What P&C AI lessons can be applied to medical stop loss

As healthcare costs continue to rise, more employers are turning to self-funded insurance plans to offer health insurance to their employees. Self-funding allows businesses to save on healthcare costs and customize coverage to suit employees’ specific needs. In the United States, approximately 64% of workers with health insurance are covered by self-funded employer plans, with 62% covered by a medical stop loss policy. 

Self-insured employers buy high-deductible stop-loss policies to ensure that they are reimbursed for unexpected large claims that could otherwise wreak havoc on their cash flow. In the face of rising costs and an increasing focus on quality, medical stop-loss insurance will remain important for managing volatility.

However, many of today’s medical stop loss insurers base their underwriting and pricing on outdated actuarial models and static data. This simplistic approach to estimating healthcare costs often leaves underwriters unable to predict coverage limits accurately and therefore challenged to maintain their loss ratios. Luckily, property and casualty insurance may provide the answers that stop loss carriers have been looking for with the way that it leverages artificial intelligence. AI and predictive analytics provide a more rigorous approach to risk management and can help stop loss carriers improve risk selection and limit costs while still providing coverage for self-funded plans.

P&C and workers’ comp insurers are ahead of medical insurers when it comes to identifying risks and making more accurate predictions using AI. As an example, if someone slips and falls at work, a P&C insurer will likely use AI to assess a wide variety of risk factors and incorporate them into predictions, to alert claims managers of potentially catastrophic outcomes.

Medical stop loss, however, does not assess risk in the same manner. And with medical stop loss premiums up 10% for 2022 according to Segal’s national stop-loss database, it’s time to rework how stop loss insurance is calculated so that self-insured employers can take advantage of value-added quality and cost-containment solutions afforded to self-funded employers. Medical stop loss can learn great lessons from what P&C insurers have already done. 

How are P&C providers leveraging AI

In addition to the workers comp claims management example cited above, P&C providers are already leveraging AI in creative and game-changing ways. Let’s consider just a few:

Consumers expect speed, and in the B2B market, turning quotes around quickly can mean the difference between winning and losing an account. AI is providing P&C underwriters with that speed in several ways. For one, it enables straight-through processing, meaning that AI algorithms allow low-risk applications to be processed automatically. This means claims are processed more quickly and quotes can be written instantly without requiring an underwriter to get involved in the process. As an added benefit, the underwriter can instead focus on more complex applications that would benefit from their attention and insights.

Additionally, AI can quickly identify signals that often appear together and imply future risk. In the case of a P&C auto insurer, higher driving mileage per year in addition to an older car and a young driver can lead to a higher risk profile. Therefore, the algorithm may recommend a higher premium to underwriters, allowing them to turn the policy around faster.

In today’s P&C insurance industry, the biggest risk is a lack of insight. In addition to speed, P&C insurers also use AI to identify hidden risks — picking out what appears to be a clear-cut case that could be problematic in the future. AI can consider hundreds of variables and many more inputs than humans in a far shorter amount of time. This can surface more risks, leading to more accurate policies.

How medical stop loss carriers can adopt P&C methods

AI can provide unique opportunities for medical stop loss underwriters as well. By applying statistical models, AI algorithms can look at thousands of policies in a fraction of the time it would take human underwriters and automatically process simple or straightforward quotes. This frees up underwriters to tackle the more complicated accounts, allowing them more time to make accurate predictions on complex cases.

AI algorithms can also identify people who might not present as high-risk today but could become high-risk in the future, according to their claim’s history. It can consider many more inputs. Is the person taking certain medications? Do they have a history of certain treatments? Do they present with other risk factors? Because AI can analyze an enormous amount of patient medical data, more signals can be considered and hidden risks can surface that might not have been detected by a human.

AI recognizes patterns better and faster than humans. Through pattern matching, AI can also benefit stop loss coverage by providing key indicators of a particular illness or injury. AI can detect minute anomalies that even the most experienced physicians would not be able to notice. These intelligent insights help patients get the correct diagnosis and into tailored programs that are specific to that diagnosis. Not only does this give policyholders an opportunity for enhanced care and the best ROI, but insurers profit because they’re able to take proactive actions during policy creation. In addition, patients benefit because they’re getting better information about their own health and can proactively address their specific medical issues.

There are thousands of programs to help specific targeted populations, and AI can perform pattern matching to not only suggest programs that could be beneficial, but make predictions about the ROI of those programs.

Staying competitive

AI helps medical stop loss providers stay competitive and scale more easily. Some providers can get requests for as many as 10,000 quotes a year, but they may only actually get to process 2,000 of them because they just don’t have the capacity to analyze them all. However, by using AI, providers can identify the best 10% of the 10,000 and work on those first to maximize their resources. And AI can automatically process straightforward cases, allowing them to produce even more quotes and price policies more aggressively.

By automating parts of the process in this way, medical stop loss providers can also improve the efficiency of their seasoned underwriters because the underwriters don’t have to be involved in every step of the process. They can review simple policies quickly before they go out, but they remain free to analyze more complicated accounts and provide better predictions. 

AI assists medical stop loss providers in limiting costs without removing a common stop-loss industry practice in which the group is charged a higher deductible for certain plan members based on their prior claims experience or the likelihood of becoming high-cost claimants in the future. 

As the medical stop loss market grows, underwriters must learn from their P&C colleagues and incorporate AI into their underwriting process, to stay competitive and scale their capacity. Not only will it reduce risk, but it will also allow them to provide better service to their policyholders by offering more information about their health and recommendations about the best course of care.