Predictive modeling certainly has advantages. Insurers can use it to streamline the underwriting process so underwriters only review exceptional cases, rather than every single application. Claims departments can use predictive modeling to detect otherwise undetectable patterns of fraud.
Yet, aspects of predictive modeling can be concerning. For example, no one seems to be able to explain the relationship between credit score and the number of claims filed under auto policies, yet there is a relationship between a lower credit score and a higher frequency of auto claims filed. So, hypothetically, my credit score may fall in 2009 because my 401(k) plan has tanked and my employer has cut back on my hours, thus making it difficult for me to pay all of my bills in a timely manner. Though my driving record would remain unblemished (no speeding tickets, no accidents), predictive analytics would cast me into the pool of people most likely to make a claim. I fail to see the relationship between the falling value of my 401(k) plan and my driving, unless, of course, I am driving in tears after having read my latest financial statement.
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