A recent Gartner survey of Health Insurance CIOs found that 29% aimed to increase Artificial Intelligence (AI) spending in 2022. Health insurers likely plan to invest more in AI use cases that streamline existing processes, improve the member experience, and ultimately improve competitiveness and profitability. But an often overlooked process can result in significant productivity gains and an improved member experience. That process? The premium billing process.
Here’s an overview of how streamlining health plan premium billing processes with AI can result in measurable productivity gains:
What is the Issue?
It is a common billing issue: Individuals who purchase health insurance directly or through an exchange must pay a monthly premium. Many choose to pay by check. But in their haste, they neglect to send billing information that helps the health plan identify the payment.
Unknown payments can be especially prevalent among Medicare Advantage populations. Often, a spouse or other family member may attempt to pay for a premium with their bank account. Or a spouse pays both health insurance premiums on a single check. As a result, the name on the check may not match a member, and the dollar value may not match the monthly premium for a single member.
Applying these unmatched payments to member accounts becomes a manual process. A member of your billing team must review the check, looking for clues that help identify where to apply the payment. Depending on your enrollment volume, your billing team could spend dozens of hours each month reviewing premium payments.
How Much Does Manual Processing Cost?
Those dozens of hours add up. By automating the process with AI, depending on your volume, you may be able to redeploy one or multiple members of your billing team to more strategic tasks. In addition to the cost of manually processing unmatched payments, there are significant opportunity costs. By minimizing manual billing tasks that waste your billing team’s time, you free them to focus on strategic tasks that can optimize revenue.
How Does AI Solve It?
To solve the problem for our clients, Certifi built a machine learning (ML) solution to streamline the process. The ML reads images of the checks, learning where to find relevant information like name, address, and payment amount. Then, it suggests accounts that it determines to be the best match with a reliability score. A human then makes the final determination on who sent the payment. That account selection feeds more information to the ML, helping improve its suggestion accuracy.
What is the Impact?
Internally, we have found a nearly 400% productivity gain when using the machine learning-based solution. Machine learning enables our team to process as many as four times as many unmatched checks in a given period. As our technology learns from each account selection, we expect productivity to increase over time.
Where else can AI be Applied in the Billing and Payments Process?
There are several areas to apply AI and ML and improve the billing and payment processes. For example, health insurers can use AI to improve the quality of invoices. Using AI to compare current invoices and associated transactions to past transactions to find anomalies can help improve the quality of every invoice without manual quality control checks.
Or, health insurers can leverage AI to analyze any incoming files (like subsidy information) that they include in premium calculations. Instead of relying on a member of your billing team to review the file, training a machine to find common errors can save time and significantly improve the quality of every invoice.
Certifi’s health insurance premium billing and payment solutions help healthcare payers improve member satisfaction while reducing administrative costs.
