Artificial intelligence models in corporate financial and accounting processes: systematic literature review

The study aims to introduce artificial intelligence models applied in the financial and accounting fields and to describe the commonly used areas, models, and algorithms that have achieved the highest level of accuracy. The methodology focuses on a systematic literature review, utilizing the Scopus Elsevier database. All studies and models are examined by application area. The results suggest that supervised models are the most applied in the accounting and financial field, while the algorithms that have been most used are decision trees, support vector machines, random forests, neural networks, and logistic regressions, employed in specific areas of financial fraud, stock market predictions, and cash flow. Although unsupervised models were not reported to be used, they represent an important scenario for future studies, focused on the classification of tax fraud.