The article examines the impact of inflationary spikes in the early 2020s on the adequacy of actuarial reserves in the property and casualty (P&C) segment, identifying the drivers of systematic delays in adjusting these reserves. The relevance of the study is determined by the fact that loss reserves constitute the core of an insurer’s financial resilience. At the same time, the structural break in inflation trends from 2021 to 2024 disrupted the baseline stationarity premise upon which classical reserving methodologies implicitly rest. The objective is to provide an integrated assessment of how macro-inflationary shocks transmit into claim severity and generate the phenomenon of inflation recognition lag, as well as to compare the sensitivity of traditional approaches (Chain Ladder, Bornhuetter–Ferguson) to the new inflation regime. Scientific novelty lies in integrating a macro-level analysis of CPI and industry-specific inflation drivers with a micro-level examination of Allstate’s public reporting (10-K) and a quantitative assessment of the temporal lag in reserve responses to inflation. It is shown that parameter inertia in Chain Ladder and the anchoring of  Bornhuetter–Ferguson to a priori expectations create an under-reserving risk in accident years, followed by sharp reserve strengthening; in the Allstate case, the 2021 inflation impulse was fully reflected in reserves only in 2022 (strengthening on the order of $1.7 bn). Additionally, it is substantiated that the decisive role is played not by the headline CPI, but by the insurer-specific inflation basket (including repairs, parts, and medical care) and the multiplicative overlay of social inflation, which amplifies pressure in long-tail liability lines. The article may be helpful for actuaries, risk managers, and financial executives of P&C insurers developing robust reserving procedures under non-stationary inflation.

This article examines the role of e-government and artificial intelligence in the transformation of public services in Azerbaijan and explores future development prospects. The study analyzes key digital initiatives implemented within the framework of the “Digital Azerbaijan 2030” strategy, including the myGov platform, ASAN Service, and the activities of DOST centers. Descriptive-statistical analysis and international comparative methods are employed, comparing Azerbaijan’s position in the field of digital public services with the experiences of leading countries such as Estonia, Singapore, and the United Arab Emirates. The results show that the application of e-government and artificial intelligence significantly reduces the time required for delivering public services, increases transparency, and improves the efficiency of resource utilization. At the same time, challenges related to data security, legal regulation, and ethical governance remain. The article concludes with recommendations to expand electronic signature and digital identification systems, implement artificial intelligence in a phased manner, and strengthen legal and ethical frameworks based on international best practices.

This article examines the current importance and status of maintaining a separate accounting policy in public-private partnerships (PPPs). In the context of increasing reliance on PPP mechanisms for financing infrastructure and socially significant projects, ensuring financial transparency, proper risk allocation, and accurate recognition of assets and liabilities has become critically important. The establishment of a distinct accounting policy for PPP projects enhances the reliability and comparability of financial reporting, strengthens investment attractiveness, and ensures a balanced protection of public and private sector interests. The study analyzes key accounting aspects within PPP arrangements, including the recognition of assets and liabilities, revenue and expense measurement, risk allocation, and alignment with international financial reporting standards. The findings highlight that a separate accounting policy in PPP frameworks contributes to improved financial governance, accountability, and long-term project sustainability.

Machine learning has emerged as one of the most influential scientific and technological paradigms of the contemporary digital era, reshaping the ways in which data are interpreted, decisions are made, and complex systems are optimized. Across fields ranging from healthcare and robotics to cloud computing and pattern recognition, machine learning techniques have evolved from theoretical constructs into indispensable practical tools. This article presents a comprehensive, theoretically grounded, and application-oriented examination of machine learning, based strictly on established academic and professional references. Drawing from foundational works in probability theory, neural networks, Bayesian learning, and classification theory, as well as more recent contributions related to hybrid systems and applied signal processing, this study develops an integrated view of how different machine learning methodologies complement, compete with, and enhance one another.

