Abstract
Artificial intelligence can greatly assist accounting professionals in information management, streamlining processes, and improving overall efficiency. AI-powered software can automate data entry tasks by extracting relevant information from documents, such as invoices, receipts, and bank statements. AI algorithms can analyze financial data and identify patterns or anomalies indicative of fraud or financial irregularities. AI can analyze historical financial data and market trends to provide predictive insights, helping businesses anticipate future financial challenges and opportunities. Human accountants will continue to play a vital role in interpreting AI-generated insights, making strategic decisions, and maintaining a human touch in client interactions. Based on the above, it is intended to systematically review the bibliometric literature on how artificial intelligence can help accounting in information management using the Scopus database to analyse 77 academic and/or scientific documents
TopMethodological Approach
The systematic bibliometric literature review (LRSB) methodology was used to collect and synthesize data. The researcher used this methodology since it provides a rigorous and structured approach to assessing the existing scholarly work on the specific study topic (Linnenluecke et al., 2019). LRSB provides a comprehensive overview of the academic landscape by systematically identifying, collecting, and analyzing relevant academic publications, thereby allowing the researcher to identify key trends, emerging research themes, and gaps in the literature. Unlike traditional literature review, LRSB adopts a replicable, scientific, and transparent process that helps minimize bias by exhaustively searching for published and unpublished literature regarding the study topic (Rosário, & Dias, 2023; Rosário, et al.,2023; Raimundo & Rosário, 2022). Moreover, the researcher provides an audit trail that helps readers assess the quality of the studies synthesized in the research and the procedures and conclusions.
Therefore, the LRSB involves the screening and selection of information sources to ensure the validity and accuracy of the data presented, in a process consisting of 3 phases and 6 steps (Rosário & Dias, 2023; Rosário, et al., 2023; Raimundo & Rosário, 2022), (Table 1).
Key Terms in this Chapter
Artificial Intelligence: The multidisciplinary study that covers several areas of knowledge. Although its development has advanced further in computer science, its interdisciplinary approach involves contributions from several disciplines.
Expert Systems: Software that simulates the reasoning of an “expert” professional in a specific area of knowledge.
Optical Character Recognition: The process that converts an image of text into a machine-readable text format.
Applying Natural Language Processing: Intention and simplification of understanding of human behavior by machines.
Deep Learning: Machine learning technique that teaches computers to do what comes naturally to humans.
AI-Driven: The process that is powered by the capabilities of artificial intelligence.
Machine Learning: Computer science that focuses on using data and algorithms to mimic the way humans learn.