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Analytics On the Cloud

The use of predictive modeling in commercial lines has become table stakes for most insurance companies.

By Jim Kinzie, Kevin M. Bingham | Updated on December 31, 2010
Over the past decade, the use of predictive modeling in commercial lines has gone from a powerful tool leveraged by a few strategic early adopters, to table stakes for most insurance companies. The industry has seen a tremendous evolution in the use of advanced analytics and predictive models in the underwriting process. On the claim predictive modeling side, we appear to be moving rapidly out of the early adopter stage, partially because of the advent of Software as a Service (SaaS) and cloud computing.

Unlike the early days of underwriting predictive models, claim predictive models can now leverage the cloud as a way of bringing analytics to life faster for insurance companies and self-insured entities that don’t have the large scale or resources of some bigger companies. As a result of the changes in technology, many claim organizations are wrestling with the “how” and “when” elements of the deployment equation, versus the historical “if” side.

The Evolution of Claim Predictive Modeling

Deloitte Consulting LLP has been engaged with the development of advanced analytics capabilities for more than 15 years, beginning with insurance underwriting, and moving on to claim management in 2006. Deloitte’s advanced analytics efforts in claims started with the workers’ compensation line of business, developing separate first notice of loss (FNOL) models and subsequent monthly models for lost time indemnity, lost time medical, and medical only claims.

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