Exploring accounting and AI using topic modelling

Abstract

Historically, literature suggests that a variety of accounting roles will be replaced by Artificial Intelligence (AI) and related technologies; however, in recent years there is a growing recognition that accounting can in fact harness AI’s potential to add value to organisations. Commentators have highlighted the need for increased research exploring accounting and AI and for accounting scholars to consider multi-disciplinary research in this area. This study uses a form of topic modelling to analyse literature exploring AI and related techniques in an accounting context. Latent Dirichlet Allocation (LDA) has been used to enable probabilistic, machine-based interrogation of large volumes of literature. This study applies LDA to the abstracts of 930 peer-reviewed academic publications from a variety of disciplines to identify the most significant accounting and AI topics discussed in the literature during the period 1990 to 2023. Our findings suggest that prior literature reviews based on more traditional methodologies do not capture a comprehensive picture of accounting and AI research. Eleven topic clusters are identified which provide a comprehensive topology of the extant literature discussing accounting and AI and set out an agenda for future research designed to foster academic progress in the area. It also represents one of the first applications of probabilistic topic modelling to accounting literature.