Five Myths About Predictive Analytics You Can’t Afford to Believe

April 23, 2013 by Wade Bontrager and Christopher Tait

Predictive analytics has a relatively long history in the P/C insurance industry. From the early 1990s, when early adopters began to use predictive modeling for rating and underwriting, to more widespread uses across organizations today, predictive analytics is a powerful tool for market advantage. Executive Summary Advances in technology, as well as new data sources and implementation capabilities, are changing the game for P/C carriers, but optimal use is difficult for those clinging to five common myths reviewed by the authors from Milliman and EagleEye Analytics.

Executive Summary

Advances in technology, as well as new data sources and implementation capabilities, are changing the game for P/C carriers, but optimal use is difficult for those clinging to five common myths reviewed by the authors from Milliman and EagleEye Analytics.

For some carriers, however, optimal use of predictive analytics has been difficult to achieve because they’re hanging on to misconceptions about the methodology and capabilities. Let’s examine five common myths and review some examples that show why they are not true:

Myth #1: Only large carriers can utilize predictive analytics effectively.

Those who believe that predictive analytics work only for large companies generally make one or more of the following arguments: