Raffaele Conforti



I am a PhD student from the School of Information Systems at the Queensland University of Technology, Australia. I am conducting research in the area of business process management, with respect to business process automation and process mining. In particular my research focuses on automatic detection, prevention and mitigation of process-related risks during the execution of business processes.


This paper proposes a technique that supports process participants in making risk-informed decisions, with the aim to reduce the process risks. Risk
reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a process exposed to risks, e.g. a financial process
exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting
the next task to execute or by filling out a form, we prompt the participant with the expected risk that a given fault will occur given the particular
input. These risks are predicted by traversing decision trees generated from the logs of past process executions and considering process data, involved resources, task durations and contextual information like task frequencies. The approach has been implemented in the YAWL system and its effectiveness evaluated. The results show that the process instances executed in the tests complete with significantly fewer faults and with lower fault severities, when taking into account the recommendations provided by our technique.