Gierbl, Anita StefanieAnita StefanieGierbl2023-04-132023-04-132021https://www.alexandria.unisg.ch/handle/20.500.14171/111013This dissertation examines the use of data analytics in external auditing. Research findings, current practice, and future applications are investigated to evaluate the potential of data analytics for auditing. The findings indicate that "conventional data analytics” methods (rule-based queries, visualization, and descriptive statistics) are widely used in the auditing industry today, while 'advanced data analytics' approaches (machine learning, process mining and natural language processing) are in its infancy stage. The primary exceptions are process mining and natural language processing which are enjoying some early successes. Examples of future use cases in the field of machine learning are journal entry testing (clustering, adversarial autoencoder neural networks, association rules), predictions for analytical procedures, and audit workforce planning. Overall, external auditing is a promising field for the application of data analytics. Successful adoption will have implications on the audit market (e.g., adoption of data driven audits in phases), the audit profession (e.g., extension of skillset) and the regulator (e.g., ensuring adequate application).enKünstliche IntelligenzBig DataDatenanalyseWirtschaftsprüfungFinancial AuditingJahresabschlussprüfungRevision <Wirtschaft>EDIS-5044doctoral thesis