The IMQ process is flawed. Here’s how alternative data is the solution. | Verikai

The IMQ process is flawed. Here’s how alternative data is the solution.

Most underwriters can admit that individual medical questionnaires (IMQs) are neither efficient nor accurate in predicting future insurance claims.

What’s wrong with IMQs?

For starters, no one particularly likes them. Employees don’t want to send their personal health information to their employers. Employers don’t want to chase after paperwork. Brokers don’t want to nag their customers to get them done. And underwriters realize that IMQ’s ability to predict risk is limited, at best.

While IMQs intend to understand employees’ medical histories better and, ultimately, what they may or may not claim against in the future, their accuracy is flawed. Why?

  • They have low fill rates. Intentional or not, IMQs are often misplaced and never completed or submitted.
  • People aren’t forthcoming. Some will lie or skew responses knowing their employers will see them. Some may not know how to answer a question and will instead guess. And others will breeze through it quickly to “check it off their list” regardless of the impact on the result. The opportunities for error are endless.
  • They look backward, not forward. IMQ responses are only as good as the information the employee knows. For example, a patient may have an undiagnosed condition that will cause them to claim in the future – yet it won’t show up on an IMQ.

These problems are not news in the health insurance market; what is news is that there is an alternative solution that does not sacrifice speed or accuracy.

The Alternative Data Solution

Underwriters no longer need to limit themselves to tools that only solve for half of their needs. With advancements in technology and data, we can now use applied data technologies to modernize the underwriting process, achieving better risk selection, increased volume, and more competitive rates.

For example, Verikai’s Capture for Health platform uses behavioral data and advanced machine learning model to provide underwriters with instant reports and insights to help make informed decisions. Given that 80% of medical claims are attributable to an individual’s behavior and lifestyle choices, we must consider behavior as a predictor of future claims.

Reports based on behavior are profoundly more valuable and cost-effective. Why?

  • The data is accurate. Like an IMQ, Verikai’s data is consumer-provided. However, unlike an IMQ, it is gathered in numerous settings in which there is no incentive to lie, manipulate, or rush. In fact, it’s nearly impossible to do so.
  • The data is predictive. Behavior is second only to genetics in predicting future medical outcomes, even when the patient may not know of an underlying condition.
  • They are instant. Get reports quickly and easily, requiring little to no involvement from the employer group or their employees.
  • They are proactive. Determine bad risks at the time of application to protect the carrier’s business. And determine the good risk to drive growth and profitability.
  • They drive the process. Create workflows at the top of the funnel based on objective data.

The goal of artificial intelligence software is to help users complete a task with more efficiency and provide better outcomes. Modern underwriting tools are no different. While there may never be a one-size-fits-all approach to predictive models, the tools available to underwriters are growing in sophistication and allow businesses to utilize all the tools available to them to generate the best, most holistic results.

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