Using data as fuel in the case of accelerated underwriting

Using data as fuel in the case of accelerated underwriting

Accelerated Underwriting is a game changer for insurers and policyholders alike. Different to simplified issue, which gathers less detail and increases risk at a higher price point, it’s still a faster process, but aims for competitive levels of coverage and premiums with the use of data analytics to assess risk.

As we all know, the underwriting process for insurance policies has long been lengthy and cumbersome. Jumping through the hoops required can upset customers, frustrate financial advisers, and is increasingly something not many younger millennial folk are willing to put themselves through.

Being able to fast track this process with accelerated underwriting means policyholders’ needs can be met much faster, with acceptance confirmed in a matter of hours from the time of application. This swift turnaround enables insurers to be highly competitive, and meet increasing consumer expectation for quick response and action.

From this perspective, automated underwriting sounds like an ideal solution to some of the life insurance industry’s long standing issues, but what does this actually mean for the insurer? Here’s a speculative look at AUW in an ideal world, and the struggles of bringing that world into focus:

How does it work?

Accelerated underwriting is achieved with the use of expert, automated underwriting processes, using freshly available and affordable data (motor vehicle records, criminal record checks, pharmaceutical records etc) to score a potential policyholder, combined with personal data collected in real time.

According to Rick Pretty, SVP at SCOR R&D, studies from his company “have shown these data sources to have statistically significant mortality risk attributes that can supplement or, in some cases, replace traditional fluid-based underwriting inputs, often with minimal mortality risk implications.” (SOA Product Development Newsletter, June 2017)

Customers answer a brief series of questions, and qualify for this sped up process if they meet straight-forward criteria (e.g. non-smoker, clear family history and no pre-existing conditions). Their self-reported information can then be analyzed alongside the larger pool of data, and may be accepted without the applicant needing to provide any medical records and undergo invasive lab tests.

The ideal scenario here is that prospective policyholders are covered faster, and advisers are paid faster. Though this obviously isn’t always the case - underwriting may still take longer for some who don’t fit the criteria, and a traditional underwriting process may still be required.


How best to manage new data

One of the key struggles in implementing successful Accelerated Underwriting is understanding the relative mortality impacts of the new data sources. This includes identifying which factors are actually relevant to the organization’s strategy, and how their current business profile effects risk outcomes.

The ideal AUW solution can vary drastically depending on the state of the current business and the desired outcomes. Obviously the investment in research and development here is substantial, but well worth it for the potential competitive advantage.

Preventing non-disclosure

A consideration for insurers is the reality that self-reporting by applicants does not necessarily equate to full disclosure. A 2013 study published in the Journal of Insurance Medicine stated 19.3% of life insurance applicants who tested positive for cotinine had self-reported as non-tobacco users, demonstrating how a significant percentage of smokers may misrepresent their status as a tobacco user.

Aside from those who deliberately deceive are applicants who did not realize the required extent of reporting on family history, and what counts as a pre-existing condition from their own medical history. Communication around this needs to be simple, clear and explicit, and ensure the consequences incorrect information can have on the policyholder when making a future claim are understood.

It’s not better for everyone

More granular, personalized “underwriting” based on data may also have negative consequences for some; those who are least at risk will pay lower premiums, while higher-risk lives will end up paying higher premiums.

Essentially, accelerated underwriting has been designed for the younger consumer who is generally healthier, tech savvy, and not scared off by the prospect of purchasing complex products online. It’s therefore a great tool for meeting the needs younger clients who expect a more convenient process.

Think it through

Accelerated underwriting can have far reaching benefits for insurers, with a very noticeable decrease in costs (less attending physicians and lab tests). Customers are happier with faster cycle times and often more applicants become policyholders. In some cases the cost impact can be significant.

Looking forward, these data-based processes will only become more accurate as wider data becomes available from new sources. The key is making sure we use these new capabilities intentionally. Accelerated underwriting could be a game changer, but the key is context. If you’re not being strategic and you’re not directly benefiting your target customers in a meaningful way, it’s not worth the investment.

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