Question: How effective are insurers at sorting through mounds of information and turning that allow them to make insightful decisions?

Overall, the insurance industry lags considerably behind other financial services in sorting through the mounds of information they possess to make insightful decisions. The industry should receive an “A” for aspiration, finally embracing the value of analytics and beginning to invest significant resources to the process. At the same time the industry should receive no more than a “C” for how effectively it has integrated analytics into daily processes around underwriting, claims and customer service.

Of course this evaluation varies significantly when looking at specific insurers, lines of business and business functions. Some major insurers have been investing heavily in staff and outside consulting services to begin mining data and enriching it with external information. Certainly the greatest progress has occurred in fraud investigation, catastrophe modeling, consumer marketing segmentation, personal insurance underwriting and high frequency claims functions. Very little progress has been made in using advanced analytics for larger commercial insurance and customer service support.

So what have been the major constraints to achieving greater effectiveness to this point? It most definitely is not the lack of available analytics software and the skilled resources to provide support to making insightful decisions. Tools from the robust suites available from firms like SAS and other software developers to the services of major consulting firms like Accenture and IBM are readily available along with packaged services available to insurers.

In addition to internal staff, firms can use skilled low cost off-shore analytics resources. Massive amounts of data can be downloaded onto portable drives that fit in a small briefcase. The primary constraints to faster adoption of analytics into insurance operations are internal and in some ways unique to the industry.

1. Regulatory issues: All analytics must be developed through the prism of regulatory compliance. Many researchers came from other industries with far fewer constraints on how insightful information can be used and thus initially reached some dead ends that negatively influenced executive acceptance. Valuable opportunities have been lost when the analysis did not consider from the outset how the insight could be implemented on a state-by-state basis.

2. Craft culture: The industry grew from a craft learning model where knowledge came from training by an expert and experience, which still remains to a large extent. That philosophy can undermine the acceptance of analytics support in the field, especially when staff professionals interpret what the executive says about analytics being useful, but judgment is most important. Behavior modification has proven difficult even when available to the “expert”.

3. Systems priorities: Many times insightful research cannot be implemented due to higher priority system requirements, suffering from less certain cost/benefit comparison to some more brick and mortar process improvements. The most effective application of these insights require complex development of near real-time support for decision making by the underwriter, claims specialist or service representative.

The industry is on the road to using advanced analytics for insightful decisions with exciting potential, but still has significant constraints to overcome in achieving highly actionable results.

Frank Cacchione is CEO of TNC Management Group, specializing in applying advanced business analytics, systems development, and project management support for the insurance industry. He has had a wide range of consulting and senior management experience in the insurance and reinsurance industry. He has consulted with Insurance organizations in North America, Europe, Latin America and theMiddle Eastas a Partner with Tillinghast/TPF&C, PA Consulting Group and AMRE Consultants.

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