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Introducing Millennials to Reinsurance Data

October 29, 2016 / Reinsurance


In a recent blog, my colleague asked the question “How can you prepare for the retirement talent gap in the insurance industry?”. Retirement is a major industry concern and this is a fundamental question that can’t be ignored. When talking about talent or knowledge gap in life reinsurance one dilemma is understanding who is supposed to fill that gap?  Who will be the next life blood and future of their companies?

The millennial generation seems to be the most obvious fit as they are the newest to the workforce. This generation is commonly defined as digitally driven, technology dependent and often on top of the latest player in the innovation game. (I’m a millennial myself, and I would say I fit the description). Although the reinsurance industry is known for being more traditional, it is definitely at a tipping point of change so it is perfect timing for millennials to get onboard.

As a millennial who has been in the industry for four years, I wanted to share my advice with fellow millennials on two key components of this industry. Data & Treaties. Below I’ll get you acquainted with reinsurance data and in part II, I will help you navigate through treaties.

Data is everywhere

I Googled the word “data” (I’m a millennial, it’s what we do) and the first definition I saw was “facts and statistics collected together for reference or analysis.” This seems simple enough. Everyone has seen data of some kind in their life. It is like the Force (YES, that Force). Obi-Wan said “It’s an energy field created by all living things. It surrounds us and penetrates us; it binds the galaxy together.” Data is no different. Its everywhere. And it is the lifeblood of reinsurance.

How to make sense of reinsurance data?

Patterns are your friend and they help you solve problems. A former manager always told me to watch out for “nonsensical” data. I used to think she was making up a word- but, thanks to Google, I learned it really is a word! I also learned that she was really telling me to understand what data combinations don’t make sense.

For example, if you have policy data where the field for birthday is listed as 1/1/1900 and the age field is listed as 45 – this is conflicting information about the insured. AKA an example of something nonsensical. Another example could be when the reinsurance duration is greater than the policy duration. This shouldn’t happen in data set. Since not all reinsurance systems would flag this kind of ‘nonsensical’ data – it is our responsibility to ensure the integrity of the data.  

What does data look like in reinsurance?

In reinsurance, data is where all of the risk occurs. If you get the data wrong, it can result in major losses and, at the end of the day, affects the bottom line. But what if the data you are working with isn’t in the form you are used to?

One day I came across a co-worker who was looking at what I thought was some kind of microscope (which I had never seen in my life). It looked like it had seen a bit too much time in storage and I could not for the life of me figure out what purpose this would serve at work. It’s 2016, everyone has laptops and is connected to the internet.  When I asked what he was doing, he looked up at me, laughed, and said he was looking at policy records. Policy records with a microscope?! No, it was actually micro film.

I only knew micro film from the movie “The Rock” with Nicolas Cage and Sean Connery.  I didn’t think micro film actually still existed or would be used in the 21st century where there are hard drives the size of my palm that can hold terabytes worth of data. This is definitely an experience I will never forget and always appreciate because it shows just how much historical data the reinsurance industry encompasses. And how much data the people in the industry, like myself and my former colleague looking at the microfilm, need to be accountable for.  While this microfilm is a more traditional form of data, low and behold it is a source of data.

The challenge with traditional forms of data  

Industry wide, reinsurance companies are presented with the challenge that some data and information is actually as old as the people that are written on the policies. That ‘old information’ sometimes isn’t worth transitioning or migrating to new digital mediums because of increasing costs to digitize old technology.  However, what happens when the people who built and understand the more traditional data systems are no longer there? This is why it is so critical for companies to bring in the next generation early – so that they can get as much mentoring and education as possible from those getting ready to retire. It’s also why data standardization is so important.

Moving towards digital

Luckily, more and more groups are moving towards making everything digital. All new, and a good chunk of old, cession or policy data is now on usable platforms. Which means it is not the Wild, Wild West anymore. Almost everything is in code and the majority of the life reinsurance data across the industry is on the TAI platform. This is fantastic because it means there is one source of the truth and for the most part, similar data variables across companies. This is so important in the knowledge growth of the industry.

It allows employees to focus on understanding reinsurance as a whole and not just understand how one group or company does their business.

The best word to describe this is standardization. It allows reinsurance and insurance groups to move seamlessly from group to group, company to company, and system to system.

That being said, for those joining the industry, it is important to immerse yourself in understanding data of all different mediums. Because, as I learned with the microfilm, there are still traditional pieces of data that exist in the industry. In my next blog, you will learn about one of them: treaties.

In the meantime, get ahead of the game 16 Pro Tips to Improve Reinsurance Quality:


Written by
Chris Copelli