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The nature and amount of data that public organizations have to monitor to counteract corruption lead to a phenomenon called “KPI overload”, consisting of the business analyst feeling overwhelmed by the amount of information and resulting in the absence of appropriate control. The purpose of this study is to develop a solution based on Artificial Intelligence technology to avoid data overloading and, at the same time, under-controlling in business process monitoring.

The authors adopted a design science research approach. The authors started by observing a specific problem in a real context (a healthcare organization); then conceptualized, designed and implemented a solution to the problem with the goal to develop knowledge that can be used to design solutions for similar problems. The proposed solution for business process monitoring integrates databases and self-service business intelligence for outlier detection and artificial intelligence for classification analysis.

The authors found the solution powerful to solve problems related to KPI overload in process monitoring. In the specific case study, the authors found that the combination of Business Intelligence and Artificial Intelligence can provide a significant contribution to the detection of fraud, corruption and/or policy misalignment in public organizations.

The authors provide a big-data-based solution to the problem of data overload in business process monitoring that does not sacrifice any monitored Key Performance Indicators and that also reduces the workload of the business analyst. The authors also developed and implemented this automated solution in a context where data sensitivity and privacy are critical issues.