Predictive analytics has become a staple tool in many industries over the past few decades. Its versatility in helping businesses make informed decisions has translated well to a wide variety of sectors, including insurance. In fact, big data has had some of the biggest impacts overall on the insurance industry due to the nature of the risk calculations involved with these businesses.
Life insurance companies have always relied on data to make calculated decisions that affect profitability. Data has always been available to some extent, but until the widespread use of big data tools, life insurance as an industry had less to go on and had to take more risks.
All that’s changed. Here’s how predictive analytics are shaping the modern life insurance industry.
First off, what is Predictive Analytics?
Predictive analytics involves looking at large amounts of data to find patterns. The goal of this is to predict when and if those patterns are likely to occur in the future. Although it’s not a foolproof method of determining factors like risk and demand, it can be extremely helpful for insurance companies in strategizing, allocating resources, and setting premiums.
Predictive Analytics and the Insurance Industry
In the insurance industry, predictive analytics has massively improved companies’ profitability. One survey indicated that predictive analytics can help life insurers to cut expenses by 67% and increase sales by 60%. Those numbers are beyond impressive and cannot be ignored.
Not only has predictive analytics helped small, tech-focused insurers grow their market share, but it has given older companies an unprecedented opportunity for growth. Larger insurance companies that do not modernize risk losing their competitive edge to smaller firms.
The Benefits of using Predictive Analytics for Life Insurance Companies
So, how are life insurance companies getting so much value out of predictive analytics? There are several benefits in play that affect overall profitability. In a 2018 industry survey from Willis Towers Watson Life, there were three main areas that have been affected positively by predictive analytics. They are:
First, a reduction in issue and underwriting expenses. Half of the firms surveyed saw at least some positive impact, while 17% saw a strong impact. Reducing expenses increases profits while still delivering the same benefits to customers.
Second, firms saw a large increase in sales. Predictive analytics allowed life insurance companies to bring in more customers and increase their profits.
Finally, firms noticed an overall increase in their profits. By cutting expenses AND bringing in lots of new business, life insurance firms were able to grow and increase profits by investing in just one powerful tool.
How Life Insurance Underwriters Use Predictive Analytics
So now that we know just how much predictive analytics has transformed the life insurance industry, how are underwriters using it to achieve better profitability? What patterns are they looking for in the data and how does that help them make better decisions?
With increased access to relevant datasets, insurance companies can use predictive analytics to set their prices for premiums. Companies can better assess common risk factors and apply that knowledge to their pricing. They can also get information on mortality and morbidity risk based on highly specific factors, helping to streamline the underwriting process and reduce risks for the insurance company.
Predictive analytics can also be used in assisting with claim management, reducing costs, and streamlining operations.
Challenges of Using Predictive Analytics in Life Insurance
Obviously, many life insurance companies are excited by the opportunities predictive analytics offers. However, there are some challenges involved that must be considered as well.
Even back in 2018, 82% of large life insurance carriers and 50% of small or medium-sized firms were interested in cloud solutions for their data. Data infrastructure has been an ongoing challenge for life insurance carriers interested in growth and gaining a competitive edge with big data.
Another persistent challenge has been privacy. We have seen the impact of cybercrime in the healthcare industry, where data breaches have been a troubling trend that has opened organizations up to large fines, loss of trust, and the theft of sensitive data that cannot be retrieved. The insurance industry must also safeguard sensitive data and use it in ethical ways.
Life Insurance & Big Data: A Process
Change is hard in any industry, and it’s especially difficult in a well-established industry that involves risk management and volatility, like life insurance. This means that many companies haven’t yet begun using predictive analytics and are missing the important benefits big data offers.
Companies are beginning to see just how important predictive analytics is in maintaining a competitive edge, but the industry as a whole hasn’t fully embraced the use of predictive analytics. In the next few years, as this process continues, we’re likely to see more carriers embracing these tools and becoming more successful.