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Business Process Deviation Prediction: Predicting Non-Conforming Process Behavior

Michael Grohs; Peter Pfeiffer; Jana-Rebecca Rehse
In: 2023 5th International Conference on Process Mining (ICPM). International Conference on Process Mining (ICPM-2023), Pages 113-120, IEEE, 2023.


The compliance of business processes is crucial for the success of organizations. To ensure it, process managers identify and mitigate deviations between the predefined process behavior and the executed process instances. Approaches that can predict such deviations in running process instances before they occur enable companies to proactively enforce process compliance. However, existing techniques cannot predict the exact deviation type or cope with the imbalanced nature of this prediction task. In this paper, we propose Business Process Deviation Prediction (BPDP), a novel predictive approach that relies on a supervised machine learning model to predict which deviations can be expected in the future of running process instances. Our evaluation shows that BPDP outperforms existing methods in predicting which deviation will occur. Further, we identify process characteristics that influence the likelihood for a deviation. Following the idea of action-oriented process mining, BPDP thus enables process managers to prevent deviations in early stages of running process instances.