Learning from Machine Learning in Accounting and Assurance

Abstract

Machine learning is a subset of artificial intelligence, and it is a computational method that learns patterns from large and complex data. The learning processes enable us to make predictions for future events. In the accounting and assurance profession, machine learning is gradually being applied to various tasks like reviewing source documents, analyzing business transactions or activities, and assessing risks. In academic research, machine learning has been used to make predictions of fraud, bankruptcy, material misstatements, and accounting estimates. More importantly, machine learning is generating awareness about the inductive reasoning methodology, which has long been undervalued in the mainstream of academic research in accounting and auditing. The use of machine learning in accounting/auditing research and practice is also raising concerns about its potential bias and ethical implications. Therefore, this editorial aims to call the readers' attention to these issues and encourage scholars to perform research in this domain.