Five insurance data topics we all need to obsess about
Article information and share options
One of my favorite things about my job is indulging my inner nerd by diving into new sources and uses of the data our industry relies on.
Insurance was the first data business – we have always collected information on a huge scale. But in the rapidly changing data world, are we making the most of our strength?
Effective use of data is key to reducing protection gaps worldwide. Clearly, more and better data can help us accurately price risks. But data has a key role to play in expanding access to coverage as well. It can change the nature of the underlying risk in ways that make coverage more affordable for all. So, in no particular order, here are my top 5 data topics for insurers seeking to use data to increase customer access:
1. The Internet of Things (IoT)
IoT will impact many industries, and the insurance industry is no exception. Data from the internet of things (IoT) will help close the protection gap because it will change the nature of risk itself: the IoT makes it possible to assess and mitigate risk in real-time.
Insurers are beginning to protect customers long before a claim materializes: the slight drop in pressure that signals a pinhole water leak; the erratic motion of an industrial system that signals an imminent failure; unexpected movement at a vacant property ... each scenario creates an opportunity for “intervention” as a new solution to the risk problem.
Insurers who don't have an IoT strategy will face unhappy customers and higher costs. This is not a forecast. It is happening right now.
2. Moving Beyond Relational Databases
Not sexy, I know, but the technology "debt" crippling many life insurers is often due to the use of slow, unwieldy relational databases. In a relational database, the data must be defined before a record can be stored. This means that changes in the data stored in a small number of records may drive weeks or months of change management. Testing across the entire system can suck up all resources or even trigger outages. It is really tough to be agile and customer-responsive when 90% of your system resources are going to maintenance and testing. Schema-less systems (like MongoDB) allow different records to be stored without necessarily breaking the system. If new data types are added they can be stored and retained without having to modify the underlying database. This can't magically make the new data useful but it can save a ton of time by avoiding back-testing processes that do not use the new data. Schema-less is by no means a panacea, but in a rapidly changing world, admin systems that rely on relational databases are going to be heavy. Switching to more adaptable models can really unlock customer solutions and help close protection gaps.
3. The Changing Speed of Data and Decisions
Data speed is often lost in the big data discussion. In the old days, other than sales figures, most performance metrics were produced monthly or even yearly. System changes and adaptations were slow to identify and recognize customer chokepoints and performance problems. Modern data infrastructures capture data by the millisecond. This leads to faster decisions that make it possible to be much more adaptable and nimble.
Some of the biggest advances in automated and fluidless (sans lab tests) underwriting are really changes in how fast we access and evaluate data; which changes how fast we learn from customers. This creates new opportunities for innovation.
4. Ecosystem Security
This is the big one: how do we protect customer data?
New cyber protection laws have rightly placed substantial responsibilities on carriers to secure the insurance value chain. More secure customer data is of course a good thing, but it also has the potential to increase protection gaps (an unintended bad thing). Unlike the captive systems of the past, today’s carriers are increasingly suppliers to multi-carrier, independent distributors. This means that carriers are working with a host of distributors, each of which sits at the center of a small constellation of carriers and downstream distributors.
Each carrier has to assess the risk of all of these connections. It's manageable for larger carriers, but very expensive for many distributors who must now field questions from every carrier in every line. If this gets costly enough, it will force distributors to limit their panels and may restrict choice and access for consumers.
5. Electronic Medical Records (EMR's)
EMR's are the IoT of life and health. They consolidate data and assessments from various sources. Carriers and regulators already know exactly how to use this data. Swiss Re’s ever-improving Life Guide is the standard of how to convert this data into strong mortality assessments. And companies like Clareto (full disclosure: I co-founded but I have no financial interest) and Life.io are at the cutting edge helping carriers use this data to benefit customers. As an industry, we need to provide market support for EMR access. It has the ability to revolutionize customer experience and increase access to protection like nothing I've seen before.
So, in no particular order, those are my top 5 trends. Each has the potential to affect how people access and secure insurance coverage. Noticeably missing are predictive modeling, AI, and ML. This is not because they are unimportant, but because the other five are prerequisites and foundational.
What's in your top 10? What data topics have your attention? I welcome all of your comments and feedback!