Predictive Analytics: Commercial Underwriting

Extracted 15NOV2011 from

...commercial insurers today are typically still relying on rule- and grid-based approaches to determine when to offer customers complementary products—rather than providing the underwriter with similar, customer-driven insight options to review.

This type of analysis is especially useful with today’s more complex risks that cross traditional boundaries.

For example, would the traditional industry cross-sell model catch the full range of coverage needs for a midrange manufacturer that also sells over the Internet, has periodic warehouse sales and forwards freight overseas? A customer-driven model would...

There are three key elements to successful use of analytics in these circumstances:

  1. Data quality: Information on properties, vehicle fleets and similar, large capital investments must be centralized, cleansed and conformed.
  2. Analytics capabilities: The use of analytics needs to be extended beyond the boundaries of actuaries with additional investments in tools, training, processes and people to turn data analysis into actionable business insights.
  3. Presentation capabilities: Once data is captured, assembled and analyzed, it must be presented in the right format, in the right place and at the right time, to help the underwriter make the right decisions in terms of risk and pricing.

With these elements in place, insurers can put advanced analytics to work in commercial underwriting.