%0 Conference Paper %1 10.1007/978-3-031-23633-4_7 %A Tritscher, Julian %A Schlör, Daniel %A Gwinner, Fabian %A Krause, Anna %A Hotho, Andreas %B Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science %C Cham %D 2023 %I Springer Nature Switzerland %K ERP Outlier XAI anomaly detection %P 79--96 %R https://doi.org/10.1007/978-3-031-23633-4_7 %T Towards Explainable Occupational Fraud Detection %V 1753 %X Occupational fraud within companies currently causes losses of around 5\% of company revenue each year. While enterprise resource planning systems can enable automated detection of occupational fraud through recording large amounts of company data, the use of state-of-the-art machine learning approaches in this domain is limited by their untraceable decision process. In this study, we evaluate whether machine learning combined with explainable artificial intelligence can provide both strong performance and decision traceability in occupational fraud detection. We construct an evaluation setting that assesses the comprehensibility of machine learning-based occupational fraud detection approaches, and evaluate both performance and comprehensibility of multiple approaches with explainable artificial intelligence. Our study finds that high detection performance does not necessarily indicate good explanation quality, but specific approaches provide both satisfactory performance and decision traceability, highlighting the suitability of machine learning for practical application in occupational fraud detection and the importance of research evaluating both performance and comprehensibility together. %@ 978-3-031-23633-4

@inproceedings{10.1007/978-3-031-23633-4_7, abstract = {Occupational fraud within companies currently causes losses of around 5{\%} of company revenue each year. While enterprise resource planning systems can enable automated detection of occupational fraud through recording large amounts of company data, the use of state-of-the-art machine learning approaches in this domain is limited by their untraceable decision process. In this study, we evaluate whether machine learning combined with explainable artificial intelligence can provide both strong performance and decision traceability in occupational fraud detection. We construct an evaluation setting that assesses the comprehensibility of machine learning-based occupational fraud detection approaches, and evaluate both performance and comprehensibility of multiple approaches with explainable artificial intelligence. Our study finds that high detection performance does not necessarily indicate good explanation quality, but specific approaches provide both satisfactory performance and decision traceability, highlighting the suitability of machine learning for practical application in occupational fraud detection and the importance of research evaluating both performance and comprehensibility together.}, added-at = {2023-02-09T08:54:25.000+0100}, address = {Cham}, author = {Tritscher, Julian and Schlör, Daniel and Gwinner, Fabian and Krause, Anna and Hotho, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/2fe5cd4cf1b588e84ca83308b6bfb6924/winkelmann}, booktitle = {Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science}, description = {ECML PKDD 2022: Machine Learning and Principles and Practice of Knowledge Discovery in Databases }, doi = {https://doi.org/10.1007/978-3-031-23633-4_7}, interhash = {81975b1b9e2930db39d99370594a30de}, intrahash = {fe5cd4cf1b588e84ca83308b6bfb6924}, isbn = {978-3-031-23633-4}, keywords = {ERP Outlier XAI anomaly detection}, pages = {79--96}, publisher = {Springer Nature Switzerland}, timestamp = {2023-08-09T16:29:26.000+0200}, title = {Towards Explainable Occupational Fraud Detection}, volume = 1753, year = 2023 }