A roadmap for alternative data in L&H underwriting

The proliferation of mobile devices tracking physical activity metrics has underwriters eager to tap this data for their risk assessment. Wearables data does have the potential to accelerate applications and inspire new products. A new Swiss Re report, "Getting practical with wearables," shows how a careful approach, the right partners and robust testing is key to making the most of alternative data.

We can all envision how a person's exercise routine that they track in real time on their phone, watch or wristband could offer important insights into understanding their overall health status. After all, somebody who works up a regular sweat may seem likely to be on the healthier end of the spectrum and may represent a sound insurance risk.

But there's a catch: physical activity data collected on wearable devices may not align squarely with the information we've traditionally collected to help our industry make good life and health insurance underwriting decisions. Consequently, we need to understand the significance of this new data, interpret it with care, and deploy it wisely to maintain the integrity of our underwriting.

Ultimately, when integrating alternative data into our underwriting in pursuit of goals like reducing sales friction or trimming onboarding costs, we should simultaneously be aiming to preserve or improve the quality of our risk assessments, whilst avoiding costly errors. 

A key message of our new report "Getting practical with wearables" is that when physical activity data is used to augment an existing underwriting journey or to underpin ongoing dynamic underwriting, it can boost accuracy of forecasting mortality rates. Conversely, outcomes may turn out to be less predictable should alternative data be used to replace or substitute existing underwriting information without fully understanding the extent of what is being omitted.

For a real-world example, let's examine one kind of physical activity data – scientists call it "Metabolic Equivalent of Task," or METs – that's been collected in studies assessing the health and nutritional status of US residents. When you examine the numbers, one key observation is the low correlation between measures of physical activity and whether those studied also suffer from a disease.

This means insurers that base underwriting too narrowly on such METs data collected via alternative sources or use it to simply replace information collected via more traditional underwriting sources run the risk of adding people with diseases into the standard underwriting pool, without being priced correctly. This illustrates why reliance on alternative data without carefully considering its limitations can lead to adverse underwriting consequences.

Some InsurTech companies promote a view that using METs alone as a replacement may be as predictive as much of the data we've been collecting for years via traditional underwriting. They maintain that they can create a portfolio that's 95% as predictive as existing underwriting using only physical activity data, age, gender, and just a few other risk factors.

Though this sounds impressive, caution is warranted. That's because even if the frequency of inaccuracy introduced into the risk assessment process seems low, the severity of incorrectly priced risks can be significant.

On the other hand, METs have shown themselves to be an excellent fit for dynamic underwriting. The data is widely available and easy to access. Over the medium to long-term, the predictive power of tracking METs makes it easier to identify, price for, and drive healthy behaviour change which can help to reduce a customer’s risk or prevent their risk from worsening.

Clearly, much depends on how alternative data is deployed. The long tradition of insurers collecting information from people seeking to protect their families from financial hardship has served consumers well for more than two centuries. This practice has ensured that our protection products remain sustainable and appropriately priced for the risks we're taking on.

Of course, the sophisticated data we collect today, often via medical examinations, can add time and cost, and can lead to some applicants abandoning the process prematurely. The hundreds of millions of mobile devices now telling us how far we walk, ride our bicycles, swim, or run – and what our hearts are doing along the way – may help us improve this experience for prospective customers, but we need to do this correctly.

At Swiss Re, we believe partnerships that pair a lucid understanding of new data with a sharp focus on an insurer's individual goals are essential. Whether the overall target is to improve risk stratification, expand dynamic underwriting, or personalise the underwriting process, alternative data can be helpful when deployed thoughtfully, with a firm grasp of its limits and possibilities.  

Swiss Re has developed a consistent framework of principles for underwriting with alternative data to help boost chances of success for insurers eager to understand what benefits new information sources can bring to their risk assessment process.

We also recommend creating a robust test environment to pilot or experiment with different designs and different types of alternative data, to make sure it delivers results we understand and can build into sound underwriting.

Test settings also make it possible to develop parameters tailored to clients' individual risk appetites, like maximum limits, minimum business volumes, and the need to minimize anti-selection, all of which can be integrated into real-world protection products. 

With collaboration aimed at gaining a deeper grasp of where and how alternative data can be best put to work, insurers can develop more-personalised protection products, simplify onboarding, improve risk management and increase business volumes, while avoiding unsuspected underwriting hazards accompanying the 21st century data revolution.

Though putting alternative data to work to improve our underwriting and create business opportunities poses challenges, these shouldn't be a barrier to starting down this road. They're just a reminder that enlisting experienced partners to identify the best scenarios for leveraging this data to achieve specific goals will make the journey much more rewarding.

A version of this article previously appeared in the publication Health & Protection.