  Businesses are naturally interested in detecting anomalies in their internal
processes, because these can be indicators for fraud and inefficiencies. Within
the domain of business intelligence, classic anomaly detection is not very
frequently researched. In this paper, we propose a method, using autoencoders,
for detecting and analyzing anomalies occurring in the execution of a business
process. Our method does not rely on any prior knowledge about the process and
can be trained on a noisy dataset already containing the anomalies. We
demonstrate its effectiveness by evaluating it on 700 different datasets and
testing its performance against three state-of-the-art anomaly detection
methods. This paper is an extension of our previous work from 2016 [30].
Compared to the original publication we have further refined the approach in
terms of performance and conducted an elaborate evaluation on more
sophisticated datasets including real-life event logs from the Business Process
Intelligence Challenges of 2012 and 2017. In our experiments our approach
reached an F1 score of 0.87, whereas the best unaltered state-of-the-art
approach reached an F1 score of 0.72. Furthermore, our approach can be used to
analyze the detected anomalies in terms of which event within one execution of
the process causes the anomaly.
