Professional Employer Organizations (PEOs) Continue to Embrace Predictive Modeling Within Healthcare Underwriting | Verikai

Professional Employer Organizations (PEOs) Continue to Embrace Predictive Modeling Within Healthcare Underwriting

As we put 2020 behind us and look forward to a prosperous New Year, uncertainty looms across the health insurance industry. It’s no secret that the Covid-19 pandemic led to a decrease in overall healthcare utilization in 2020, making 2021 an unusually difficult year for medical insurers to predict employee risks and rates.

Group health insurance is a core offering that contributes to the value proposition of partnering with a Professional Employer Organization (PEO). Given the state of uncertainty the pandemic has brought to the health insurance industry, PEO Underwriting teams are under tremendous pressure to ensure the groups they partner with are inline with their risk appetite. PEOs are constantly looking at how to improve their rating precision and risk selection.

For those of you who may not be familiar with the PEO industry, here’s a quick rundown. A PEO is a type of full-service, outsourcing solution for small businesses. The PEO performs various employee administration tasks, such as payroll, benefits administration, worker’s compensation, all on behalf of an employer. By using an arrangement known as co-employment, PEOs are able to leverage economies of scale by “pooling” small employers together so that a small business may be able to access affordable healthcare plans, and many other tools, that normally wouldn’t be available to a company of their size.

While no PEO is alike in their approach to growing their market share, many PEOs rely on affordable healthcare offerings to win their clients over and many have turned to some creative ways to keep their health insurance plans competitive.

According to Willis Towers Watson, more than two-thirds of insurers credit predictive modeling with reducing issues and underwriting expenses, and 60% say the resulting data has helped increase sales and profitability. After discussing with a number of PEOs that offer major medical plans and networks, roughly 90% of them have incorporated predictive modeling and risk scoring into their medical insurance underwriting process.

According to the medical insurance underwriting teams at the various PEO’s we have worked with, these are their top priorities for 2021:

  1. Find better ways to identify high risk individuals and groups that may adversely impact the overall health of their books of business
  2. Seek out improved methods in underwriting their smaller employer groups, since unlike traditional group healthcare, claims information is rarely available when a PEO is competing for a new prospect
  3. Explore new ways to expedite their underwriting process

For many PEOs, the speed in which they can get a medical insurance proposal into the hands of their prospect or broker partner can sometimes be the differentiator in winning a new client. In the past, PEOs relied on individual health questionnaires that had to be filled out and returned by the prospective employer group. The questionnaire would ask an individual seeking medical coverage detailed questions about their health history, an intrusive process that slowed down the policy lifecycle and left significant room for error. Additionally, many individuals are not comfortable providing their entire health history to their employer, rendering the health questionnaire incomplete or inaccurate.

Recently, numerous PEOs have adopted predictive modeling underwriting tools and have found significant value in their implementation. The most common use cases for predictive modeling in PEO medical underwriting are threefold:

  1. Reducing loss ratios: many predictive modeling tools provide risk scores that allow underwriters to quickly identify if an employer group is worth pursuing aggressively or if the risk within a particular group is too high to bring into their medical insurance pool
  2. Developing discounting strategies: PEO underwriters may consider providing a discount to particular groups that score below a predetermined threshold
  3. Expediting the underwriting process: with speed being critical, adopting predictive modeling allows an underwriter, armed with only a census, to receive instant feedback on the type of risk they are evaluating. This new approach has allowed many PEOs to move away from the time consuming, individual health questionnaires that had been the industry norm for decades

PEOs have long been a suitable option for small employer group’s looking to outsource administrative processes and find more cost-effective ways to provide health insurance to their staff. With the cost of group health insurance continuing to rise and the quality of the plan offerings continue to fall, PEOs are looking for new ways to improve their underwriting processes. It’s why we at Verikai are focused on helping PEO organizations. We are the only behavioral based, machine learning, predictive risk platform supporting the PEO industry. At the heart of our platform and what makes Verikai unique, is a database of 250 million people with over 5,000 behavioral attributes per person. We track over 1 trillion data points and can match up to 95% of the adult population in the United States. Verikai also taps into our database of clinical and RX claims on 250 million individuals. When combining all of these unique data points, the Verikai risk scores amplify the medical underwriting process for our PEO partners, allowing them the competitive advantage needed to increase their growth and win more profitable clients.

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