A telematic device is just one example of how predictive analytics is changing the auto insurance industry. But there’s more.
Predictive analytics isn’t just a buzzword in the insurance industry. Insurers are actively employing it to better model new products, set rates, and boost revenues by uncovering noteworthy patterns in existing data. While those tendencies may not quite predict actual outcomes, the data indicates what might happen in the future with a high degree of probability, thereby allowing insurers to more accurately forecast risk and lower costly payouts.
The use of predictive analytics has rapidly become standard in the insurance industry. More than 80% of the 269 insurance professionals in the U.S. and Canada surveyed in 2013 by customer analytics provider Earnix and property and casualty insurance information source ISO said that they use predictive analytics in one or more lines of business. The potential to drive profits, reduce risks, increase revenues, and improve operations all prompted insurers to embrace it.
The insurance industry is not the only user of predictive analytics. Worldwide, the predictive analytics software solutions market, which includes data mining and customer insights, is estimated to grow by a compound annual growth rate of 21% from 2016 to 2022 to reach $10.95 billion by 2022, according to Zion Market Research.
Auto Insurance Tops List for Predictive Analytics Usage
According to the Earnix survey, nearly 50% of insurers reported using predictive analytics to support auto insurance modeling and pricing. Next up were homeowners (37%), commercial auto (32%), and commercial property (30%). The current leading example of predictive analytics in auto insurance are telematic devices that hook into a car’s operating systems to monitor a driver’s habits, such as average speed and the times at which they drive most often.
Telematics override the traditional underwriting standards such as the insured’s age and driving history and instead base rates on everyday road behaviors, or what is commonly known as usage-based insurance (UBI). If a policyholder exhibits safe driving habits, he or she stands to receive a reduced premium. Given that a large share of the property and casualty industry’s revenues derive from the highly competitive auto insurance sector, insurers gain an advantage by accurately pricing this product.
By several estimates, UBI and telematics usage will increase in the coming years. SMA Research predicts 70% of all auto insurers will offer telematics by 2020. By the same year, BI Intelligence anticipates that 50 million American drivers will have participated in a UBI insurance program.
More Than Telematics
Telematics, however, isn’t the only outgrowth of predictive analytics that could impact the auto insurance industry. Analyzing auto insurance data has further implications for car insurance:
Customer Acquisition. Data mining can unearth scores of potential customers for particular products, paving the way for more personalized coverage. In addition, auto insurers are able to reasonably predict which policyholders may not renew their policy, thereby giving the insurer a chance to intervene and retain that customer.
Cost Control. By analyzing data, insurers are able to discover the costliest auto insurance claims. Using this information, insurers can take measures to lessen those risks by either changing the negative driving behaviors detected by telematics or by alerting owners when they need to fix a vehicle before the car breaks down and causes an accident.
More Flexible Underwriting. Insurance underwriting is sometimes inflexible, and that could needlessly punish policyholders and create more paperwork for the insurer. For example, insurers typically award good students discounts, but only if they regularly submit their grades. Predictive analytics spots those students who are good risks, and so don’t need to continually send in school reports.
Fraud Detection. Predictive analytics software pinpoints duplicate claims and other irregularities that may indicate fraud that the human eye of an underwriter might miss. Some $80 billion is lost to fraudulent claims each year, according to the Coalition Against Insurance Fraud. With predictive analytics, insurers have a weapon to pare down that number.