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This work presents a fraud and abuse detection framework for streaming services by modeling user streaming behavior. The goal is to discover anomalous and suspicious incidents and scale the investigation efforts by creating models that characterize the user behavior. We study the use of semi-supervised as well as supervised approaches for anomaly detection. In the semi-supervised approach, by leveraging only a set of authenticated anomaly-free data samples, we show the use of one-class classification algorithms as well as autoencoder deep neural networks for anomaly detection. In the supervised anomaly detection task, we present a so-called heuristic-aware data labeling strategy for creating labeled data samples. We carry out binary classification as well as multi-class multi-label classification tasks for not only detecting the anomalous samples but also identifying the underlying anomaly behavior(s) associated with each one. Finally, using a systematic feature importance study we provide insights into the underlying set of features that characterize different streaming fraud categories. To the best of our knowledge, this is the first paper to use machine learning methods for fraud and abuse detection in real-world scale streaming services.