Using Autoencoders for Data-Driven Analysis in Internal Auditing

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dc.contributor.authorNonnenmacher, Jakob
dc.contributor.authorKruse, Felix
dc.contributor.authorSchumann, Gerrit
dc.contributor.authorMarx Gómez, Jorge
dc.date.accessioned2020-12-24T20:12:03Z
dc.date.available2020-12-24T20:12:03Z
dc.date.issued2021-01-05
dc.description.abstractNew challenges in internal auditing are created as all areas of companies are digitalized. These challenges are forcing internal auditing to implement more and more data-driven procedures. Auditing is increasingly using artificial intelligence methods such as neural networks to overcome these challenges. Since in internal auditing labels are usually not available at the beginning of an audit engagement, unsupervised methods have to be used. We used autoencoders as an unsupervised method, which we evaluated for its use in auditing in a practical case study with an international automobile manufacturer. For the case study, two real-world, non-financial data sets from production-related processes were provided. The results of the case study show that the use of autoencoders can support auditors in the audit execution and in the audit planning process step to improve the quality of the internal audit engagement.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.697
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/71317
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Control, and Risk Management
dc.subjectauditing
dc.subjectautoencoder
dc.subjectdeep learning
dc.subjectinternal audit
dc.subjectunsupervised
dc.titleUsing Autoencoders for Data-Driven Analysis in Internal Auditing
prism.startingpage5748

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