Abstract: This article explores the effectiveness of various working capital management models in the context of heightened economic uncertainty. Through theoretical analysis, model classification, and industry-based evaluation, the study compares traditional static approaches with adaptive frameworks such as scenario-based planning, stochastic simulations, and real options. A conceptual model is developed to demonstrate the integration of internal and external indicators into decision-making processes. Empirical data from five industries illustrate measurable gains in liquidity, cost efficiency, and cash conversion cycle reduction linked to model sophistication. Additionally, the article identifies key enablers of successful implementation, including cross-functional integration and digital infrastructure, while also addressing model limitations and risks associated with data quality and overreliance. The findings underscore the strategic importance of selecting context-appropriate working capital models and embedding them within an agile financial management system.

Abstract: The acceleration of digital platform development and ecosystem-based business models is fundamentally reshaping corporate financial strategies. This paper investigates how companies reconfigure their financial priorities and operational frameworks in response to increasing digital interconnectedness, real-time information exchange, and the strategic importance of intangible assets. The analysis includes a structured overview of transformation areas, a staged model of financial maturity, and a discussion of enabling technologies and organizational capabilities. The findings suggest that ecosystem-integrated, data-driven, and agile financial strategies are critical for ensuring strategic adaptability and long-term value creation in the digital economy.

Abstract: This article presents a comprehensive comparative analysis of liquidity and current asset management practices in Russian and international corporate environments. The study investigates the structural, regulatory, and technological differences that shape financial decision-making across jurisdictions. Particular emphasis is placed on the role of real-time data, risk-adjusted metrics, digital tools, and institutional frameworks in enhancing liquidity efficiency. The paper introduces a multi-level analytical model supported by visual schemes and highlights the strategic implications for governance and treasury transformation. It concludes that digital integration, regulatory harmonization, and financial agility are key to optimizing liquidity performance in a globalized financial context.

Abstract: This article provides a comprehensive examination of the role of cognitive factors in shaping investment preferences within corporate structures. Drawing on insights from behavioral economics, organizational psychology, and corporate finance, the study challenges the assumption of full rationality in institutional decision-making. It systematically categorizes cognitive biases-such as overconfidence, anchoring, framing, status quo bias, and loss aversion-and analyzes how these distortions manifest in strategic investment planning under uncertainty. The paper further explores how organizational variables, including governance structure, decision-making processes, and corporate culture, mediate the impact of these biases. Through an integrative approach, the article proposes an expanded framework of corrective mechanisms, such as standardized decision protocols, scenario-based evaluations, pre-mortem analysis, rotating committees, and decision support systems, designed to reduce behavioral distortions and enhance financial resilience. The findings emphasize that cognitive biases are not anomalies but structurally embedded tendencies in corporate behavior. Addressing them requires coordinated efforts at both the individual and institutional levels. Incorporating cognitive considerations into investment strategy and governance design improves the consistency, objectivity, and adaptability of capital allocation, especially in environments characterized by volatility, complexity, and information asymmetry.

Abstract: The article is dedicated to the role of financial planning as a tool for sustainable resource management in enterprises. It discusses key financial tools such as cash flow forecasting, zero-based budgeting, risk modeling, and big data analytics, which help organizations allocate resources efficiently, reduce costs, and achieve sustainability goals. Special attention is given to the integration of environmental, social, and governance (ESG) factors into financial planning. The paper emphasizes the importance of sustainable financial planning for enhancing competitiveness, attracting investment, and strengthening the company's reputation in the market.

Abstract: This article focuses on the integration of environmental, social, and governance factors into corporate financial strategies. It explores various integration models, such as the triple bottom line, integrated reporting, and the sustainability accounting standards board (SASB) standards, and their impact on financial performance, risk management, and stakeholder engagement. Special attention is given to the benefits and challenges companies face when implementing these models and their role in creating long-term value and sustainability. In conclusion, the article emphasizes the importance of a strategic approach to integrating sustainability factors to achieve successful and ethical business practices in a changing economy.

Abstract: This article focuses on the application of artificial intelligence (AI) in financial risk analysis and management. It explores the main methods of integrating AI into stress testing and scenario modeling processes, as well as its impact on improving forecast accuracy and decision-making under uncertainty. Special attention is given to the use of machine learning and deep learning algorithms for analyzing large datasets, identifying hidden patterns, and simulating risks, including rare but highly impactful events («black swans»). The article discusses the advantages of AI in enhancing the adaptability of systems to changing market conditions and in improving the speed of decision-making. It also addresses challenges related to data quality, the need for algorithm transparency, as well as ethical considerations and regulatory compliance. In conclusion, the article highlights the importance of further AI development in the financial sector and its role in improving the resilience of financial institutions in the face of global economic instability.

Abstract: This article is dedicated to the economic and mathematical modeling of capital turnover in production systems. Various models and approaches aimed at optimizing capital utilization, improving financial performance, and enhancing production efficiency are discussed. The role of artificial intelligence, linear programming, and scenario modeling in improving prediction accuracy and decision-making is analyzed. Special attention is given to risk management strategies such as diversification, hedging, and supply chain management, which contribute to faster capital turnover. The conclusion emphasizes the importance of integrating mathematical and AI models to enhance the resilience and financial flexibility of enterprises in a changing economy.

Abstract: The article explores current strategies for modernizing human resource management in financially-oriented businesses under the conditions of digital transformation. It analyzes the key directions of implementing digital technologies and artificial intelligence in HR practices, their impact on operational and financial performance, and the role of HR analytics in strategic planning. The paper presents visual models and tables that summarize the maturity levels of HR functions and their integration into the company’s financial architecture. Special attention is given to challenges related to data interpretation, fragmented digital infrastructure, and the necessity of cultivating an analytical culture. The study concludes that with sufficient digital maturity, the HR function becomes a strategic asset influencing business sustainability and capitalization.

Abstract: The article examines modern approaches to diagnosing the financial stability of enterprises under conditions of a dynamically changing macroeconomic environment. It outlines the main methodological groups, ranging from traditional ratio analysis to stress testing and predictive analytics models. A classification of diagnostic tools is presented, considering their applicability depending on business scale, digital maturity, and industry specificity. Particular emphasis is placed on the role of macroeconomic factors and digital platforms in enhancing the reliability of diagnostics. Based on the analysis, conclusions are drawn about the need for an integrative and adaptive approach to building a financial monitoring system in the context of high external uncertainty.