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Predictive Analytics Provides Insurers Food for Thought

The sophistication within every part of the risk-sharing process—from rate-making to underwriting to claims—has increased at a quickening pace, driven by greater precision and predictability and built on an ever-expanding amount of data.

By Michael P. Voelker | Updated on February 08, 2011

It’s generally accepted that the first written insurance agreement was established with the Hammurabi code. It took quite a while—over 3,000 years—before humankind could establish enough understanding of risk transfer and mathematics and analyze enough data to develop the first actuary tables in the late 1600s. Since then, the sophistication within every part of the risk-sharing process—from rate-making to underwriting to claims—has increased at a quickening pace, driven by greater precision and predictability and built on an ever-expanding amount of data.

But despite the industry’s developments associated with cross-company information sharing—bureau rates, claims databases, and so on—there has long been competitive advantage to be gained by the amount of data an insurer could collect in order to fine-tune rates and underwriting, support new products, and carve out new market niches. It was a model that favored companies with larger books of business generating more data and with more staff resources available for information collection to establish greater certainty around predictability.

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