This study aims to use artificial neural network based classifiers to predict fraud, particularly that related to health insurance. 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. To detect medicare frauds, we propose a multiple inputs deep neural network based classifier with an 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. We use the data sets from the Centers for Medicaid and Medicare Services (CMS) of the US federal government and four benchmarks fraud detection data sets. 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 an autoencoder to embed the provider behavior gives better results and makes the classifiers more robust to class imbalance.