Autoencoder Neural Networks versus External Auditors: Detecting Unusual Journal Entries in Financial Statement Audits

dc.contributor.author Schultz, Martin
dc.contributor.author Tropmann-Frick, Marina
dc.date.accessioned 2020-01-04T08:18:31Z
dc.date.available 2020-01-04T08:18:31Z
dc.date.issued 2020-01-07
dc.description.abstract With the increasing complexity of business processes in today's organizations and the ever-growing amount of structured accounting data, identifying erroneous or fraudulent business transactions and corresponding journal entries poses a major challenge for public accountants at annual audits. In current audit practice, mainly static rules are applied which check only a few attributes of a journal entry for suspicious values. Encouraged by numerous successful adoptions of deep learning in various domains we suggest an approach for applying autoencoder neural networks to detect unusual journal entries within individual financial accounts. The identified journal entries are compared to a list of entries that were manually tagged by two experienced auditors. The comparison shows high f-scores and high recall for all analyzed financial accounts. Additionally, the autoencoder identifies anomalous journal entries that have been overlooked by the auditors. The results underpin the applicability and usefulness of deep learning techniques in financial statement audits.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.666
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/64408
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Control Systems, Business Risks
dc.subject accounting information systems
dc.subject auditing
dc.subject autoencoder neural networks
dc.subject journal entry testing
dc.title Autoencoder Neural Networks versus External Auditors: Detecting Unusual Journal Entries in Financial Statement Audits
dc.type Conference Paper
dc.type.dcmi Text
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