Researchers from Seongnam si Provide Details of New Studies and Findings in the Area of Machine Learning (Medicare Fraud Detection Using Graph Analysis: A Comparative Study of Machine Learning and Graph Neural Networks): Machine Learning

Health Policy and Law Daily

2023 SEP 07 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- A new study on artificial intelligence is now available. According to news reporting originating from Seongnam si, South Korea, by NewsRx correspondents, research stated, “Insurance companies have focused on medicare fraud detection to reduce financial losses and reputational harm because medicare fraud causes tens of billions of dollars in damage annually.”

The news correspondents obtained a quote from the research from Division of Research and Development: “This study demonstrates that medicare fraud detection can be significantly enhanced by introducing graph analysis with considering the relationships among medical providers, beneficiaries, and physicians. We use open-source tabular datasets containing beneficiary information, inpatient claims, outpatient claims, and indications about potential fraudulent providers. We then aggregated them into a single dataset by converting them into a graph structure. Furthermore, we developed medicare fraud detection models using two approaches to reflect graph information, i.e., graph neural network (GNN) models and traditional machine learning models using graph centrality measures. Therefore, the machine learning model with graph centrality features showed improved precision of 4 percent point (%p), recall of 24 %p, and F1-score of 14 %p compared to the best GNN model. The improvement in recall to this extent could result in substantial cost savings of 3.1 billion euros and 5 billion dollars in the United States and Europe, respectively, benefiting governmental institutions and insurance companies involved in healthcare insurance operations.”

According to the news reporters, the research concluded: “Furthermore, the required learning time of the best GNN model was approximately 250-300 times more than that of the best machine-learning model. This outcome suggests that successful and efficient detection of medicare fraud can be achieved if graph centrality measures are used to capture the relationships among medical providers, physicians, and beneficiaries.”

For more information on this research see: Medicare Fraud Detection Using Graph Analysis: A Comparative Study of Machine Learning and Graph Neural Networks. IEEE Access, 2023,11():88278-88294. (IEEE Access - http://ieeexplore.ieee.org/servlet/opac?punumber=6287639). The publisher for IEEE Access is IEEE.

A free version of this journal article is available at https://doi.org/10.1109/ACCESS.2023.3305962.

Our news editors report that more information may be obtained by contacting Yeeun Yoo, Division of Research and Development, KakaoBank, Seongnam-si, South Korea. Additional authors for this research include Jinho Shin, Sunghyon Kyeong.