Insurance Technology & Underwriting Risk: What’s Next?

If you are reading this, you or someone from your organization may have attended InsureTech Connect 2019 and learned about the latest in insurance technology.

Now what’s next? You or your team members may be asking yourselves this very question after returning from a sojourn to sin-city.

With so many companies, vendors, partners, etc. to follow-up with from what is a top industry event, where is your time best spent and with whom should you further a relationship?

Insurance technology for risk processing

Writing and retaining profitable business is the primary objective of all insurance companies.

From the top of the funnel to the stack of claims cases piling up, carriers are continually adjusting operations, adding solutions, and revising business procedures to process both applicants and claims faster, cheaper, and more profitably.

The risk assessment problem

It is no secret that 70% of the work done by underwriters today will eventually result in a “No.” Getting to that answer consumes most of the initial resources.

How much better would your book of business perform if you had a clearer look into the future and you could see which applicants were going to be a problem long term and unprofitable? What if you could see which applicants contained just the right amount of risk and profit potential?

Current InsurTech providers focus on addressing this need by building predictive analytics and behavioral analysis systems based on historical data to augment the predictive power of the underwriting discipline.

Significant progress has been made in categorizing and pricing risk at the onset. However, even with advances in insurance technology, combined ratios are still at unsustainable levels. Underwriting profit is more of a distant goal than a near term reality.

With each new analytic solution to improve the insurance process comes the complexity of distilling the right information from alerts, reports, and indicators.

As successive layers of analytical sophistication are added, the overall picture can become blurrier instead of clearer. In this Risk & Insurance article, an executive at The Hartford was quoted as saying:

One of the anticipated solutions for making this data clearer is to apply artificial intelligence.

In the meantime, are there more productive measures that integrate well with existing and additive solutions?

The voice of the applicant

Among all the complex insurance technology solutions, there is a surprisingly simple and accurate way to add missing risk profile data points.

What if two common words that an applicant uttered over a phone line could yield far greater insights into risk than complex behavioral and statistical models?

And what if analysis of those two spoken words gave underwriters the ability to say “No” 30% faster and provided a higher degree of confidence when saying “Yes.”

A pipe dream? No. Proven technology.

How does this form of InsurTech work?

Our technology uses military-grade analysis and the familiar subtly of the human voice to identify risk in large populations. It quickly flags the low risk participants for streamlined—and even automated—processing. Those identified for risk are funneled to an investigative team for further follow up.

The solution requires minimal set up and administrative resources. The implementation is straight forward and complements current processes.

Applicants answer a set of five questions during a phone interview. All questions have “Yes” or “No” answers. The question set can be delivered in any language, in any country, without bias, and delivered on any size population. A question can be as simple as:

The results of each interview are fed back into any existing dashboard or processing system currently in use. The system could be Salesforce for insurance or any other system capable of consuming APIs. We also provide a system which you can chose to use alongside or in lieu of an existing solution.

Our voice analytics solution gives the underwriter an indicator that places applicants into 4 categories of risk:

• High
• Potential
• Average
• Low

What’s more, these indicators include specific attempts to game the system. Also, the indicators flag participants who openly admit to something during the interview.

By rapidly categorizing applicants with this technology, the underwriter can apply their expert human reasoning to cases that have been flagged for risk and need further evaluation.

Those assigned a low risk rating can be processed through automated systems confidently with minimal oversight.

When experienced professionals can focus solely on the applicants that require reasoned assessment, the effort wasted on eventually disqualified applicants is significantly reduced.

Ongoing Applicability

As insureds enter a book of business, the initial indicators can be carried forward and added to other data points collected during and after policy issuance.

Historical indicators are valuable, but having a tool that can be applied at a point in time is exponentially more useful. To gauge whether risk level has changed at any period of time in the policy lifecycle, voice assessment can be applied mid-term, prior to renewal, or after binding.

Learn more about how Clearspeed Verbal works to help you identify risk.

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