Automation of Analytical Processes through Artificial Intelligence in Financial Instrument Accounting and Enterprise Management

Abstract

The purpose of the article is to substantiate the theoretical and applied foundations of automating analytical processes through artificial intelligence technologies in financial instrument accounting and enterprise management, to identify directions for their integration into analysis, assessment, control, and forecasting procedures, and to develop approaches to the use of intelligent algorithms for generating accounting and analytical information to support managerial decision-making. Methods. The methodological basis of the study is formed by the provisions of International Financial Reporting Standards governing accounting for financial instruments. The research employed system analysis to examine the transformation of analytical processes under the influence of digitalization and artificial intelligence technologies; structural and functional modeling to develop a model for automating analytical processes in financial instrument accounting; comparative analysis to assess the applicability of machine learning technologies, predictive analytics, intelligent data processing, and digital control; and logical generalization to formulate scientific conclusions and determine directions for the practical application of artificial intelligence in enterprise management systems. Results. It is established that the development of the digital economy and the increasing volume of financial data requires a transition from traditional analytical procedures to intelligent models of information processing. It is proved that the use of artificial intelligence technologies in financial instrument accounting ensures the automation of analytical, assessment, and forecasting procedures, improves the timeliness of information processing, and expands the possibilities of financial risk management. It is determined that the application of machine learning algorithms enables the automation of expected credit loss assessment in accordance with IFRS 9 requirements, the analysis of counterparties’ behavioral characteristics, the forecasting of default risk on financial assets, and the identification of anomalies in financial data. The expediency of integrating artificial intelligence technologies with ERP systems, digital data analytics platforms, and tools for continuous control is substantiated. An approach to the automation of analytical processes is proposed, based on the combination of accounting data, intelligent algorithms, and decision-support tools within a unified enterprise information environment. Conclusions. It is substantiated that the use of artificial intelligence technologies in financial instrument accounting contributes to improving the reliability of analytical information, reducing the time required for financial data processing, developing a continuous control system, and improving financial risk management. The practical significance of the research results lies in the possibility of applying the proposed approaches to automate analytical procedures, perform digital assessment of financial instruments, and improve the efficiency of information support for enterprise management.