Working in insurance, most of us know the average consumer isn’t our biggest fan. Sure, in theory, insurance policies are there to protect people and businesses from financial losses during the worst events of their lives. In reality, studies show that the majority of consumers believe an insurance company will do as much as possible to avoid paying claims.
From the inside, this typically isn’t true. There are many legitimate reasons an insurance carrier may deny a claim. Usually it’s because the claim is for something not actually covered by the policy, it was filed too long after the loss, it didn’t include the necessary information, or the policy wasn’t actually in force at the time of the loss. In rare cases, carriers might deny a policy due to attempted insurance fraud.
Those of us within the insurance industry largely want to help people, and want people to receive payment for valid claims they are entitled to reimbursement for. At the same time, in a claims-management process that’s long been dependent on manual human work, we can’t say there’s zero bias, or every claim that should be approved actually is. This is where new technology can help. As insurance claims processing becomes more automated, it relies on rules and not human judgment.
There still are, and always will be, situations when a degree of human judgment is necessary to determine the right outcome of a claim. But implementing artificial intelligence (AI)-based automated rules can result in a greater level of uniformity and objectivity since (for the most part) AI follows rules and doesn’t make value judgements the way humans do. A study by IBM found that 84 percent of insurance industry companies that are implementing or plan to implement AI cite “improved customer satisfaction” as their top reason for doing so. Keep reading to see three ways AI will make the claims process better, faster, and more equitable for consumers.
Healthcare and health insurance are some of the most hated aspects of American life. Who hasn’t experienced a claim being denied, even when you did all your homework to make sure you were visiting an in-network provider and following all the rules?
New developments in AI may be able to change this notoriously consumer-unfriendly part of the insurance industry. A few ways it’s already in use include:
Using AI, new tech solutions are able to collect data from across many different (previously unconnected) sources to complete the medical insurer’s prior-authorization process. Instead of relying on overworked office staff at your medical provider, AI-powered technology can gather and quality-control everything your insurance company needs in order to pre-approve a medical service or prescription drug. AI can also eliminate the back-and-forth between you, your provider(s), and the insurance company by intelligently pulling the right information from the right sources to achieve an accurate and complete prior-authorization request.
In a similar way to prior-authorization processes, AI can dig through large numbers of highly technical insurance plan documents to determine which treatments and procedures are covered by your plan. There are even new software tools that promise to discover coverages the policyholder and medical provider didn’t know existed. AI can’t change your insurance policy, or magically make you eligible for coverage you’re not, but it can be much better than a human at understanding the provisions of your plan and highlighting what you can file a claim for versus what you can’t. Using AI tools for this purpose can reduce the number of denied health insurance claims, which end up costing consumers and medical providers.
AI can help solve one of our largest pain points in the healthcare system: the lack of price transparency for consumers. With access to data from your insurance company and healthcare providers, AI tools can run cost estimates for a variety of different scenarios: in-network and out, deductible met or not, different coverage levels based on intricate plan details, and more.
All in all, for healthcare claims, the addition and adoption of AI has the potential to make the process much more user-friendly for the everyday people who need their healthcare.
Insurance fraud is costly, and not just to insurance companies. The FBI estimates the cost of non-healthcare insurance fraud to be $40 billion per year – costing American families between $400 and $700 each year in increased premiums. It’s also not that rare! A study by AI-powered software provider FRISS found that about 18 percent of all insurance claims have some element of fraud, including exaggerating losses to get more money.
Traditionally, insurance companies have detected fraud manually. Insurance employees are on the lookout for discrepancies and inaccuracies that could indicate fraud. It also raises red flags when the same person files multiple insurance claims within a short period of time. Insurance companies know their historical statistics on claim frequency and can flag a customer who files claims significantly more often than average for additional scrutiny.
Given the rate of fraud, and its cost to the insurance industry and consumers, these traditional methods aren’t doing a great job. Luckily, with new technology constantly developing, insurance companies can increasingly rely on artificial intelligence to identify fraud, and even predict it before it happens! Using AI to prevent and detect fraud keeps insurance carriers’ operating expenses lower, both in terms of dedicating less people to fraud detection and fewer unnecessary payouts for fraudulent claims. These savings should make their way back to consumers whose insurance rates won’t go up simply to cover the carriers’ fraud costs.
It’s a known fact: humans have subconscious and implicit biases. Setting aside conscious and explicit biases, we all still have beliefs that influence our behavior even if we don’t know it. When humans determine insurance claims, these biases can factor into their decisions. Of course, we’re not saying that an insurance claims adjuster would knowingly award a higher value to a claim based on the insured’s gender, race, or any other protected class. But, there are so many implicit biases factored into each decision we make each day that it would be impossible to remove them altogether. A 2003 study found there was a trend for insurers to assess fault more often to female, elderly, and young drivers (as opposed to male and middle aged drivers).
By removing humans from making judgment calls on individual insurance claims, AI may be able to more fairly assess who’s at fault and how much their damages are worth. At least for the vast majority of claims that fall within largely typical and mundane circumstances, AI can speed up the claims process, resulting in quicker approvals and payouts to customers.
We can’t touch on this topic without a large caveat, however. AI may reduce the influence of human biases in some ways, but it’s also known to follow the biases of its creators (and the dominant culture) in other circumstances.
For example, there are numerous data points that would seem to be completely objective, but can act as proxies for race, gender, or socioeconomic status. If AI is trained to make decisions based on those data points, it can end up disproportionately siding against certain groups of people. This has been widely illustrated through coverage of biases in AI job recruiting tools. Even when the creators don’t intend it, training AI to predict the future based on past outcomes can result in the AI reinforcing past biases that further inequity, rather than removing bias from the system.
AI isn’t a panacea to every challenge the insurance industry and its customers face. But it’s still true that artificial intelligence can process more data, and more complex data, than humans could ever hope to – and at a speed we can only dream of. This computing power means AI can spot trends and make predictions that can increase both the speed and accuracy of the insurance claims process, ultimately making life better for consumers who rely on insurance for everyday protection and just want their claims paid quickly when they have a loss.
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