Analyzing Business Process Anomalies Using Autoencoders

TL;DR

This paper presents an autoencoder-based method for detecting and analyzing anomalies in business process execution, effective on noisy datasets and outperforming existing methods in accuracy.

Contribution

The paper introduces a novel autoencoder approach for business process anomaly detection that does not require prior process knowledge and handles noisy data effectively.

Findings

Achieved an F1 score of 0.87 on diverse datasets.

Outperformed three state-of-the-art anomaly detection methods.

Enabled detailed analysis of anomalies at the event level.

Abstract

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.