Few industries are more data-heavy than insurance. They then use complex algorithms to determine the parties’ risk and generate competitive premiums.
Unfortunately, says SkyTree founder Dr. Alexander Gray, these algorithms are currently “rudimentary at best.” This shortcoming is even more glaring now that it’s possible to collect and organize incredible amounts of data in near-instantaneous timeframes. Put another way, the old-school insurance model isn’t keeping up with the changing times.
That’s where machine learning comes in. According to Dr. Gray, “machine learning in insurance industry is the modern science of finding patterns in…data in an automated manner using sophisticated methods and algorithms.” Basically, it’s “big data” on a smart scale.
Machine learning in insurance industry — even once-sleepy niches, such as insurance. Here’s what it means for your current and future insurance policies — and your bottom line.
Companies like EagleEye Analytics have harnessed the power of machine learning to dramatically improve the underwriting process. Using huge amounts of data from past claims and like policies, EagleEye’s system makes snap, ever-improving determinations as to the appropriate scope and value of policies and premiums issued by client underwriters.
Machine learning systems scan troves of policyholder and claim data to spot suspicious patterns and uncover potential instances of fraud before they threaten insurers’ bottom lines. This reduces risk for insurers and indirectly lowers costs for policyholders by bleeding risk out of the premium model and tightening insurers’ loss reserves.
Machine learning helps insurers deliver the right products to the right policyholders, every time. Automated compliance controls monitor and analyze interactions between insurance agents, policyholders, claims adjusters and/or customer service personnel, ensuring that nothing falls through the cracks.
Machine learning isn’t just for “hard” disciplines like underwriting and compliance. It’s also a critical innovation engine for customer service and workflow management processes. For instance, machine learning systems can anticipate periods of peak service demand, facilitating proactive resource allocation that heads bottlenecks off at the pass.
Faster Claims Processing
The holy grail of most insurance niches: faster claims processing. Tech-savvy auto insurers like Esurance are already using data-driven systems to reduce processing times and boost accuracy. Look for home, life and specialty insurers to follow suit soon.
Which Shoe Is Next to Drop?
Machine Learning in insurance industry is staring down the barrel of an…interesting…future. Even as machine learning promises to improve the underwriting process and wring out new efficiencies that last century’s insurance executives could scarcely have dreamed, new threats, opportunities and complications look poised to keep today’s crop of insurance leaders on their toes.
From self-driving cars that turn the concept of liability on its head to new medical treatments that could dramatically lengthen human lifespans, there’s going to be plenty to untangle in the years and decades ahead. Whether that’s good or bad news for rank and file consumers of insurance is still very much an open question.