In today’s underwriting environment, carriers are constantly asked to do more with less, and do it faster. Underwriters need to underwrite effectively and price the right risks appropriately while simultaneously providing policyholders and agents/brokers with a great customer experience. It’s a constant juggling act.

That is why anything that streamlines data collection and reduces “underwriting friction” in the insurance submission process can be a “win-win” for underwriters and customers.

The good news is that the information required to underwrite a risk is more readily available from external resources. Carriers may not have to ask applicants for it, as they currently do as part of the typical submission process, if they can access it externally at the time of underwriting.

In some cases, carriers can entirely skip the back-and-forth process of requesting data, having producers and clients provide it, then seeking further clarification, and so on. By using external data whenever available, carriers may acquire more valuable information than they asked for in the first place and underwrite with greater efficiency. This already is happening in life insurance and in personal lines such as auto and homeowners.

Minimizing Friction

Friction is defined as the force resisting the relative motion of elements or objects sliding against each other. It prevents objects from moving or slows them down and results in wasted energy that could be used elsewhere.

For example, a common bit of information in a commercial insurance submission is the applicant’s company revenue. Revenue data can now be accessed online and/or from financial data providers. By accessing this information directly, carriers reduce the amount of data that the insured needs to supply and avoid mistakes that can occur when submitted data is manually entered into company systems.

A streamlined application process is attractive to everyone involved. Besides reducing underwriting friction among buyers, producers and underwriters, a streamlined process can reap a number of other benefits, including increased customer and producer satisfaction, improved new business conversion rates for target insureds, and better retention for profitable accounts.

In place of medical underwriting, many life insurers cull information from credit records and demographic data to underwrite and price the coverage. Emerging new sources of data, such as an individual’s social media activities and profile, provide information regarding a person’s risk profile that can be used in life insurance underwriting.

According to a January 2018 survey by the actuarial firm Milliman, 33 out of the 40 life insurance companies surveyed use or plan to use accelerated underwriting in term life insurance.

Some life and health insurers are tapping into data shared from Internet-connected fitness trackers that policyholders agree to share with their insurers.

In personal property/casualty lines, auto insurers are using telematics devices that capture real-time data about insureds’ driving behavior to help determine customer rates. Similarly, residential property insurers are looking at ways to use more data from interconnected home devices to assess homeowners insurance risks.

Commercial property/casualty insurers need to follow the lead of life insurers and personal lines carriers and tap into available external data resources. This requires commercial insurers to challenge assumptions, question conventional wisdom and explore new ways of doing business. It’s not easy.

Challenging Assumptions

For so long, commercial insurance underwriting has relied on common elements provided by applicants, such as names and addresses, descriptions of business activities and products, revenue by region, number of employees, past legal actions, and a description of insured locations plus assets such as automobiles, aircraft and watercraft.

Today, much of this application data is readily available on company websites, social media platforms, government reports and in online news articles. Asking clients for this information is no longer required. What is required is that carriers identify the truly essential information for writing a risk.

Given changing risks and market dynamics, commercial insurers are wise to ask more questions of themselves to keep testing the value of the information they use for assessing risk. Is the information they’re seeking indicative of risk, and how so? Is there relevant data they aren’t utilizing that can improve their risk insights?

Greater use of external data can help challenge conventional wisdom.

Consider an example from the mortgage lending industry. For years, lenders have commonly tracked how long applicants worked for their employers, the conventional wisdom being that a worker with a long tenure with one employer was a better risk than one with a history of shorter tenures with multiple employers.

More recently, some analysts have reassessed the performance of home loans and determined that individuals with a track record of finding new jobs have more marketable skills and an easier time finding new employment when needed compared to individuals who are long-serving employees of one organization. Other things being equal, this may make the person who demonstrate an ability to find employment a better mortgage risk.

Using analytics to debunk conventional wisdom has enabled mortgage providers to be more effective at writing profitable mortgage business. To benefit from this kind of innovation, they had to challenge an old way of thinking and embrace the new way.

Commercial insurers also have to challenge their ways of thinking. Fortunately, there is plenty of information to test conventional assumptions and develop new ones.

Marketing Applications

International Data Corporation (IDC) projects that by the year 2020 there will be 44 zettabytes, or 44 trillion gigabytes, of data in the world. That figure is roughly equivalent to four million years of high-definition video or five billion Libraries of Congress.

Almost all of this data is not applicable to any specific business objective. Merely collecting more and more data, without a clear business purpose for the data and without an effective framework for managing the data, destroys value rather than creating an asset.

The first challenge is to search through the datasphere for data that is relevant to a business problem or opportunity, the proverbial needle in the data haystack. For insurers, this requires integrating external data with internal policy and claim data to test the external data’s ability to predict claims.

As an example, XL Catlin’s Strategic Analytics team partnered with one of the specialty businesses in its U.S. insurance group to develop a risk segmentation tool using only external data to underwrite and price every risk in its target market. The analytics team sourced, profiled and combined data from multiple external data sources and applied machine learning methodologies to create the risk segmentation algorithm.

But XL Catlin didn’t set out to build this model.

While developing a similar tool for underwriting submissions that utilized both internal and external data, it became clear the company could achieve strong risk segmentation using external data only. This presented a valuable opportunity to quantify the risk of every company in our target market, not just those for which the company receives submissions.

This new tool is, therefore, a marketing model that arose from underwriting considerations, which enables XL Catlin to target good risks and reduce friction in the acquisition process. Since launching the marketing model, XL Catlin has made insurance purchasing easier and increased its new business.

Business development and distribution partners similarly benefit from using the marketing model to prioritize prospecting efforts. Most importantly, customers get an improved experience with significantly reduced underwriting friction.