Fraud is a major contributing factor to escalating healthcare costs: fraud costs the U.S. about $60 billion annually and accounts for up to 10 percent of total healthcare spending. Graph analytics can generate graph-based machine learning features for a low risk provider (“good doctor”) and a high risk provider (“bad doctor”) and use them to train an artificial intelligence to look for these profiles within huge healthcare datasets.
In this solution brief we examine how graph analytics can be used to find features that can train an artificial intelligence to detect fraud at scale. These include a stable group for ICD codes, the cost of care averaged across a community and potential undeclared prescriber-facility relationships.
TigerGraph is the only system today that can help us make real-time care-path recommendations using knowledge of 50 million patients. Your products will have worldwide impact on making everyone’s lives better in more ways than you can imagine.
TigerGraph is the only scalable graph database for the enterprise. Based on the industry’s first Native and Parallel Graph technology, TigerGraph unleashes the power of interconnected data, offering organizations deeper insights and better outcomes. TigerGraph fulfills the true promise and benefits of the graph platform by tackling the toughest data challenges in real time, no matter how large or complex the dataset. TigerGraph’s proven technology supports applications such as fraud detection, customer 360, MDM, IoT, AI and machine learning to make sense of ever-changing big data, and is used by customers including Amgen, China Mobile, Intuit, Wish and Zillow, along with some of the world’s largest healthcare, entertainment and financial institutions.