# Analyzing Business Process Anomalies Using Autoencoders

arXiv: https://arxiv.org/abs/1803.01092 · HTML: https://mlsanity.com/paper/1803.01092/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2018.

## Built on by (most cited first, 7 of 7)

- [Adaptive Online Incremental Learning for Evolving Data Streams](https://mlsanity.com/paper/2201.01633/) (2022)
- [Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction](https://mlsanity.com/paper/2502.10211/) (2025)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [xSemAD: Explainable Semantic Anomaly Detection in Event Logs Using Sequence-to-Sequence Models](https://mlsanity.com/paper/2406.19763/) (2024)
- [Online anomaly detection using statistical leverage for streaming business process events](https://mlsanity.com/paper/2103.00831/) (2021)
- [Detecting Context-Aware Deviations in Process Executions](https://mlsanity.com/paper/2206.05532/) (2022)
- [Natural Language Querying System Through Entity Enrichment](https://mlsanity.com/paper/2410.15753/) (2024)

## Abstract (arXiv)

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.