How Can We Use Artificial Intelligence and Machine Learning in Accounting and Auditing: An Engineering Overview with the Mapping Method in the Field of Business

Today, the industry faces data-related challenges stemming from the use of artificial intelligence. Similarly, sustainability challenges in real world applications also arise. One of these is the accounting and auditing fields. Because of how to use artificial intelligence in this field and what technical approaches are not known enough. This study presents a machine learning approach with engineering and technical view. Because of the technical complexity, the papers on artificial intelligence and machine learning in accounting and auditing are often hard to evaluate and need a clear understanding of critical methodological parts. This paper aims to guide the key methodological aspects of artificial intelligence and machine learning for non-expert researchers of accounting and auditing. The proposed paper draws up the general framework for the use of machine learning, a sub-branch of artificial intelligence, in sustainable accounting studies. The most frequently used classification methods for accounting data and the metrics used in the evaluation of the results have been explained. In addition, the framework of recent studies such as bankruptcy prediction, financial distress, corporate failure, accounting fraud prediction, tax compliance problems, and financial difficulties conducted with accounting and auditing data has been examined. One of the findings obtained from this study is that although many studies in literature mention artificial intelligence with a few words, they decorate the title or keywords of the study with artificial intelligence. Keyword analyses are also among the findings of the study. All findings of the analysis emphasize the increasing importance of artificial intelligence in the field of accounting and auditing and the diversity of research in this field. If the notion of artificial intelligence is one of the essential components of future research, this study advises researchers to use it carefully in the study's title and keywords.