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Predictive Analytics for Insurance Part 2: Classes of Application and Tools for Competitive Advantage

In part one of this two-part series, we discussed what was new about analytics.  The insurance industry has been using analytics in various parts of its core operations throughout its history.  What is different is the range of new applications for dealing with both internal and external processes and the amount and complexity of data that is produced. In part two, we discuss the state of evolution and maturity of various applications for of analytics, including pricing, underwriting, claims management, and marketing.

Information Overload – Bad and Getting Worse

A recent study indicated that more than 25% of customer contact centers are suffering from information overload and that the customer reps are not able to make use of the information they have about the customer. “An even higher percentage of service reps who have access to customer information don't use it to the fullest to support calls,” according to the study, which was conducted by the International Customer Management Institute (ICMI)[1]      The report continues on to say that “Nearly 48% of contact centers collect satisfaction data, but few use this information. About 36% of agents don't collect data around customer satisfaction. Some 51% of call centers do not ask for customers' channel preference, while 32% of contact centers report collecting preferred channel preference information from customers.”

This is an interesting turn of events.  The study reveals numerous issues that all boil down to the dilemma that organizations face when they have the data but don’t know what to do (or do not have the ability)  to make it actionable.  Multiply this scenario by each stage of the business lifecycle and it becomes clear that there is tremendous potential for leveraging the various data sources and streams that are available through increasingly powerful and sophisticated tools.

Pricing and Actuarial Analytics

Pricing is likely the most mature in terms of the ability to optimize according to risk level, customer segment, and appetite. Though still challenged by limitations of legacy systems, fragmented processes, the need for widespread collaboration, siloed applications, and the difficulty in harvesting real-time test pricing data from rejected offers, most insurance organizations recognize the payback that improvements to this core area can provide. Surveys have shown that predictive pricing models have been or will be adopted by the majority of carriers. This is not surprising, as pricing and actuarial functions have always been core to the success of carriers. The increasing sophistication of models and continued integration of new data sources along with faster model evolution with near real time feedback are the game changers.

Underwriting Processes

Of course underwriting and pricing are inextricably linked.  Pricing models allow for development of risk profiles and factors with actuarial data to develop products. Underwriting classifies and quantifies specific customers and opportunities according to attributes identified in actuarial and pricing rules and mechanisms.  Everything from underwriting rule development and applications to performance metrics, mix analysis, market comparisons, exposure management, loss analysis, and segmentation are part of typical baseline analytics.  However, new information and insights are available from an increasing variety of sources ranging from government and public data, to proprietary providers and increasingly social graph information (like Facebook and Linkedin data) and web behavior data.  The ability to rapidly change modeling parameters (along with more complex analytics frameworks and data sources) requires that every enterprise push the envelope with regard to current capabilities.

Claims Management and Knowledge Processes

 

Unstructured claims information is yielding to text analytics approaches as well as business process mining to allow for new efficiencies and effectiveness.  Claims can never be completely automated – some part of the process involves humans.  Whether the requirement is for coding of healthcare procedures using ICD-10 codes or for producing first notice of loss forms, human judgment, unstructured text, and manual processes are part of the picture. Call center and claims processing agents require access to detailed policy content and guidelines that allow for proper claims handling.  Analytics allow for mining of knowledge for online reference applications, self-help, and self-service and even structured knowledge algorithm-driven avatars to increase accuracy. Not to mention the ever expanding competition between perpetrators of fraud and fraud detection approaches – each of these require advanced mechanisms for data and text analytics. 

Marketing Processes and Analytics

Most insurance organizations don’t consider themselves marketers first.  However, marketing automation, customer segmenting, and buyer attribute analysis are just as essential to success as pricing and actuarial analytics.  These need to be part of the toolkit of the enterprise.  The customer dynamic has changed forever, and knowledge of customer lifecycles, buying behaviors, and individual needs (even in business to business contexts) will allow forward looking insurers to stay ahead in this ever shifting competitive, demographic, economic, and technological landscape by leveraging core analytics strengths in new ways. The clock speed of these processes is continually increasing, and picking your unique combination of competitive strengths and optimizing the analytic dimensions will help assure your place in the data driven marketplace.

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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