Big Data is a term that has gained momentum in recent years but the cause of it has been around for much, much longer. Industries have been collecting data for as long as they have been around but in recent years, more and more of it has been streaming in at incredibly fast speeds and volumes due to the facilitated process we know today. Additionally, the type of data coming in is far more different in format: numerical, text, video, email, plus many more. The industries in which we operate, life insurance and reinsurance specifically, we can certainly attest to the fact that big data is on the rise.
So Big Data = extremely large volumes of data which can be analyzed to detect patterns... But how is the data classified, and how does it relate to the life insurance industry?
Some of this data is called structured and some is known as unstructured.
Structured data typically describes data that is formatted and of a defined length, e.g. “strings of data” and I recently read that most experts agree that this accounts for about 20% of the data that is out there. Unstructured data is more about data that does not follow any specific format: images, videos, scientific data, test within documents and emails, social media data, mobile data, among many others.
The reason that the term Big Data came about is that it describes this enormous amount of data. Most companies, however, do not have the systems in place or knowledge on how to properly analyze or use this data, nor do they have the ability to keep up with it. So while having all this information is valuable, it is not of much help if there is no means to analyze it and use the results to improve business operations.
Data Analytics vs Predictive Analytics
Data analytics is used by many industries to make better decisions for their business by analyzing data to find ways to better market their products and improve business processes. Through this analysis, they are able to find patterns that they may not have seen otherwise. The more data that is analyzed, the better their ability to see trends, to see what works for their business and what is not working.
Predictive analytics takes all this data, both historical and current, and by using sophisticated software, attempts to make predictions about the future. This way businesses can use past patterns and trends to look for current risks or future opportunities. Through this, companies can better manage their current businesses plus more accurately develop their future markets, etc. Have a look at TAI Insights, a recently launched software solution and analytics tool by TAI, which provides businesses transparency by giving individuals the power to understand reinsurance portfolio risk.
Among life insurance and reinsurance, predictive analytics is used in actuarial science, marketing, financial services, insurance, retail, travel, pharmaceuticals and many, many, other fields.
One example of predictive analytics is credit scoring where the data from a customer’s credit history, loan application, customer information, etc., is analyzed in order to rank individuals by the likelihood of their ability to make their payments on time.
Why the current buzz words in life insurance underwriting?
Predictive analytics has been around in life insurance for many years but it, along with the term Big Data has really risen to the forefront over the past four to five years. Mark Dion, RGA Reinsurance Company, in his 2011 article in the On The Risk Magazine for underwriters stated that:
“Literature relating to life insurance applications of predictive modeling techniques is relatively sparse compared with other disciplines”.
He also pointed out that “while the techniques do lend themselves to life insurance business, they are not as widely used as in auto or health insurance”. Over the past few years, these terms and the tools used for predictive analytics are becoming more common and hence the prevalence of these buzz words at more and more industry meetings, including the most recent AHOU annual meeting in Orlando.
This was Part I in my series on Big Data and how it relates to Life Insurance. For today, I will leave you with two question: How would you say Big Data is currently being used in life insurance? How will it affect life insurance underwriting in the future? I will answer BOTH of these questions in Part 2 of this series, as well as shed light on how it will affect life underwriters specifically in their day to day work!
What are YOUR predictions? Share them with me in the comments section below!