Economic aspects of artificial intelligence implementation in financial monitoring systems of large enterprises

Abstract

The article analyzes the economic efficiency of using artificial intelligence technologies in financial monitoring systems of large corporations. Mechanisms for reducing compliance costs, improving detection accuracy of anomalous transactions, and optimizing internal control resources are examined. Hidden costs of ML model implementation and regulatory requirements for automated decision-making are assessed. Practical significance lies in substantiating criteria for choosing between rule-based and predictive algorithms, considering business scale.