Predictive behavior analysis allows prediction of the (human) behavior based on the analysis of historical data. Efficient approaches for predictive behavior analysis are available for scenarios with structured processes (e.g., based on ERP systems). The prediction of behavior becomes an obstacle when unstructured (decision making) processes underlie the scenario. Scenarios with unstructured processes can be found in smart environments logging sensor (event) streams such as e.g., Smart Home or Connected Cars. No efficient solutions exist to identify abnormal behavior (anomalies) in such smart environments. To provide a solution for anomaly detection in unstructured processes we suggest crossing process engineering with deep learning. Methods from process engineering allow identifying deviations while deep learning improves the robustness of anomalie detection and prediction. This conjunction is a promising approach in order to find an efficient solution.