By Nelson Lee, CEO and Founder of iLIFE, a next-generation disruptive portal to help consumers vet policies for asset protection and insurance.
Data analytics are increasingly becoming an integral part of our lives: the athletes we watch are analyzed by hundreds of new advanced metrics, smart energy usage is now adaptive to user consumption patterns and wealth management companies are using big-data to see future trends before they happen. There is also one unexpected, yet important use we have come to discover: data analytics’ ability to find hidden gems in life insurance cash values.
Many people may be unfamiliar with the concept of life insurance cash values, so here’s a quick overview – some types of permanent life insurance policies that do not expire will accumulate investment values much like an asset. This investment value that users can withdraw and use anytime like cash is called the cash value.
Now, there are more than 700 insurers in the United States and each insurer has over 20 different products, so sorting through hundreds of millions of data sets becomes a necessary endeavor in the quest to find the policy that generates the absolute best returns. That’s where modern data analytics techniques, such as big-data, cloud-computing and machine learning, can be utilized to sort through billions of data sets quicker than a human ever will. Using this method, an investor can quickly find a policy that generates the best returns for the given parameters, enabling them to compare millions of options quickly through technology, while a human agent may likely just be comparing two products, if even that. In simpler terms, if you only had the chance to look at three products out of 300 million, the best option might only produce 5% returns, but if you get to compare 30 million products, the best product will likely produce a much higher return.
In the hopefully not-so-far-away future where this approach is widely adopted, what we will see is a natural selection process where clients and their capital will disproportionately gravitate only toward the best performing products. The worst performing products will be eliminated, as they will rarely produce favorable mathematical outcomes, and therefore attract much less uneducated or misled capital. This trend will eventually create a higher performing insurance industry on average, as top performers continue to get rewarded and poor performers continue to be eliminated. Through data analytics, consumers will have an industry that’ll be harder to “go wrong,” and “hidden gems” finally are no longer “hidden.”
