Abstract:

Information technology (IT) audit focuses on auditing companies’ IT systems and processes. The systems companies use are getting more complicated and better integrated. This means more data also needs to be audited. An IT audit often requires performing repetitive manual tasks, which makes IT audits more labor-intensive and costly. Current technological advancements have immense potential for improving an IT audit’s performance, quality, and accuracy. Therefore, by leveraging advanced data processing and analysis technology, this workload can be lowered, allowing the auditing process to be performed effectively and efficiently with higher-quality outcomes. To achieve this objective, a systematic literature review (SLR) has been conducted to identify studies that use artificial intelligence (AI), machine learning (ML), predictive analytics, process mining, and natural language processing (NLP) techniques applied within IT auditing. Process mining is seen to have emerged as the most commonly used technique among the analyzed studies. The studies also reveal that combining techniques such as process mining and data mining, natural language processing, and machine learning enables effective and efficient audit processes by conducting continuous, automated, or online auditing work. The application of these new techniques in the examined studies are seen to generally provide solutions regarding the audit’s testing stage. Overall, the study reveals a limited number of academic studies to have examined how these techniques are implemented into IT audits.

Keywords: