APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN CORPORATE FINANCIAL MANAGEMENT: AN APPLIED ASPECT

Abstract

The article explores the issue of integrating artificial intelligence (AI) technologies and machine learning (ML) algorithms into corporate financial management systems of real-sector enterprises. In the context of macroeconomic instability and high market volatility, traditional tools for financial control and planning, including spreadsheets and basic ERP modules, are gradually becoming insufficient to support timely and well-grounded managerial decisions. These conventional approaches rely primarily on retrospective analysis and deterministic linear extrapolation, which may limit the ability of corporate management to process large arrays of unstructured information (Big Data) and identify hidden, non-linear relationships. Accordingly, there is a growing scientific and practical need to move from reactive financial monitoring toward proactive predictive management. The primary aim of the research is to substantiate the practical utility of AI instruments and demonstrate how these technologies can be applied in the operational activities of financial managers.

The methodological framework combines analysis and synthesis, systematisation, comparative analysis, qualitative content analysis, and the case study method applied to publicly available corporate implementation cases.

The study highlights that the practical value of artificial intelligence in corporate finance may be manifested primarily in three key domains. First, predictive liquidity forecasting and the prevention of cash gaps. By using neural network architectures such as Long Short-Term Memory (LSTM), enterprises can analyse cash flows as complex time series and adapt financial forecasts to macroeconomic fluctuations and internal production cycles, which may help identify liquidity risks before a cash gap occurs. Second, accounts receivable management and counterparty scoring. The integration of Natural Language Processing (NLP) and machine learning enables real-time intelligent scoring, continuous monitoring of the information environment, and adaptive credit limit management, thereby contributing to the reduction of bad debt risks. Third, fraud monitoring and internal auditing. AI can strengthen the audit function by supporting continuous auditing, applying anomaly detection algorithms and autoencoders to identify multidimensional deviations and reduce the probability of financial losses caused by internal threats or phishing attacks.

Despite the considerable potential of AI identified in recent studies, the article outlines significant infrastructural challenges that may hinder its widespread corporate adoption. These barriers include the poor quality of primary historical data, commonly described by the “garbage in, garbage out” principle, the psychological and managerial difficulty associated with the “Black Box Problem”, and substantial investment constraints related to IT infrastructure and data science expertise.

Future research should focus on developing accessible and adaptive ML models for small and medium-sized enterprises (SMEs), improving the explainability of AI-based financial decisions.