Flagging suspicious healthcare claims with Amazon SageMaker Amazon Web Services

The National Health Care Anti-Fraud Association (NHCAA) estimates that healthcare fraud costs the nation approximately $68 billion annually--3% of the nation's $2.26 trillion in healthcare spending. This is a conservative estimate; other estimates range as high as 10% of annual healthcare expenditure, or $230 billion. Healthcare fraud inevitably results in higher premiums and out-of-pocket expenses for consumers, as well as reduced benefits or coverage. Labeling a claim as fraudulent could require a complex and detailed investigation. This post demonstrates how to train an Amazon SageMaker model to flag anomalous post-payment Medicare inpatient claims and target them for further investigation on suspicion of fraud. The solution doesn't need labeled data; it uses unsupervised machine learning (ML) to create a model to flag suspicious claims. This solution uses Amazon SageMaker, which provides developer and data scientists with the ability to build, train, and deploy ML models.