The insurance industry as a whole relies on analytics and big data to run efficiently. This kind of information helps insurance companies set rates and premiums, as well as avoid assuming too much risk. Part of this process requires predictive modeling to be reliable, which is an ever-changing field of its own. Let's learn more about the value of insurance predictive modeling for healthcare coverage in 2023.

What is health insurance predictive modeling?

For anyone working in the insurance industry, it's important to understand the value of insurance predictive modeling. To put it in relatively simple terms, it's a technique that uses statistics, analytics, artificial intelligence and machine learning to analyze data and help assess the likelihood of a future event. The result is an extremely detailed report that illuminates things like levels of risk that can help the underwriting process.

This technique is especially useful for insurers in the case of health insurance because it is such a highly variable subcategory. Here are some of the things that health insurance predictive modeling can analyze the available data for:

• Pricing and premium rates: Predictive analytics have been used to set premium rates for many years at this point, but the technology is becoming increasingly reliable.
• Risk selection: Data insights can reveal the true risk that an applicant poses based on more reliable information gleaned directly from the source.
• Cancellation risk: Insurers can relatively accurately predict who is likely to reduce their health insurance coverage or cancel it altogether.
• Finding outlier claims: Predictive modeling can help insurers make more reliable claims decisions, like finding outlier claims when compared to previous ones.
• Fraud prevention: Unfortunately, insurance fraud is still a common crime in the United States. With predictive tools, insurers can prevent it in some cases by using information like behavioral data.
• Customer engagement: With predictive modeling, insurance companies can create a more well-rounded customer profile. This enables them to assist the insured with on-demand solutions.

Keep in mind that health insurance has specific requirements when it comes to risk selection. The introduction of the Affordable Care Act (ACA) in 2010 restricts certain underwriting policies, including denying an applicant healthcare coverage because of their predicted risk.

How health insurance predictive modeling can help your business

If there is anything that the past two years have taught those in the health insurance industry, it's that the market can be highly unpredictable. However, the right predictive modeling tools can mean the difference between staying competitive and sinking. A modern health insurance business can use analytics to make the most informed decisions based on reliable data.

The COVID-19 pandemic has made the adoption of new technologies a requirement for any well-run insurance company, and this growth has sustained beyond two years into the health crisis. Insurers need more reliable big data to operate competitively during a time when there is not much data to analyze in the first place. This originally spurred innovation across insurance technology, including predictive modeling.

According to research by McKinsey, "across sectors, revenue growth (as measured by the five-year compound annual growth rate) for digital leaders is on average four times that of companies that only dabble in disjointed digital initiatives." Their study is proof that the future will rely more on insurtech and that customers will expect this level of modernization.

By creating a more reliable risk model, for example, your business can serve policyholders more efficiently. Instead of waiting for someone to adjust the model by hand, the technology can automate the process. Both insurance companies and actuaries can benefit from this type of machine learning, which should continue to keep premium rates accurate.

Even though some of the software and machine data analytics that are most beneficial to insurance companies can be somewhat expensive, the investment is necessary. Competitors are likely to use some form of new-age predictive modeling, and your team needs to meet the rising customer expectations. In the end, these tools are vital to staying afloat in the health insurance coverage marketplace.

Using health insurance predictive modeling analytics correctly

Your team could have access to the most modern health insurance predictive tools, but they are only as effective as your business makes them. You and your insurance team must know how to use predictive modeling technology correctly. According to the tech company, Diceus, there are "five essential stages of a typical predictive analytics process:"

1. Identification: In the insurance healthcare industry, it's valuable to understand what the end goal of your modeling process is. For example, you could be targeting risk selection or premium rate settings.
2. Information gathering: This is where automation and machine learning comes into play. The technology goes to work efficiently collecting policyholder data. 
3. Analysis and modeling: Now that all the information has been gathered, it's time to analyze the results and create the predictive model that will inform the insurance company's choices.
4. Deployment: In this stage, your business sets rates or evaluates applicants based on predictive modeling technology. Note that this is not always possible or legal with some kinds of health insurance underwriting.
5. Monitoring: After the decisions have been made, your team and the associated technology should monitor the effectiveness of the predictive models.

The modern insurance industry relies on predictive modeling to function correctly. Even the most basic predictions use some form of this technology to remain competitive in the marketplace. However, the combination of high-level predictive modeling, in combination with insurance or actuarial professionals, can set a business apart from the rest of the pack.

In addition to creating reliable future-focused models, your team can use these methods to focus on customer loyalty. You can anticipate the needs of your policyholders, sometimes even before they know what they are themselves. Through this, your business can provide stellar customer service, which is one of the reasons that clients decide to stay loyal to your company instead of jumping ship.

Laws and regulations

We already mentioned that there are special restrictions when it comes to underwriting for health insurance, which is important to understand when using predictive modeling for risk selection. Under regulations implemented by the ACA, insurers can't deny any applicant coverage or even charge them higher premiums because of their health.

The purpose of these regulations is to protect policyholders from the impact of adverse selection and risk selection. Risk adjustment becomes even more valuable because it takes into account the applicant's health status and also uses healthcare predictive analytics to look at the meaning of the policyholder's healthcare spending.

This doesn't mean that insurance companies can't use predictive modeling, and there are many other ways to utilize the technology to improve the way insurers operate.  

The future of predictive modeling in the insurance industry

As technology improves, so will predictive modeling in the health insurance industry. This means that your team needs to move and grow with the changing marketplace. To stay current and competitive, insurers need to stay on the cutting edge of insurtech. It can seem overwhelming to meet this challenge, but that is why it's a good idea to work with other professionals who can guide the business in the right direction.

The role of actuaries

When you work with an actuarial expert, the insurer gets professional advice surrounding liabilities for several types of accident and health (A&H) products, such as:

• Hospital surgery.
• Managed care.
• Major medical healthcare.
• Long-term and short-term disability care.
• Medicare supplement.
• Critical illness.

With Lewis & Ellis, our experts can perform independent calculations of all claim liabilities, reserves, and policy reserves. In addition, we are highly connected to state and other governmental agencies, national associations, and employers, which helps our team suggest reliable changes to stay current with health care reform and policy.

There are many components to health insurance, but predictive modeling can help insurers prepare for the future with reliable data and analytics.

Are you ready to stay competitive and work with a team that can assist you in getting there? Reach out to Lewis & Ellis today.