Title: If humans fail, machines take action: assessment of accounting error detection using machine learning

Authors: Marius Gros; Anika Hanke

Addresses: Hochschule Niederrhein Niederrhein University of Applied Sciences, Reinarzstr. 49, 47805 Krefeld, Germany ' Goethe University Frankfurt, Theodor-W.-Adorno-Platz 4, 60323 Frankfurt am Main Germany

Abstract: Various accounting scandals have documented that current enforcement systems may fail to detect erroneous reporting or identify fraudulent actions. Therefore, enforcement systems should continuously reassess and update the applied methods. Existing studies have shown that textual information and machine learning techniques can substantially support accounting error detection. We use proprietary data from enforcement investigations conducted by the German enforcement system to gain insights into the feasibility of such applications. Our classification models consider a wide range of classification and feature selection methods based on three indicator categories: financial, linguistic, and content. In contrast to many previous studies, we evaluate the classification performance using a realistic imbalanced holdout sample. We observe that content features are particularly important error indicators, and the combination of indicator categories has a significant impact on error detection. Additionally, feature selection plays an essential role in preventing indicator overload.

Keywords: enforcement; error detection; feature selection; synthetic minority oversampling technique; SMOTE; topic modelling.

DOI: 10.1504/IJEA.2022.130411

International Journal of Economics and Accounting, 2022 Vol.11 No.4, pp.375 - 406

Received: 20 Sep 2022
Accepted: 12 Oct 2022

Published online: 19 Apr 2023 *

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