What if the patient was given a test, but it was actually a less expensive version than what the hospital billed for? Finding the answers to these kinds of questions can involve gathering and analyzing massive amounts of data from myriad sources. Traditionally, human fraud examiners have taken on these tasks, as they specialize in sifting through healthcare compliance and medical billing documents to determine what's legit and what isn't. Healthcare is built around helping people stay well or recover following illnesses and injuries, but like other industries it is susceptible to a range of fraud that can cause financial harm and drive up costs in the system. "Fraud, waste, and abuse is a huge contributor to unnecessary costs and the rise of spend within healthcare in the U.S.," says Arielle Trzcinski, senior analyst at Forrester.

As many countries in the world start to experience population aging, there are an increasing number of people relying on medical insurance to access healthcare resources. Medical insurance frauds are causing billions of dollars in losses for public healthcare funds. The detection of medical insurance frauds is an important and difficult challenge for the artificial intelligence (AI) research community. This paper outlines HFDA, a hybrid AI approach to effectively and efficiently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China.