Auto-encoder Based Medicare Fraud Detection

Mansour Zoubeirou a Mayaki (1) , Michel Riveill (1)

1 MAASAI - Modèles et algorithmes pour l’intelligence artificielle

In this study, we used deep learning based multiple inputs classifier with a Long-short Term Memory (LSTM) autoencoder component to detect medicare fraud. The proposed model is made of two separate block: MLP block and auto encoder feature extraction block. The MLP block extracts high level feature from the invoice data and the auto encoder block extracts high level features from that describes the provider behavior over time. This architecture makes it possible to take into account many sources of data without mixing them. 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. Our results show that baseline artificial neural network give good performances compared to classical machine learning models but they are outperformed by our model.

Mots clés: Medicare fraud; Anomaly detection; Deep learning; Auto encoder; Machine learning

Domaines: Intelligence artificielle [cs.AI]; Statistiques [stat]; Machine Learning [stat.ML]

Mansour Zoubeirou a Mayaki, Michel Riveill. Auto-encoder Based Medicare Fraud Detection. ASPAI 2022 - 4th International Conference on Advances in Signal Processing and Artificial Intelligence, Oct 2022, Corfou, Greece. ⟨hal-03847301⟩