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How will Predictive Modelling Impact Insurance? Actuaries and Practitioners Speak!

According to some experts, predictive modeling, using increasingly sophisticated tools, is poised to alter how the industry rates and underwrites.  There’s some controversy among insurance practitioners as to whether predictive modelling will effect real change to traditional practices.    Let us know what you think.

A recent Insurance Journal report on the Casualty Actuarial Society Spring Meeting focused on a presentation on predictive analytics and raised some of the opportunities and implications.   Claudine Modlin, a senior consultant at Towers Watson noted that in regards the use of predictive analytics, “As an industry, we have really learned a lot.”   But these leanings have not led immediately to wide-spread adoption.

The best example is the use of credit scores as a predictor of loss frequency.  This is common place in the US, but is controversial in Canada, where, as a result of strong lobbying from insurance brokers and others, the Ontario government banned its use for automobile insurance underwriting and is considering extending the ban to homeowners insurance underwriting as well.

As the Journal article points out, however, credit scores are only one predictor.  Actuaries and other data modelers are continuing analysis to find others.  Utilizing the ever increasing computing power, the Journal reports that carriers are able to investigate literally thousands of variables “including such things as what other policies an insured has, whether they pay their bills on time, and various characteristics of the area in which the risk is located.”

And this is only beginning.  One area that is lagging is linking customer demand into the equations.  As the Journal reports, “Predictive modeling could also help marketing by researching what mix of social media grows the customer base or what brand attributes drive new business. The concept isn’t new to marketers, but the actuarial skill set can enhance understanding of the work.”

As with credit scores, not everyone in the industry is positive about the developments. The publication of the article resulted in a number of comments from readers.  For example, in response to suggestions that use of modelling to improve the precision of pricing would create issues for independent distributors, one producer wrote:

“If all you do is sell on price,,, then yes, this is difficult to manage on the retail level. However if the agent themselves adds value, and has a ‘coaching’ relationship with their customer, then price changes should not make or break the deal.”

An underwriter suggests that use of predictive modeling is just the latest example of the natural cycles of the market place:

“Because of commodification of the market, consumers have been taught to look at the lowest price. Some agents make it worse by selling based upon price alone, which justifies that behavior in the consumer’s mind. This focus on price results in the increaing(sic) need for carriers to create very sophistocated (sic) pricing models that attract their niche customer to yield the best profit.”

Finally, one commentator says that the logical conclusion is a challenge to a fundamental principle of insurance:

“The greater concern is if we can predict risk and charge accordingly, when does it cease to be insurance because there is not longer a spreading of risk, only identyfying (sic) and excluding it or making each insured pay for all their risk!”

So what do you think?  Are the new techniques opportunities for more precision and sophistication, or just new packaging to confuse practitioners and customers?  Will predictive modelling techniques become common practice in all insurance disciplines, or be narrow in scope?  Your opinion counts!