Anomaly detection in healthcare data is an enabling technology for the detection of overpayment and fraud. In this talk, we demonstrate how to use PageRank with Hadoop and SociaLite (a distributed query language for large-scale graph analysis) to identify anomalies in healthcare payment information. We demonstrate a variant of PageRank applied to graph data generated from the Medicare-B dataset for anomaly detection, and show real anomalies discovered in the dataset.

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