Using Interpretable Machine Learning for Accounting Fraud Detection – A Multi-User Perspective

Leonhard J. Lösse and Barbara E. Weißenberger

Die Unternehmung - Swiss Journal of Business Research and Practice, 2023, vol. 77, issue 2, 113-133

Abstract: Machine learning models are increasingly used to identify accounting manipulations based on disclosed information. Still, most approaches focus on accuracy, which at the same time leads to a high number of false-positive predictions that practically hinder their application. The paper analyzes the need for interpretable machine learning techniques from the perspective of primary users such as statutory auditors, enforcement institutions, or investors with respect to their specific legal and organizational frameworks. From this, requirements for additional explanations are derived, which in turn serve as indicators for plausibility checks or as starting points for investigations, thus improving the manageability of predictions and promoting the implementation of the models.

Date: 2023
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DOI: 10.5771/0042-059X-2023-2-113

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Die Unternehmung - Swiss Journal of Business Research and Practice is currently edited by Prof. Dr. Artur Baldauf, Universität Bern, Prof. Dr. Manfred Bruhn, Universität Basel, Prof. Dr. Pascal Gantenbein, Universität Basel (geschäftsführend), Prof. Dr. Markus Gmür, Universität Fribourg, Prof. Dr. Klaus Möller, Universität St. Gallen, Prof. Dr. Günter Müller-Stewens, Universität St. Gallen, Prof. Dr. Dr. h.c. Margit Osterloh, Universität Zürich, Prof. Dr. Dieter Pfaff, Universität Zürich and Prof. Dr. Martin Wallmeier, Universität Fribourg

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