Pharmacy Claims Fraud Detection
It is estimated that the United States spent around $3.5 trillion in total, or 18 percent of GDP, on health expenditures in 2017. However, according to the National Health Care Anti-Fraud Association, 3%-10% health care spending is fraudulent, which means up to US$350 billion should be flagged and investigated. The solution we provided helped our client to capture unnecessary spending.
A two-step process consisting of a combination of unsupervised and supervised learning techniques can be used to effectively identify the Fraud, Waste and Abuse in the Pharmacy industry. Machine learning [Spark MLLib and GraphX] was used to identify suspicious activity like co-conspiracies to commit fraud by pharmacies and prescribers[doctors] and others.
Claims submitted by the pharmacies to insurance companies/health plans provide data rich in valuable insights that enable prediction of fraud, waste and abuse. Suspicious activity can be identified by detecting anomalies in the data using unsupervised techniques like clustering, univariate and multivariate outlier analysis, link analysis, simulated fraud signatures, etc.
With known fraud, waste and abuse data, supervised learning techniques like Random Forest, Neural Networks, etc., can be used to identify fraudulent transactions similar to historical fraud signatures. Combining outputs of these techniques, Fraud scores are generated for each player [member, pharmacy and prescriber].
Scope of Engagement
AI Machine Learning Models