Sign up for our newsletter and get the latest big data news and analysis.

Is AI the Future of the Insurance Industry?

Anyone who has ever experienced an accident or trauma will tell you that insurance is essential to survival. It can provide a safety net for your family or business; help you get back on your feet and protect you from overwhelming bills. Yet, the very companies that we depend on to be there for us when things go wrong are basing their policies and products on outdated, incorrect data which not only costs them money but could also damage their consumers in the long run. And while other businesses rely on thousands of data points to make every decision, the insurance industry continues to use demographic information that is more than 40 years old. Industry leaders understand that this is a problem. According to a recent McKinsey Report, nine out of ten insurance companies identified legacy software and infrastructure as barriers of digitization and respondents to a Center for the Study of Financial Innovation survey ranked outdated technology as the biggest threat to the insurance business.

It doesn’t have to be business as usual for the insurance industry, artificial intelligence can process more than 4,000 data points in minutes and analyze 20 years’ worth of mortality, demographic, health and government trends resulting in a dynamic algorithm that can be used to make informed decisions. Data Science-as-a-Service (DSaaS) and Insurance-as-a-Platform (IaaP) platforms can help insurance companies more precisely determine risk, create better, more appropriate products for their clients and improve customer experiences. This enhanced data can also help insurance companies price products more accurately, be able to understand the demographics of who is ready to buy and get the right risk on their books—making them more profitable in the end.

These data streams can also help insurance companies create better policies and products that meet the needs of their current and potential clients. Traditionally, the industry has developed new products based off of ones that already exist, instead of truly examining what works and what adaptations are needed. AI and DSaaS platforms can give carriers the tools to develop products based on demographic and risk data. Industry leaders can then incorporate agent input to curate and design products that meet policyholders’ needs.  

In fact, while correct and up-to-date use of technological advances can be a big boon to a company’s bottom line, agents are still necessary to build trust and to be the bridge between big data intelligence and developing a personal connection. The agents interacting with the consumers are the ones who build relationships with clients and who can, based off the knowledge those relationships give them, help curate suggested data driven products to make insurance offerings more dynamic and desirable.

While many consumers turn to digital channels to explore and investigate their insurance options and want the option to file and follow claims online, they still want to know that there is a knowledgeable, caring person they can turn to when they need personal help. The companies that are able to successfully merge cloud-based data driven artificial intelligence technology with the traditional methods of engagement through their agents are the ones who will rise to the top.

About the Author

Paul Ford co-founded TRAFFK in 2015 and serves as its Chief Executive Officer. He also serves as an Advisory Board Member to HSBlox, Inc., in the blockchain industry. With an eye on AI and predictive analytics, using his entrepreneurial spirit he previously founded Qloud Health in 2014 and OrchestraRX in 2015. His experience as Senior Underwriter for Aetna in Walnut Creek, CA formed a strong base to leverage his skill with AI. Paul is also Managing Partner at DS9 Capital which manages interest in a portfolio of Insurtech companies they operate to scale profitably. He received a B.A. in economics from California State University – East Bay.

Sign up for the free insideBIGDATA newsletter.

Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1

Leave a Comment

*

Resource Links: