Predictive analytics have been used in a variety of industries for a number of years. Perhaps nowhere is the concept of predicting and rating risks more common than in the insurance industry. From zip codes and income to credit scores and driving records, a whole host of data points can be used to accurately gauge risks, identify trends, and set rates.
Certainly there are those who have taken exception to some methodologies, as not all people with bad credit are bad drivers, nor are all people living in lower socio-economic zip codes going to be in an accident or have their vehicles stolen. But this overly simplistic view misses the point: risk is rated upon probabilities, borne out by statistically validated historical facts.