Assessing the Application of Artificial Intelligence in the Financial Sector under Strategic Uncertainty
DOI:
https://doi.org/10.58423/2786-6742/2026-12-255-271Keywords:
artificial intelligence, financial sphere, strategic uncertainty, metrics of maturity, metrics for the effectiveness of using artificial intelligenceAbstract
The relevance of assessing the application of artificial intelligence (AI) in the financial sector is driven by the growing impact of its maturity on the effectiveness of financial decision-making, risk management, cost reduction, and the maintenance of financial resilience under conditions of digitalization and high uncertainty. Existing methodological support for evaluating the application of AI remains fragmented, with disparate and incomplete approaches, which leads to difficulties in the practical use of such assessments in strategic planning. Therefore, the purpose of this article is to develop a methodological approach to assessing the maturity and performance of AI application in the financial sector. The study of the digital transformation of the financial sector through the use of AI is based on uncertainty theory combined with the concepts of strategic and institutional isomorphism, dynamic capabilities, the VUCA framework, ecosystem theory, and the platform economy. The research methodology is grounded in a systems approach adapted to the environment of the digital economy. The assessment of AI application in the financial sector is characterised as a dominant element in analyzing the level of technological maturity of businesses and the effectiveness of digital transformation, particularly the depth of AI integration into business processes and its impact on efficiency and competitiveness of the economic system. The theoretical foundation of this analysis is formed by qualimetric theory, evaluation logic, and multi-criteria utility theory. A comprehensive assessment of AI application is based on the use of resource-based, factor-based, performance-based, value-based, and integrated approaches. A comprehensive two-component evaluation system for AI application is proposed, encompassing maturity and performance dimensions, along with a multi-layer structure that includes an integral indicator and partial indicators for each component. The system of partial indicators incorporates both quantitative and qualitative metrics. The developed system for assessing the performance of AI application consists of four groups of metrics: financial, operational, customer-related, and risk-related.
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