As Yogi Berra, American baseball legend, once said, “You can observe a lot by just watching.” There has been a lot to observe in the world of data privacy and protection recently. Advances in artificial intelligence and mobile computing promise to automate repetitive error-prone claims and underwriting tasks, from sifting through physician notes to scrutinizing heart traces. At the same time, insurers can better access masses of data to develop more granular assessments of risk.
Impressive, yes. But also concerning. Consumer advocacy groups have begun to raise the alarm about data misuse and theft. Indeed, according to Forbes magazine, the average data theft in 2018 cost $7.91 million in the U.S. alone in [legal fees, consumer reimbursements and other losses], the highest annual amount on record. Regulators have taken notice. The European Union’s proposal of the General Data Protection Regulation (GDPR) in 2012 spurred a spate of similar policies across the globe.
As sports analogies go, baseball seems oddly appropriate to describe the shifting state of data protection in insurance today. In baseball, the defense is given control of the ball, and the offense must decide whether to swing or let an opportunity pass by. Similarly, while insurers today have good reason for caution, many also have access to invaluable data and the knowledge to protect it. How can insurers both address data privacy concerns and capitalize on competitive opportunities?
If you’re a financial professional in the life insurance market, the health insurance market, the annuity market, or related markets, this topic is of keen importance to you, because insurers’ thinking about this will shape how underwriting programs work, and how much the coverage costs.
Perhaps the lessons every insurer, broker, or financial services professional needs to learn can be found on a baseball field.
Going on Offense: Data Analytics in Risk Assessment
The question is urgent as carriers face mounting volumes of data. Basic demographic details, such as age, gender, education, and occupation, are only a small percentage of the possible insights now available. Data scientists can now build highly accurate predictive models based on behavioral, socioeconomic, and biometric information.
Collected together, data from pharmacy, wellness, motor vehicle, and credit sources can help carriers construct a far more complete profile of each applicant or claimant. The possibilities seem endless. New rating factors can enable underwriters to better segment risk and protect against anti-selection, nondisclosure, and fraud. Post-sale, carriers can perform more accurate and comprehensive multivariate experience analysis to support better in-force management and uncover untapped distribution, cross-selling, and upselling opportunities. And, at claims time, more insight can deliver greater accuracy in adjudication.