CONCEPTUAL APPROACHES TO THE FORMATION OF AN ADAPTIVE-PROGNOSTIC PARADIGM OF ENTERPRISE FINANCIAL MANAGEMENT

Abstract

The article presents conceptual approaches to the formation of an adaptive and predictive paradigm of enterprise financial management, which is gaining particular relevance under conditions of growing economic turbulence, digital transformation, and geopolitical instability. Traditional financial management models based on fixed budgets, retrospective analysis, and standardized procedures are no longer capable of ensuring flexible responses to dynamic changes in the external environment. In this context, the study aims to justify and structure a new management paradigm that integrates predictive analytics, digital technologies, adaptive strategies, and a systemic approach to enterprise finance. The research outlines five key components of the adaptive-predictive financial system: analytical (collection and processing of big data), predictive (scenario modeling, probabilistic forecasting), adaptive (flexible budgeting, KPI monitoring), integrative-technological (implementation of ERP, BI, cloud platforms), and organizational (coordination, decentralization, information flow management). The tools enabling each component are detailed, including GARCH, ARIMA, machine learning models, neural networks, decision support systems (DSS), and real-time analytics. Special attention is paid to the role of digital technologies in transforming financial functions, particularly the possibilities for automation, complex risk profiling, unstructured data analysis, and the creation of integrated analytical platforms. The article emphasizes the need to develop a new managerial culture focused on rapid response, cross-functional collaboration, development of digital competencies, and data-driven decision-making. A number of unresolved issues are identified, including the lack of unified approaches to evaluating the effectiveness of adaptive-predictive systems, underdeveloped risk management models using AI, and barriers to implementation in organizations with traditional hierarchical structures. The findings of the study may serve as a foundation for developing industry-specific models of adaptive financial management and enterprise digital transformation.