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Predictive Analytics for Insurance - Part 1: What’s Truly New for the Industry?

Big Data Noise

There’s a lot of noise and nonsense about so-called Big Data, especially about its role in the new and exciting field of “predictive analytics.” But insurance has always been about predictive analytics.  What are actuarial tables, loss history analysis, and pricing/risk algorithms if not “predictive”?   

Moreover, these approaches also have always required data analysis along with judgment and expertise of underwriters and actuarial experts in order to accurately assess and price risk, and to balance it with the risk appetite and business objectives of the organization.  In a way, almost every decision we make in business is “predictive.”  The planning process is predictive.  Making resource allocations is predictive.  We have an expectation about an outcome when making business plans and setting organizational objectives.

What is Truly New?

Thanks to Big Data, predictive analytics now can be applied to a much wider range of processes in the enterprise, including those that have traditionally relied on human judgment and expertise. We now have more means and mechanisms to collect and measure the results of our decisions, and test the collective application of business strategy and performance.  Of course every new tool that helps with this process also adds to the arms race of new applications that create capabilities, more data produced by those applications, new tools to manage the output and as a result more complexity to manage.

Customer Retention and Acquisition

Take the world of customer acquisition and retention.  Until recently, only a few tools needed to be mastered in order to manage customer processes.  Agents and call centers were the primary points of interaction, and call center and agent systems supported those processes. Then the Internet became a point of contact, starting with simple web sites offering virtual brochures.  Over a short time, the website became increasingly complex windows into customer interactions, transactions, self-service, provisioning, claims processing, and overall experience.

Hundreds of Tools Available

Now, hundreds of tools are available just for customer communications and marketing processes.  Each of these tools yield data that can be mined to surface patterns of behavior that can yield enormous payoffs, if the organization responds correctly.  Conversely, not making use of these tools appropriately can cause loss of brand equity and market share with astonishing speed.  The difference is in understanding the “digital body language” of the customer.  Responding to that data requires the ability to understand customer data – the attributes, needs, characteristics, life stage, behavior, demographics, psychographics – and then responding to those details in the right combinations with information that will cause them to act in the way that satisfies their needs and meets the business objectives of the organization. 

Applications for Predictive Data

This is a new way to view the work that insurance companies have operationalized – understanding risks and predicting outcomes – in terms of additional dimensions of the customer interaction.  This approach can be applied to loyalty and retention, to pricing, marketing segmentation, conversion, and to quality measures across the spectrum of internal process performance.  Predictive data can also be applied to collaboration, knowledge processes, and even search (a search result is merely a prediction of what someone needs based on limited clues of keywords and perhaps some knowledge of their context).

Overwhelming Choices

It’s easy to be overwhelmed with the range of technologies, the number of processes, sources and complexity of data, rapidity of change and velocity of data (the industry standard characteristics of volume, velocity and variety comprising the “Big Data” definition). However relatively straightforward ways of understanding choices and measuring outcomes are now available.   One place to start is with the desired end state or target process; however, that approach always needs to be aligned with the larger goals of the business area and strategy of the organization.  Data analytics is evolving and maturing, and the tools and capabilities are available to provide the competitive advantage to organizations ready to methodically understand, apply, and leverage the underlying data.

Testing and Performance

Organizations have always struggled to deal with the deluge of data and ability to accurately link interventions and programs and measure outcomes in order to optimize spend.  But we now have more means and mechanisms to collect and measure the results of our decisions, and test the collective application of business strategy and performance.

Go to part 2 - Read: Predictive Analytics for Insurance Part 2: Classes of Application and Tools for Competitive Advantage

Seth Earley
Seth Earley
Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.

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