In a perfect world, insurers would conveniently have at their fingertips data that provides deep insight into each customer’s driving behavior—insight that would drive simple, digestible driver scores that strongly correlate to loss propensity. This information would not only give incremental lift to other traditional data sources (like credit scores and demographics), it would also allow for risk monitoring over time. Carriers and customers alike would benefit from this capability, as it would enable customized underwriting that better protects carriers against potential losses while offering the best premium value for customers. If you’re thinking, “doesn’t usage-based insurance (UBI) already deliver these benefits?” the answer is, “yes and no.”

One of the biggest challenges in widespread UBI adoption has been the expense of collecting the data. Recently, smartphone apps have emerged as the most cost-efficient UBI data collection method—and one that’s proving to be popular with customers as well. Yet while this development has provided a significant boost to UBI cost-efficiency, it’s also presented problems related to data accuracy. For example, depending on the type of smartphone used, data accuracy can vary by as much as 55 percent*.

When it comes to UBI, data quality is king. Without the assurance that all data utilized is accurate, customers will not be willing to participate and insurers will not have the information they need to improve risk management capabilities—and therefore, their bottom line.

Yet even with its drawbacks, smartphone-driven UBI is here to stay. In my next post, I’ll explain how you can ensure the data you receive through these applications meets the standards you and your customers require.

*Based on LexisNexis proprietary data.

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