Accelerated Underwriting

Insurance companies are constantly searching for ways to control costs and more accurately price risk while, at the same time, making the process of acquiring insurance as straightforward as possible for policyholders, whether business or consumer.

One of the tools they have adopted and continue to improve upon is accelerated underwriting (AUW). By refining and, in some cases, removing as many steps in the underwriting process as possible, insurers strive to create a simplified method for their customers that speeds the issuance of coverage. Of course, insurers must be careful not to eliminate any steps that could lead to ongoing premium leakage because of under-pricing the risk posed by an applicant. This applies to both life and property and casualty (P&C) coverage.

It must be emphasized that accelerated underwriting is occurring primarily among life carriers; the underwriting process for home and auto carriers is far more simplified, however those companies are also benefiting from streamlined data that gives them speed and flexibility on issuance and premiums.

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Applying Digital To P&C Carriers

Today, the entire AUW process is being supercharged by digital technology. As a result, more sophisticated means are being applied to improve AUW. Through the extensive use of data, analyzed with powerful algorithms, insurers are able to break away from old models that fit policyholders or their businesses into categories – categories that often fail to allow for individualization and result in risk priced higher or lower than necessary.

The rate of adoption of these digitalized and data-powered tools is being whipped forward by the ongoing global pandemic. It’s self-evident that life insurers would embrace non-fluid based (but still risk-accurate) underwriting in the present environment, however the same holds true for the P&C field. Donn Vucovich, Gerent’s Insurance Industry Lead, maintains that P&C companies are using data aggregators to create a complete picture of a commercial building, for example, without an appraiser having to venture inside.

“The emergence of data consolidators is big. Companies like Verisk and Hazard Hub are gathering vast amounts of data from wide-ranging data sources for insurers. Even companies like TransUnion [the credit-scoring firm] are creating underwriting scores for P&C companies. So, there are lots of interesting dynamics that are changing roles around who’s doing what and how risk is being analyzed,” he explained. What these aggregators allow insurance companies to do is speed the underwriting process and, at the same time, reduce costs and the likelihood of premium leakage.

The capability of advanced analytics for P&C carriers is so powerful that Donn argues it will be able to be “specific to certain profiles within a neighborhood, within a section of the neighborhood, even within a house. The key is that underwriting needs to match pricing. It’s the old adage: ‘No risk is a bad risk; it’s just priced wrong’,” Donn stated.

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Applying Digital To Life Underwriting

For life insurers, the same artificial intelligence can predict illnesses among family members based on one member’s DNA, through modelling. Because life insurers must assess risk by “taking a snapshot” of an applicant’s health at a given point in time and extrapolating that picture forward 10, 20 or more years, the underwriting process has been far more complex than with P&C, generally speaking. Using data aggregators to help reduce the underwriting period has allowed clean applications to be approved automatically in record time, thereby lowering an insurer’s costs and the workload on underwriters.

Such technology comes with its own issues concerning the use of personally identifiable information (PII). However, Donn Vucovich sees the day coming when personal information can be held within a consortium created along the lines of the Medical Information Bureau (MIB) which is owned by over 400 insurance companies in North America. A highly-regulated consortium like the MIB would acquire and utilize massive amounts of data acquired through aggregators to speed up life underwriting.

Russ Bostick, a former carrier executive and now an underwriting consultant to the industry, points out that the growth of life insurance over the past decade or so has been a slow-to-no-growth reality. “People are simply put off by the whole underwriting process in general. The length of time it takes, the multitude of questions that are asked – it’s not a pleasant experience,” he argues. “That’s why way more people have auto insurance than life insurance. Sure, you have to have insurance to drive but it’s so much easier to acquire than life.”

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How Analyzed Data Is Impacting Auto Premiums

That point is underscored by the fact that a growing number of auto insurers are also using analyzed data or telematics to more accurately price car insurance and more quickly issue policies. The net result is that costs are reduced, premiums accurately reflect risk, consumers are more satisfied and companies increase earnings.

It's this last point that Donn Vucovich believes is the goose that will lay the golden egg; increased earnings can be realized through elimination or near-elimination of premium leakage. “The more refined we can get in our price points, our algorithms, and our ability to price risks, the more money can be made on marginal differentiation,” he says. He uses the example of two neighbors who work in the same location every day. One is driving a car loaded with safety features while the other drives an older model lacking similar advancements. The safer model may result in a hundred dollars less in claims but the insurer reduces its premiums by just 10 dollars a month. The driver gets a good deal and the insurer beats the competition while arbitraging 90 dollars.

This may be a highly simplistic example but it underscores how algorithms, the heart and soul of data analytics, can drive cost competitive products for insurers. At the end of the day, insurers will have moved away from being underwriters and processors, instead moving those functions to Managing General Agencies (MGAs) and focusing on analytics and their utilization.

“In the next 10 years, this is really going to change the insurance business, especially the risk side for life insurers. I absolutely believe the industry needs to grasp this, take ownership of it and really drive all the dimensions of it,” Donn concludes.