Critical issues of the audit expectation gap in the era of audit digitalisation

Abstract

This study aims to conceptually examine how a paradigm shift from traditional audits to digital audits could impact critical and recurring issues of the expectation gap. The findings indicate that digital platforms such as data analytics systems supported by machine learning can facilitate the identification of anomalies in data which can be investigated manually by auditors. Also, big data can be used in the audit process to interrogate an entire population of journal entries, transactions, and unstructured data, enabling auditors to focus on transactions displaying unusual patterns identified through artificial intelligence. Furthermore, drones could equally facilitate stock counts, compliance, and the attainment of operational objectives. In a nutshell, existing extant literature and commentaries underscore the significance of these digital technologies in enhancing internal controls and facilitating fraud prevention and detection. This study further contributes to the extant literature by projecting new avenues where the expectation gap is likely to emerge due to a paradigm shift from traditional audits to digital audits, enabling the auditing profession to take pre-emptive measures to prevent the exacerbation of an already worsening trust and confidence in the audit profession by financial statement users.