THEORETICAL FOUNDATIONS FOR USING ARTIFICIAL INTELLIGENCE IN ASSESSING CORPORATE FINANCIAL STABILITY
DOI:
https://doi.org/10.5281/zenodo.22834793Abstract
The increasing complexity of corporate financial activities requires advanced approaches to assessing financial stability. This study examines the theoretical foundations of using artificial intelligence (AI) to assess corporate financial stability as a multidimensional phenomenon encompassing liquidity, solvency, profitability, efficiency, cash-flow stability, capital structure, working capital, and financial risk. Particular attention is given to machine learning (ML) and explainable AI (XAI) for identifying complex patterns and enhancing assessment transparency. The study proposes an integrated conceptual framework combining financial indicators, AI-based assessment, XAI, and early-warning mechanisms, providing a theoretical basis for future empirical research and application in Uzbekistan.Keywords
artificial intelligence, machine learning, corporate financial stability, explainable artificial intelligence, early-warning systemReferences
Altman, E.I. (1968), “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”,
The Journal of Finance, 23(4), pp. 589-609.
Černevičienė, J. and Kabašinskas, A. (2024), ‘Explainable artificial intelligence (XAI) in finance: a systematic
literature review’, Artificial Intelligence Review, 57, 216.
El Madou, K., Marso, S., El Kharrim, M. and El Merouani, M. (2024), “Evolutions in machine learning
technology for financial distress prediction: a comprehensive review and comparative analysis”, Expert
Systems, 41(2), e13485.
International Monetary Fund (2006), Financial Soundness Indicators: Compilation Guide, Washington, DC:
International Monetary Fund.
Weber, P., Carl, K.V. and Hinz, O. (2024), ‘Applications of Explainable Artificial Intelligence in Finance - a
systematic review of Finance, Information Systems, and Computer Science literature’, Management Review
Quarterly, 74, pp. 867–907.
Zhao, J., Ouenniche, J. and De Smedt, J. (2024), ‘Survey, classification and critical analysis of the literature
on corporate bankruptcy and financial distress prediction’, Machine Learning with Applications, 15, 100527.
President of the Republic of Uzbekistan (2024). Resolution No. RP-358 “On the Approval of the Strategy
for the Development of Artificial Intelligence Technologies until 2030”, 14 October 2024. National Legislation
Database.
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