Process Mining (PM) enhances the evaluation of the internal control system with corresponding tests of controls due to a comprehensive analysis of variants within business processes. It can be used by external and internal auditors to improve audit efficiency, effectiveness, and quality. Nevertheless, PM is still not an industry-wide best-practice standard, especially due to existing implementation barriers for practitioners. This study utilizes Design Science Research (DSR) to develop the Audit Process Mining (APM) Framework to implement PM into audit tasks and overcome existing implementation barriers. The APM Framework was designed using empirical insights extracted from interviews with subject matter experts across various audit firms and large industrial companies. Furthermore, 19 auditing professionals confirmed that the APM Framework is a valid and verified solution.

Recent advancements in Deep Learning (DL) yield to significant breakthroughs in solving complex audit tasks, traditionally reserved for subject matter experts. While economies of scale associated with DL are high, its widespread adoption within the audit profession faces substantial implementation barriers. Notably the lack of comprehensive guidelines for DL results in reluctance to embrace DL, confining its practical application largely to realms of research and development. Our study introduces a DL-framework to navigate these challenges and to provide auditors with overarching guidance for the application and implementation of DL, informed by focus group discussions and expert interviews. This DL-framework, rigorously evaluated for reliability and efficacy, bridges the gap between DL's theoretical promise and its practical application in both research and audit settings.

ABSTRACT The study at hand develops the Audit and Assurance Value Chain as a structured framework with four categories: accessing information and data, verifying information, protecting information, and assessing internal controls. After development of the Audit and Assurance Value Chain, it was utilized to categorize emerging technologies derived out of investigations of more than 100 technology innovator companies and interviews with 23 organizations comprising audit and assurance practitioners, regulators, associations, and national standard-setters. The study is based on global data coverage to identify emerging technologies that have not been connected to audit and assurance services before, bringing new insights to the field. Additionally, it emphasizes the importance of the International Auditing and Assurance Standards Board convening power and influence in promoting the adoption of emerging technologies. JEL Classification: M40; M41; M42; O30; O32; O33.

Ensuring the validity and reliability of the economic activities communicated in sustainability reports is essential to comply with the exposure draft of the International Standard on Sustainability Assurance 5000. In recent years, a significant technological transformation has been observed, attributable to the far-reaching functionalities associated with Large Language Models (LLMs). This study combined LLMs with sustainability report auditing to develop a sustainability-related audit prompt framework. Using a case study, we demonstrated the usefulness of the developed framework, analyzing sustainability reports to automatically verify and validate the sustainability of economic activities described within them. The integration of LLMs signifies the advent of a new era in auditing of sustainability reports.