of powerful market forces and competitive imperatives have driven insurers across
the industry to embrace advanced analytics. There is broad consensus that
carriers need to adopt and expand analytics programs to improve insight into the
business and decision making in everything from product design, marketing
and distribution to underwriting and claims As insurance companies
address key strategic operational and technical issues, they will need to
embrace a holistic approach to developing their analytic capabilities. This will
require fundamental and coordinated change across every phase of the project life cycle.
This paper outlines the key elements of the EY methodology by raising a
series of critical questions insurers must ask to generate full value from their
analytics project. We provide a road map to help them prepare, design,
implement and monitor activities that will generate the most from their investment.
The use of advanced data mining techniques to improve decision making has already taken root in property and casualty insurance as well as in many other industries However, the application of such techniques for more objective, consistent and optimal decision making in the life insurance industry is still in a nascent stage. This article will describe ways data mining and multivariate analytics techniques can be used to improve decision making processes in such functions as life insurance underwriting and marketing, resulting in more profitable and efficient operations. Case studies will illustrate the general processes that can be used to implement predictive modeling in life insurance underwriting and marketing. These case studies will also demonstrate the segmentation power of predictive modeling and resulting business benefits.
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