INTRO: The insurance industry is spending more time and money on analytics and big data. Mark Breading, a partner with insurance advisory and research firm Strategy Meets Action, discusses the challenges and opportunities that such a wealth of information presents.

Analytics and big data are vital competitive weapons for commercial lines insurers.

A recent Strategy Meets Action research study has investigated the roles that analytics and big data are starting to play in the insurance industry and has found that insurers are spending significant amounts of money on analytics, funded by both the information technology department budget and by business units outside of IT. The average commercial lines insurer spends 9.1% of the IT budget on data and analytics, while an amount roughly equal to that is spent by business units. All told, commercial lines insurers in North America spend more than $2 billion per year on the area of data and analytics, including big data.

The design of products, risk assessment, pricing, and catastrophe planning are becoming highly scientific endeavors. It's not that creating and using models and computer technologies are new to these areas — actuaries and underwriters have been applying mathematical and statistical approaches for a long time in order to keep up with or leap ahead of their competitors. But the variety and volume of data is now increasing exponentially and is available from many sources.

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The software for modeling analytical problems has advanced significantly and the hardware/software options now available allow insurers to obtain results much faster. The state of the art is advancing rapidly and allowing insurers to understand and model risk more precisely. Insurers expect big data to produce game-changing insights for areas like pricing models, actuarial analysis, fraud detection, and risk assessment.

Location-based data and the wide availability of data on many specific perils, coupled with new approaches such as predictive modeling and big data, allow insurers to more rapidly build, run, analyze, tune, and re-run models. These are not academic exercises. The new insights are being used to determine which specific products, coverages, and geographic areas to write — or to suspend writing. New models and insights are enabling more precision pricing. Sophisticated catastrophe models are helping insurers manage their portfolio of risks and make real-time decisions on adjuster deployment, loss control measures and reserving.

However, the story doesn't end with risk assessment and management. The big trend in analytics is the use of these tools in other areas of the business, including market/customer analytics, operational analytics, and financial analytics. As more and more insurers look to operationalize customer-centric strategies, they are turning to analytics for a deeper understanding of customer needs, behaviors and perceptions. Commercial lines insurers are increasingly using analytics for market segmentation, campaign analysis, and channel performance assessments.

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Challenges and opportunities

However, these approaches often rely heavily on transactional data created by and trapped in legacy systems and databases. Indeed, the top challenges to fully capitalizing on analytics cited by commercial lines insurers are all related to data and legacy systems. Insurers view data quality and completeness as the number one barrier to successfully leveraging analytics. Data within the insurance enterprise is often fragmented — stored in a variety of siloed systems and databases. The variety and amount of risk and customer data available from external providers is staggering — but sometimes the quality of that data is not optimum. It is great to get detailed data on property characteristics from public sources — but it is sometimes incomplete or even inaccurate. The accessibility of the data is another concern cited. Even when data quality is high, it is sometimes difficult to access and obtain the data required for analysis.

In addition to the fundamental challenges regarding enterprise data, the opportunities created by analytics and big data are gated by “big legacy.” Most insurers use a variety of systems for underwriting, policy servicing, billing, claims and more that were developed at different points in time and have been integrated via patches or manual methods. And the systems are often different by line of business — one set of systems for small commercial, another set for workers comp, a third set for mid-market, and so on. Legacy systems also contribute to the data issues, as the quality of the transactional data they manage is often not optimum. In addition, legacy applications sometimes are unable to access and use the data. For commercial lines, the breadth of the data makes access and analysis even more complex.

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Fortunately, more ways are emerging for insurers to address these challenges head on and to improve and extract the data required for analytics. Modern technology architectures are making it easier to integrate systems. Modern core systems for policy, billing, and claims are designed with integration in mind, available in a range of integrated states from best of breed to complete integrated component based suites. Data cleansing software and methodologies are more advanced and enable insurers to improve data quality. Finally, some insurers are implementing master data management strategies to organize and leverage their data enterprise-wide, supported by the transactional side of the business and the need for business intelligence and analytics.

These developments are cause for optimism — although successfully breaking down the barriers will still take sustained effort and investment. Together, these new capabilities and initiatives have ushered in a new era of analytics. Commercial insurers that capitalize on these new capabilities will be the winners in the next decade. Every insurer should evaluate the current state of their data and analytics capabilities. Investigate big data approaches, but don't forget about more traditional business intelligence such as executive dashboards and ad-hoc analysis tools. Consider how to take risk analysis to the next level while capitalizing on the expertise of actuaries and underwriters and providing them with a more sophisticated tool set. Extend analytics beyond risk into the areas of customer analytics, operational analytics, and financial analytics.

Ultimately, these efforts should not be done independently but should be planned within the overall strategy framework of the company. Insurers that are successful will achieve “management by analytics” and will stay a step or two ahead of the competition.

Mark Breading is a partner at Strategy Meets Action, a Boston-based strategic advisory firm serving both insurance companies and solution providers. He leads the research program at SMA and has exceptional knowledge and experience in all aspects of advanced technologies and solutions that provide value across the insurance enterprise – including data and analytics, customer communications, enterprise content management, and mobile technologies. He can be reached at mbreading@strategymeetsaction.com, or follow him on Twitter at @BreadingSMA.