The use of artificial intelligence to analyze and optimize financial flows

М. Р. Miakisheva ()

Entrepreneur’s Guide, 2025, vol. 18, issue 1

Abstract: The article systematizes modern ideas about the features of using artificial intelligence tools in order to analyze and optimize financial flows. The relevance of the topic is argued by the rapid growth in the volume of transactions in the global economy, combined with the inability of traditional methods to provide truly effective processing of multidimensional dynamic data in real time. In the current conditions, there is an urgent need to develop new approaches to managing cash flows, primarily based on artificial intelligence technologies. The purpose of the study is to systematize the theoretical and methodological basis for the use of AI in the analyzed area, as well as to identify specific advantages and limitations (in relation to this, the author’s view of the situation is proposed, which it is advisable to consider as a starting point for subsequent research to determine checks and balances for the use of artificial intelligence). In the scientific literature, there are contradictions between theoretical models of using AI and the practical possibilities of their implementation, as well as disagreements in evaluating the effectiveness of various types of neural networks for financial forecasting. The issues of information security and legal regulation in this area have not been sufficiently studied. It has been established that the most promising areas are the use of deep neural networks for time series analysis, reinforcement learning methods in order to optimize management decisions, and the introduction of natural language processing technologies for working with unstructured financial documents. The importance of graph tools in detecting suspicious patterns of funds movement and preventing fraudulent actions is emphasized. The article is of interest to analysts, specialists in the field of artificial intelligence, heads of financial departments.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ahc:journl:y:2025:id:2060

DOI: 10.24182/2073-9885-2025-18-1-15-23

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