  Medicare fraud results in considerable losses for governments and insurance
companies and results in higher premiums from clients. Medicare fraud costs
around 13 billion euros in Europe and between 21 billion and 71 billion US
dollars per year in the United States. This study aims to use artificial neural
network based classifiers to predict medicare fraud. The main difficulty using
machine learning techniques in fraud detection or more generally anomaly
detection is that the data sets are highly imbalanced. To detect medicare
frauds, we propose a multiple inputs deep neural network based classifier with
a Long-short Term Memory (LSTM) autoencoder component. This architecture makes
it possible to take into account many sources of data without mixing them and
makes the classification task easier for the final model. The latent features
extracted from the LSTM autoencoder have a strong discriminating power and
separate the providers into homogeneous clusters. We use the data sets from the
Centers for Medicaid and Medicare Services (CMS) of the US federal government.
The CMS provides publicly available data that brings together all of the cost
price requests sent by American hospitals to medicare companies. Our results
show that although baseline artificial neural network give good performances,
they are outperformed by our multiple inputs neural networks. We have shown
that using a LSTM autoencoder to embed the provider behavior gives better
results and makes the classifiers more robust to class imbalance.
