CAiSE’13

Short-bio

Dr. Moe Thandar Wynn is a researcher in the field of Business Process Management (BPM) within the school of Information Systems at Queensland University of Technology. Her research interests include cost-aware BPM, risk-aware BPM, process automation, and process mining.  She works on interdisciplinary research projects (e.g., healthcare, insurance). She has published over 35 refereed papers and her work is currently supported by over AUD $1.14 million in grant funding.

Abstract

Risk identifi cation is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little
guidance and companies often rely on the knowledge of experts for risk identi fication. In this paper we demonstrate how risk indicators can be used
to predict process delays via a method for confi guring so-called Process Risk Indicators (PRIs). The method learns suitable con figurations from past
process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company.