In tracing the evolution of accounting research, this paper delves into the historical landscape of Artificial Intelligence (AI)-based applications, examining their transformative impact on accounting practices throughout the annals of time. The study utilizes a bibliometric analysis, employing two bibliometric software (i.e., Bibliometrix and VosViewer) as analytical tools. The methodological approach involves a multifaceted process, including a review of relevant literature on AI, extraction of key AI application keywords, Scopus-based search for English articles in accounting journals, and subsequent analysis of the 378 collected articles using the two bibliometric software. Our analysis uncovers a significant timeline in the evolution of AI in accounting, as marked by pivotal moments and transformative applications. Rooted in Turing’s 1950 proposition, AI faced funding constraints until the emergence of business-specific AI systems in the 1980s. Despite steady growth until the late 2010s, limitations in accounting applications persisted. The late 2010s witnessed a seismic shift with disruptive AI applications reshaping accounting practices, including Deep Learning (DL) and Machine Learning (ML). Challenges arose in AI integration within auditing, highlighting nuanced judgment requirements. Recent advancements liberated auditors from manual tasks via AI, big data analytics, and robotics, yet raised ethical and social accountability concerns. Financial reporting analysis evolved with AI-driven predictive modeling and risk identification in textual disclosures. This evolutionary journey emphasizes guidance for present and future accounting researchers, practitioners and policy makers. Thus, researchers should explore beyond Neural Networks (NNs), focus on disruptive tech like ML, DL, and Robotic Process Automation (RPA) for auditing and financial analysis, deepen opportunities and implications around text mining of narrative financial disclosures, conduct longitudinal studies on innovation of single accounting practices, and delve into the ethical implications of AI-based applications. Professionals should foster collaborations, adapt skills to advanced tools, and assess AI-based applications within organizations. Policymakers should promote continuous education initiatives, address ethical concerns, and emphasize transparent, fair AI decision-making algorithms for accounting practices.
Keywords: Accounting, Artificial Intelligence, Bibliometric analysis
Bruno A., D’Amore G., Lepore L. (2024). La corruzione negli studi di matrice economico-aziendale: un’analisi strutturata della letteratura dal 1990 ad oggi. RIREA, 1, 57-59. DOI: 10.17408/RIREAABGDALL010203042024
