While the number of usage-based insurance (UBI) policies reached 14 million at the end of September 2016, most insurance companies are still overwhelmed by the challenge of using collected data to rate their customers’ driving habits.
This conclusion is based on analyzing the world’s 27 largest UBI programs, including those of Admiral, Allianz, Allstate, AXA, Generali, Desjardins, Direct Line, State Farm, the Hartford, Unipol, Uniqa and Zurich.
See also: Why Exactly Does Big Data Matter?
Progressive, the No. 1 telematics insurer globally, still uses a temporary device and does not collect GPS data. Unipol, the No. 2 player, still only collects mileage data from its customers.
We believe, however, that the prehistoric age of connected insurance analytics is ending. The era was based on the premise that all policyholders are reluctant to be “tracked." But with most of us giving daily credit card, fingerprint, driving speed or location details to companies such as Apple, BMW or Vodafone, how to make sense of the self-censorship that insurers apply to their programs?
The truth is that more data benefits insurance companies… and the careful drivers! At the center of this change is advanced data analytics – the ability to extract insights from real-time data sources and discover risk-predictive patterns.
Our analysis, detailed in the Connected Insurance Analytics report, shows that the glaciation period’s ice is melting and that all the key insurers are now moving.
See also: Data Science: Methods Matter (Part 3)
Progressive started a vast recruitment plan to attract data scientists. Generali also made a strong move by acquiring MyDrive, an analytics provider with early footsteps in smartphone UBI. Allstate just created Arity, which will collect data on drivers and sell analytics products to third parties. Simultaneously, Unipol created Alpha, a self-standing analytics and telematics operation.
The bulk of insurance companies is yet to act. To help them adapt to this new climate, Ptolemus published the Connected Insurance Analytics (CIA) report as a step-by-step guide to advanced analytics. It describes, analyzes and illustrates the process by which advanced analytics companies take raw driving data and transform it into real-time, individual risk profiles.
The investigation shows that acceleration, braking and mileage are the most used -- unsurprisingly -- but also that the range of factors is much wider and illustrates the complexity involved in selecting the correct criteria.
To offer a predictive driving score, the report demonstrates that insurers must gain a deep understanding of driving conditions. Adding contextual data, such as road type or relative speed, is a necessary step to price customers fairly.
The full article from which this is extracted is available here.
The investigation shows that acceleration, braking and mileage are the most used -- unsurprisingly -- but also that the range of factors is much wider and illustrates the complexity involved in selecting the correct criteria.
To offer a predictive driving score, the report demonstrates that insurers must gain a deep understanding of driving conditions. Adding contextual data, such as road type or relative speed, is a necessary step to price customers fairly.
The full article from which this is extracted is available here.
