Using Artificial Intelligence and Machine Learning to Fight Medical Fraud

It’s simple for data analysts and fraud investigators to know what red flags they’re seeking out in the data they look at and what questions they need to be asking. It could be “was a payout made for a deceased individual?”. This is an obvious error to check for on the surface by humans. They happen all the time and are a basic result of rule-based payment engines. This occurs due to time-lapses in data updates and basic human error.


The Consequences

The overall loss of money to fraud is astounding, and many people have figured out how to cheat the system in many ways that we’re just now catching. As far as time-lapses go, it just seems to be an accepted issue within the process that will result in, at worst, fraudsters exploiting this weakness then disappearing before they can be caught. At best, it can be considered a government approved loan that providers repay years down the road. Either way, it’s something that needs to be circumvented.


Benefits of AI and ML

AI and ML have the ability to analyze layers that may not be obvious to the human mind. They can detect abuse patterns with providers who consistently manipulate the system by billing posthumously. Additionally, AI and ML can specifically determine patterns of obvious abuse within the system; especially when the providers would already have access to death dates etc. These cases of obvious fraud can be caught significantly quicker by AI and ML because they can point out details that humans simply gloss over and do not notice. If you’re looking to fight fraud within your business, AI and ML can help you without costing notable time, money and manpower.

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