  Process-aware Recommender systems (PAR systems) are information systems that
aim to monitor process executions, predict their outcome, and recommend
effective interventions to reduce the risk of failure. This paper discusses
monitoring, predicting, and recommending using a PAR system within a financial
institute in the Netherlands to avoid faulty executions. While predictions were
based on the analysis of historical data, the most opportune intervention was
selected on the basis of human judgment and subjective opinions. The results
showed that, while the predictions of risky cases were relatively accurate, no
reduction was observed in the number of faulty executions. We believe that this
was caused by incorrect choices of interventions. While a large body of
research exists on monitoring and predicting based on facts recorded in
historicaldata, research on fact-based interventions is relatively limited.
This paper reports on lessons learned from the case study in finance and
proposes a new methodology to improve the performances of PAR systems. This
methodology advocates the importance of several cycles of interactions among
all actors involved so as to develop interventions that incorporate their
feedback and are based on insights from factual, historical data.
