First Advisor
Joleen Kremin
Date of Award
Spring 6-2024
Document Type
Thesis
Degree Name
Bachelor of Arts (B.A.) in Business Administration: Accounting and University Honors
Department
Accountancy
Language
English
Subjects
Audit, Audit Quality, Artificial Intelligence, Risk Assessment, Ethical Considerations
Abstract
This theses explores the transformative potential of artificial intelligence (AI) in the auditing industry, focusing on its implications for audit quality, efficiency, and the evolving role of human auditors. A comprehensive review of relevant literature highlights the integration of AI-driven tools such as machine learning, deep learning, and natural language processing in audit processes, emphasizing their capabilities in enhancing risk assessment, fraud detection, and data analytics.
The experimental design features an innovative approach to compare the efficacy of AI and human auditors in conducting risk analysis for three companies—Blockbuster, Enron, and Lehman Brothers—presented anonymously with pre-bankruptcy financial information. The findings reveal that while AI models provided consistent and comprehensive risk assessments, human auditors displayed a tendency towards conservative evaluations. This underlines the critical role of AI in standardizing audit processes and identifying subtle risk indicators that might be overlooked by human judgment alone.
The analysis underscores the necessity of combining AI's computational strengths with human auditors' strategic insights to improve audit outcomes. Ethical, regulatory, and practical considerations surrounding the adoption of AI in auditing are also examined, advocating for robust frameworks to ensure data privacy, algorithmic transparency, and audit independence.
This theses contributes to the understanding of AI's role in modern auditing, advocating for its responsible integration to enhance audit quality and reliability. As the auditing profession stands at the brink of a technological revolution, this research offers valuable insights into leveraging AI to achieve more accurate, efficient, and insightful audits.
Recommended Citation
Denegri, Andre V., "Risk Analysis in Financial Statements, a Comparative Study of AI vs Human Risk Assessment" (2024). University Honors Theses. Paper 1472.