In previous parts of this series, increased usage of algorithmic underwriting in complex risks and prevalence of integrated workbenches in life insurance were discussed, as ways in which traditional underwriting was modernizing. In this final part, the use of third party, alternative data in auto insurance underwriting is the focus.
A recent Deloitte study found, 90% respondents in insurance struggle to find value in data they access. Though its foundation is tightly linked to data, the insurance industry still relies predominantly on the same data points they used decades ago – claims histories, credit ratings, customer demographics and general business information – to underwrite risk. Hundreds of data sets are available to insurers, but only some show strong promise, such as IoT data, new forms of open source and social media data. Among the most common applications of IoT are telematics that provide insurers an opportunity to leverage data generated by vehicles on the roads.
According to IHS Markit, approximately 70% of new light vehicles produced in 2023 will be equipped with some form of telematics systems, with connected devices providing information on driver behavior, such as harsh braking or rapid acceleration. According to IoT analyst firm Berg Insight, the number of insurance telematics policies in force in Europe grew at a CAGR of 28.2 percent, while in North America, the CAGR was 29.6 percent.
To unlock meaningful insights, insurers combine this data with internal data, which is often in siloes. Auto insurance has already benefited over the past few years from availability of real-time motor vehicle reports, CLUE reports and credit scores. Individually, these data elements might provide limited insight into a driver’s risk profile, but together they lay the foundation for a data-driven future. By consuming emerging sources of data, personal auto insurance has arguably been able to outperform other insurance lines by bolstering key customer interaction points.
We now see players like Verisk, a leading data analytics provider, delivering telematics data from its Data Exchange, enabling auto insurers to provide usage-based insurance (UBI) to connected car drivers. It uses traditional and advanced underwriting attributes from LightSpeed Auto to help insurers streamline the UBI buying experience without upfront driving observation period, by calculating discounts at point of quote.
Verisk returns prefilled data on drivers, vehicles, coverages, licensing, violations and losses from LightSpeed Auto while scoring raw data for thoroughness and fraud. Carriers access driving behavior data from the Verisk Data Exchange, which contains normalized driving behavior attributes from consenting drivers of over 7 million connected vehicles.
Some are still skeptical of the ROI of auto telematics. Are costs to collate and digest the data justifiable against value received? What benefits accrue from underwriting decisions based on telematics versus traditional proxy data? Ongoing and future research will keep illuminating this discussion, though strides made by usage-based pioneers, make this a foregone conclusion. As Musk claims, “Ultimately, (we want) to be able to use the data that’s captured in the car … to be able to assess correlations and probabilities of crash and .. assess a premium on a monthly basis.”
The nature of risk keeps changing. Underwriters have been adapting well to evolving risks. The lines are overlapping between personal and commercial auto insurance, as are boundaries between workers’ compensation and homeowners’ coverage, with millions working from home. Insurers are gearing up to provide comprehensive solutions beyond risk transfer. Amid these tectonic shifts, forward-thinking underwriters are staying ahead of the curve to ensure they are not rendered obsolete. They are thriving in an environment that challenges them to move from hindsight, with post-facto underwriting decisions, to foresight, with active portfolio monitoring. As the future unfolds, historical data will be relentlessly subsumed with new, alternative data to underwrite evolving risks.
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