Machine learning could become a new weapon in the fight against Medicare fraud.
Researchers at Florida Atlantic University’s College of Engineering and Computer Science recently published the world’s first study using Medicare Part B data, machine learning, and advanced analytics to automate fraud detection.
They tested six different machine learners on balanced and unbalanced data sets and eventually found that the RF100 Random Forest algorithm would be most effective in detecting potential cases of fraud.
Then we can alert researchers and auditors, who should focus on 50 cases instead of 500 cases or more.”
In the study, Bowder and colleagues examined Medicare Part B data, covering 37 million cases from 2012 to 2015, for incidents such as patient abuse, neglect, and billing for medical services.
The team has reduced the data set to 3.7 million cases, which is still a challenge for human researchers charged with pinpointing Medicare fraud.
Machines are much better than humans at processing large datasets.
They are able to detect and recognize thousands of patterns on a user’s purchasing journey instead of the few captured by creating rules.Faster & Efficient DetectionIncreased AccuracyBetter Prediction with Larger DatasetsCost-effective Detection TechniqueRead the full article about How Machine Learning Facilitates Fraud Detection?
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