Big data and predictive analytics, which forecast future outcomes based on past occurrences, allow companies to examine large stores of data and uncover patterns that can be used to gain a competitive advantage. Long-used in the financial services industry by banks, credit card companies, investors, and even property and casualty insurers, big data has only recently gained traction in the life and annuity industry.

For life insurers, predictive analytics provide valuable insights into areas such as consumer behavior, life expectancy, and investment risks, and ultimately inform everything from marketing and product development to underwriting and claims assessment. Analytics enable insurers to more accurately acquire and retain customers, predict lapses, and root out fraud. As a result, big data’s popularity in the industry has skyrocketed: while very few life insurance companies reported using big data just a few years ago, according to recent reports, 90 percent now use predictive analytics to implement streamlined processes, increase sales, reduce costs, and generally improve their businesses.

While big data can provide a competitive edge in the insurance market, it is a double-edged sword, as regulators are also increasingly using such techniques. In laying out its 2015 regulatory priorities, FINRA stated that data mining and predictive analytics are being used to identify risks posed by particular individuals and businesses, with this information then used to make faster and more targeted determinations about examinations and enforcement.

Similarly, the SEC announced that 2015 will bring augmented use of big data analytics to identify potential compliance issues and illegal activity. Like FINRA, the SEC will use analytics to examine the activities of registrants and companies and to target its examinations and investigations. Thus, life insurers must be astute as to both sides of the big data coin because big data will only continue to become a bigger deal in the industry.