Anita Gierbl

The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement ('true and fair presentation'). International audit standards require the assessment of a statements' underlying accounting relevant transactions referred to as 'journal entries' to detect potential misstatements. To efficiently audit the increasing quantities of such journal entries, auditors regularly conduct an 'audit sampling' i.e. a sample-based assessment of a subset of these journal entries. However, the task of audit sampling is often conducted early in the overall audit process, where the auditor might not be aware of all generative factors and their dynamics that resulted in the journal entries in-scope of the audit. To overcome this challenge, we propose the use of a Vector Quantised-Variational Autoencoder (VQ-VAE) neural networks to learn a representation of journal entries able to provide a comprehensive 'audit sampling' to the auditor. We demonstrate, based on two real-world city payment datasets, that such artificial neural networks are capable of learning a quantised representation of accounting data. We show that the learned quantisation uncovers (i) the latent factors of variation and (ii) can be utilised as a highly representative audit sample in financial statement audits.

This 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).