Implementation of artificial intelligence technologies in corporate finance: classification by spheres of activity - Tutueva Darya; Viktorova Natalya; Sayakbaeva Ayganysh (Pi-Economy, 2022)
The article considers the meaning of artificial intelligence technologies, such as machine learning, deep learning, natural language processing plus robotic process automation and data analytics. This is done for understanding of AI technologies functions in their implementation in corporate finance of an enterprise. According to the literature review, machine learning is mainly used in reconciliation, validation, analysis, and forecasting; RPA and intelligence process automation – for KPI management, transaction processes and compliance; NLP – for speech and text recognition in different operations. Based on the analysis of the technological development prospects of the largest financial companies, consulting bodies, professional associations, and educational structures, such as ICAEW, CPA Australia, CPA Canada, ACCA and SigmaIQ, the relationship between AI technologies and business processes of such companies is systematized. A classification is proposed in relation to the introduction of AI technologies in the areas of corporate finance in the financial sector and specific tasks to be solved in each area. Namely, in the areas of: accounting (accounts receivable management, accounts payable control, reporting, financial planning and analysis), external finance processes, internal audit, forensic accounting and fraud detection, work of CFO. The main benefits of such technological implementation are also defined for three groups: efficiency, control and decision-making. Significance of the study is expressed in the most concrete classification of AI technologies implementation in corporate finance, which allows harnessing the benefits specified. However, the article does not cover such topics as prerequisites, problems of implementation and their solution, because these questions were discussed in previous articles of the authors. Future research could be viewed as description of real implementation evidence in business environment, complicated with constraints of the economic reality.