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Decluttering expert Marie Kondo asks a single question when determining whether a piece of clothing or a book should stay in the house or be thrown out: “does the piece spark joy?”

A very similar question can be asked today in the world of insurance. Is new data positively impacting insurers and sparking joy? Or are these just more data points destined to be thrown out in the end?

In general, the impact of new data sources can mean very different things to different insurance stakeholders. Of course, the insurtech movement is all about making an impact. However, the timeline to impact may vary greatly depending on which part of the industry is being targeted. This dichotomy can be seen clearly in the difference between claims and underwriting, and the impact insurtech-generated data has had so far.

In underwriting, there has been an explosion of new data sources over the last several years. While this fountain of new data has the promise to significantly impact the future of underwriting, underwriters often do not have the ability to pull these new data sources into existing processes. Matthew Grant recently wrote about this very problem and the need to create “smart data” for underwriters.

We see this most clearly in the IoT space, where insurtechs have spent the last few years struggling to find a silver-bullet dataset that can tell underwriters something about the risk of a property.

For example, my home IoT sensor may tell me that I leave my garage door open once a week. Does that mean I’m a better or worse risk? Are open garage doors in my underwriting guidelines? The answer is going to be no -- it’s not yet a relevant variable to underwriters and actuaries, because no one has explored whether or not there is a risk signal there.

Alternatively, my auto telematics dongle may report that I hit the brakes harder than average. As an underwriter, do I believe hard braking is a loss signal? Quite possibly! Can I prove this at scale? Not yet. Is hard braking in my underwriting guidelines? Also not yet.

As you can see, in underwriting, the bar for usability is high and the time frame to impact is long – at least a year or two. Therefore, insurtechs must design their data with current underwriting guidelines in mind and focus on providing higher quality data that plugs directly into existing workflows. The success of data in underwriting today is based on easy ingestion, data quality, and predictive power (which you can shortcut by focusing on existing attributes).This does not mean underwriters won’t use new forms of data. In fact, certain insurtechs, like Cape Analytics and ZenDrive, are making major headway in this space. But insurtech vendors need to walk before they run. Over the long term, the ambition should be to evolve the underwriting process with new kinds of loss-predictive data.

In short, data in underwriting is all proving risk signals and building towards long-term impact.

 

...data in underwriting is all proving risk signals and building towards long-term impact.

The claims side of insurtech has had a different experience over the last few years.

New forms of claims data do not need historical oversight – the impact is immediate and obvious. In a car accident, how badly is the bumper banged up? I can take a photo on my smartphone and send it to my agent. What is the condition of my roof after a hurricane? Let’s send a drone up there. New technologies can help answer questions that claims professionals have been asking for decades, more quickly, more cheaply, and more effectively. The problem in claims is far simpler: there aren’t enough human claims adjusters to personally inspect damaged properties – especially after a big event.These new technologies increase the speed and bandwidth of insurers to get information from the field and make a payment. This is crucial: claims payments don’t get smaller the longer they sit outstanding. According to DroneDeploy, “each day a claim is put on hold because of backlogs is another day a company must pay for downtime, like business interruption costs and additional living expenses.” Time is money, and the fast deployment of claims payments allows people to get back on their feet faster.

In claims, while new data helps enable the speed of the claims payout, it’s not perfect yet. Here, the pitfalls come when insurers are not prepared to move as quickly as the data is available. As the WSJ has reported, mistakes in claims are costly -- both for the insurer and, potentially, for the consumer. So good oversight and evolving the underlying processes will be key to claims departments continuing to make progress.

Still, the use of imagery, drones, and smartphones can put insurers in a much better position to positively affect the lives of their customers. Imagine sitting in a hotel room hundreds of miles away from your home, after a major event and mandatory evacuation. If the insurer can give you a sense of what is going on at your property at that time, it would save plenty of heartache and stress. Sharing of information in this way can further align the customer with the insurer and provide truly useful information.

Data in claims is more about making a short-term impact.

So, what does this all mean?

 From my perspective, underwriting-focused insurtechs can maximize change and impact today by improving data quality to fit existing underwriting processes. In underwriting, the workflow involves actuaries who need to prove that losses are minimized due to better underwriting. This requires more data, much more time to account for the entire policy lifecycle, and more buy-in from the customer. It’s also a larger-scale opportunity: underwriting data touch all properties in the portfolio and sets the rest of the business up for success or failure.

Claims, on the other hand, is essentially triage. There is a lot more flexibility as it relates to new data on the claims side. Here, it’s about using new data and more efficient data collection efforts to improve the claims process. It’s both easier to include a new data element and also easier to justify savings in claims. Savings are inherently seen far faster -- either by lowering the average payout or facilitating faster turnaround time.

Over the course of a year or two, though, better underwriting will trickle down into the rest of the insurance workflow, including claims. More efficient underwriting means fewer claims and less costly claims, taking friction out of the system as a whole and leading to happier customers. Insurtechs should understand the tradeoffs inherent in either working with the claims or underwriting divisions of an insurer.

In the short term, claims will see the first wave of transformation. But, in the long term, the impact that new data will have on underwriting will dwarf the claims transformation in a much larger, and more transformative second wave.

About the author: Chris Downer is a principal with XL Innovate, a leading global insurtech venture capital firm, which backs entrepreneurs who are creating companies that can achieve significant scale and market impact. Interested in getting a daily dose of the most important Insurtech news? Sign up for Chris’ newsletter here.

The views expressed above are Chris’ personal views and not necessarily those of XL Innovate or AXA XL.

 
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