Auditing II

Introduction

As the auditing profession undergoes significant digital transformation, the role of the auditor has transitioned from basic compliance verification to the management of sophisticated data landscapes. Auditing II builds on the conceptual foundations established in Auditing I, and focuses on the application, analysis, and evaluation of audit principles and techniques in complex and technology-driven business environments. The course is designed to bridge the gap between theoretical audit frameworks and the operational complexities of contemporary, data-intensive organizations. It deepens students’ understanding of the audit process by emphasizing statistical and non-statistical sampling, and auditing of key business processes.

A central element of the course is the increasing role of digital auditing, data analytics, and artificial intelligence (AI) in contemporary audit practice. Students analyze how auditors use audit software, automated testing, continuous auditing techniques, and AI-based tools for risk assessment, anomaly detection, and substantive testing. The course also addresses how digitalization affects professional judgment, audit quality, and ethical decision-making. Particular attention is paid to issues of professional integrity, independence, transparency, and accountability, as well as ethical challenges related to data use and algorithmic bias.

Course content

The course covers the following topics, integrating traditional auditing approaches with digital and ethical perspectives:

• Statistical and non-statistical sampling tools
  • Audit sampling methods and their application
  • Sampling versus full-population testing
  • Sampling in data-intensive and automated environments

• Auditing business processes, including the revenue process, the purchasing and expenditure process, the human resource management and payroll process, the inventory management process, and the financing and investing process. For each process, the focus is on:
  • Understanding process design, governance structures, and identifying inherent and control risks
  • Evaluating internal controls, including IT-dependent and automated controls
  • Applying data analytics and AI-based techniques for control testing and substantive testing
  • Identifying ethical risks, incentives, and behavioral factors that may lead to misstatements or control failures

• Digital auditing, ethics, and audit analytics
  • Use of audit software and data analytics tools
  • Automated audit procedures and continuous auditing concepts
  • AI-based methods for risk assessment and anomaly detection
  • Ethical challenges related to data quality, bias, privacy, transparency, and explainability

Disclaimer

This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.