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Life insurance companies worldwide have a big data problem. While other sectors of the insurance industry, like P&C and health, are embracing the benefits of data analysis, artificial intelligence and new technology, many life insurance companies are still lagging far behind.
For the life insurance industry to evolve, the effective utilization of big data is crucial, according to Dror Katzav, CEO of Atidot, a provider of big data and predictive analytics tools for the life insurance industry.
Atidot is a cloud-based platform tailored specifically to the needs of the life insurance industry, which enables life insurers to make data-driven business decisions, while simultaneously enhancing their services to cater to the unique requirements of their customers.
“Probably the biggest challenge life insurers are facing is how to work with and monetize the tremendous amounts of data they collect,” Katzav told Insurance Business. “Many companies we talk to are saying 60-80% of their data is not accessible for decision making.
“Ideally, what they need to do is augment their data with external sources who can run predictive models to help them understand the consumer, and then leverage that predictive analysis to make business decisions. Those who fail to do that are facing an economy where maybe 40-60% of their book of business is not breaking even. The money they spend on things like acquisitions, underwriting and administration is not being earned back in premiums because customers are churning and lapsing before they get to profitability.”
Legacy systems and methodology are two key barriers preventing life insurers from evolving into fresh digital age carriers. Some are dragging along legacy technology that’s 50 years old and does not have the ability to produce the high-quality data insights that consumers expect today.
For example, consider a life insurance company with a book of business of 100,000 policyholders. The question is: who among this customer base needs to increase their coverage? Legacy technology might group the policyholders by age and gender, whereas big data analytics can provide insights that are much more fine-tuned around things like address and employment changes.
“Predictive analytics and big data enables much better lead generation and provides companies with a great trigger point for conversation with a consumer,” said Katzav. “Customer satisfaction is becoming more and more relevant. Our task is to help companies monetize and use their data to improve their insurance solutions, distribution methodologies and customer services.
