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The Way forward for Underwriting, Half 3: Enabled Underwriting


In the first two parts of this series on the future of underwriting, we examined how data, omnichannel access and intelligent tools to collect, validate, supplement and prepare information for underwriters can be used. In this post, we’ll focus on underwriting itself and how collaboration between people and smart tools can reach new heights.

Is the underwriter of tomorrow a human or a robot?

The specter of people losing their jobs to robots looms over any discussion of the use of intelligent technologies. However, the current decision of the wearer is not a binary decision about whether the person or the machine should take the risk. Rather, it’s about how best to use machines to help human underwriters make more consistent, profitable underwriting decisions.

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This is not intended to deny the existence of automated underwriting – it already exists and is being expanded to cover homogeneous risks. However, more complex cases and coverage will still require human intervention. The future of high-end underwriting lies in figuring out how best to combine human intuition and expertise with the processing abilities of machines.

Machines charge expertise

One of the most valuable skills of a seasoned insurer is their ability to assess risk against a peer group of similar risks from the insurer’s experience to determine a policyholder’s relative risk. Combining a human underwriter with intelligent technology improves this process by using comparative analytics to measure a single account against peer companies in the carrier’s portfolio and provide a broader view of the risk in the account.

Smart machines mean that an insurer does not have to rely solely on their own experience and judgment when examining the details of a submission. When reviewing the details of a submission such as payroll, assets, finances, losses, population, or driving record, the underwriter doesn’t have to rely solely on his own expertise. Instead, an analytical dashboard can highlight where that particular account is doing better or worse than its peer group. This information will help the underwriter determine the overall risk of the account and how aggressively to rate it. More importantly, they are prevented from believing an account is better or worse than it actually is by providing an empirical point of view to aid in the consistency of their judgment.

Eliminate blind spots and increase productivity

Machines can also enable the underwriter to be more productive and process large numbers of assets without missing out on important details.

For example, with large real estate and auto accounts, it’s not uncommon for an insurer to have to value hundreds of properties or vehicles. Analytical and machine learning techniques can be applied to identify features or vehicles that require special attention due to defined hazards or remote information. With these tools, an underwriter can quickly identify items from the long list that require human attention. This makes the underwriter less likely to miss out on engagements. For employee benefits insurers, similar techniques can be used to evaluate large schedules for benefits and classes.

A new era in underwriting

Improvements like this are a new chapter in the history of underwriting. The original focus of underwriting was on capturing the information required for the policy or offer in our rating and offer systems. Then we had the impetus to develop workflows that help us manage and move the work along the drawing process. Today we need to enable the underwriter to overlay intelligently prepared data and workflow insights so they can make more consistent, high quality decisions that lead to better underwriting outcomes.

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