What if Process Predictions are not followed by Good Recommendations? (Technical Report)
This addresses the issue of ineffective recommendations in process-aware systems for financial institutions, but it is incremental as it builds on existing prediction methods by focusing on intervention improvement.
The paper tackled the problem of Process-aware Recommender systems (PAR) failing to reduce faulty executions despite accurate risk predictions, due to suboptimal intervention choices based on human judgment rather than data. The result was no observed reduction in faulty executions, and the authors propose a new methodology involving iterative feedback cycles to improve interventions.
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.