Predictive analytics is a valuable tool and is being applied to a growing number of areas in auto  insurance operations. In recent months, I have been asked questions about the benefit of applying  predictive analytics to auto physical damage data and medical data to identify questionable injuries. To be sure, predictive analytics has shown benefits in claims operations by improving fraud referrals, identifying subrogation opportunities and “right tracking” claim assignments. Consequently, on the surface, the approach sounds appealing. However, a closer look into what predictive analytics can offer and the constitution of the data employed reveals a problematic landscape.

In the book “Competing on Analytics” authored by Tom Davenport and Jeanne Harris, analytics is described as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models and fact-based management to drive decisions and actions”.

In the deployment of analytics, most will concur that the usefulness of results will depend greatly on the quality of the data, the appropriateness of the data analysis and the quality of assumptions employed. It is also important to note that when models are properly deployed, they don’t give answers; they yield information about a tighter distribution on possible outcomes.

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