Healthcare fraud’s growth rate is outpacing the overall growth of the healthcare industry. And medical fraud will only continue to grow because of a  growing and aging population producing more healthcare requirements, the increased complexity of healthcare claims, the shift from paper records to digital, and more.

Fortunately, technology can help combat these issues. How can Artificial Intelligence (AI) assist in fraud detection? Here’s what you need to know.

How Does Fraud Detection Work?

Fraud detection AI uses pattern recognition and data analysis to teach itself how to spot anomalies. Data for each transaction is collected from every possible source and then classified for approval or rejection. Both safe and fraudulent activities have their own behavioral pattern characteristics which are measured by healthcare fraud software systems.

These systems review attributes such as proxy filtering and behavioral analytics. The information is then sent to an analyst to make a final decision on whether the activity was fraudulent or safe. The analyst’s decision then provides feedback to help improve the machine learning models.

WhiteHatAI Is Different

One type of AI is just one type of AI. WhiteHatAI’s Centaur uses many different types of AI to examine and detect fraud. The AI techniques and technologies used by the Centaur work together to make decisions, producing a collective intelligence of systems sharing what they know and the models they produce.

Pick the Right Club

Think of it like a bag of golf clubs — a golfer will use different clubs to hit different shots, but all of those clubs work together to give the golfer the right tool at the right time for the best shot at winning.  In fraud detection, the game is not that different.  The more tools you have, the better shot you have to adjust and win against new and changing healthcare fraud schemes.

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