New 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.

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