlessly integrate with, and support, policy management are fast becoming an essential element of the strategic technology value chain of successful P&C insurance companies.
John Belizaire is the CEO of FirstBest Systems, a leading provider of underwriting workstations for commercial and specialty lines. He is an accomplished enterprise software entrepreneur with a track record of new venture development and management success. Prior to co-founding FirstBest, John was senior director of business development and strategic planning for BEA's E-commerce Applications Division, where he grew annual revenue to over $150 million. Before BEA, John co-founded The Theory Center, a leader in Java-based component software for enterprise applications.
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