The article explores supervised, unsupervised, and hybrid learning paradigms, placing special emphasis on the philosophical and mathematical underpinnings of probabilistic reasoning, evidence weighting, and pattern recognition. It also analyzes decision trees, support vector machines, fuzzy nearest neighbor algorithms, artificial neural networks, and Bayesian classifiers as interconnected components of a broader learning ecosystem rather than isolated techniques. Particular attention is given to the role of hybrid approaches in complex real-world problems, such as electrocardiogram signal analysis and cloud load balancing, demonstrating how machine learning systems can be adapted to deal with uncertainty, noise, and dynamic environments.

Beyond algorithmic descriptions, this work delves deeply into the epistemological foundations of learning from data, examining how evidence, probability, and induction form the basis of predictive modeling. By synthesizing insights from classical and contemporary literature, this article reveals that machine learning is not merely a collection of computational tricks but a coherent scientific discipline grounded in theories of inference, cognition, and decision-making. The discussion also addresses the limitations of existing methods, including issues of overfitting, interpretability, and data dependency, while proposing future directions in hybridization, meta-learning, and adaptive systems. The result is a publication-ready, holistic, and rigorously argued account of machine learning as both a theoretical science and a transformative applied technology.

Universities are increasingly recognized as central actors in innovation systems, contributing not only to education and research but also to entrepreneurship, technology transfer, and regional economic development. In this context, the concept of a university innovation ecosystem has gained growing attention in the literature. This study aims to identify and analyze the institutional factors that influence the formation of university innovation ecosystems. The research employs a qualitative analytical approach based on a structured review of academic literature and international policy reports related to innovation ecosystems, academic entrepreneurship, and university–industry collaboration. The analysis identifies several key institutional determinants that shape the development of university innovation ecosystems, including governance and strategic leadership, funding and financial support, technology transfer and incubation infrastructure, university–industry collaboration mechanisms, entrepreneurial culture, and digital infrastructure for innovation management. The findings suggest that innovation ecosystems within universities emerge not only from research excellence but also from the presence of supportive institutional structures and collaborative networks. The study proposes a conceptual framework illustrating how these institutional factors interact to influence innovation outcomes such as patents, industry-funded research, and academic spin-offs. The results highlight the importance of adopting a systemic and institutionally grounded approach to strengthening university innovation ecosystems.

Global crises have consistently showed their negative influence on tourism systems by affecting mobility, safety perceptions, and economic stability. Tourism demand is particularly sensitive to crises due to its dependence on optional spending and cross-border movement. The impact of global crises on tourism trends is analyzed by reviewing academic studies and international tourism research. Focusing on economic crises, health emergencies, geopolitical instability, and environmental shocks, the study identifies regular patterns in tourism behavior and structural change. The findings indicate that global crises not only lead to short-term declines in tourist flows but also increase long-term trends such as domestic tourism growth, digitalization, and sustainability-oriented tourism development. Global crises shape changing tourism trends and influence the future development of the tourism sector.

The article is dedicated to the strategic management of artificial intelligence technology implementation in corporate contexts. The relevance of the study is driven by the growing gap between the declared potential of artificial intelligence and the frequent stagnation or reversal of corporate AI initiatives. Scientific novelty lies in the integrated interpretation of technology adoption not as a technical decision, but as a multidimensional managerial process shaped by organizational readiness, trust, emotional responses, and leadership practices. This article examines the interaction between perceived usefulness, organizational pressure, cognitive attitudes, and disengagement mechanisms in enterprise AI deployment. Special emphasis is given to the transformation of managerial roles and human–algorithm interaction in everyday organizational workflows. This article aims to conceptualize the factors that determine sustained AI usage or disengagement in corporate settings. Analytical synthesis, comparative analysis, and structured review of academic sources are used to achieve this goal. The conclusion describes how trust, leadership support, and emotional acceptance together shape AI adoption trajectories. The article will be useful for managers, corporate strategists, and researchers studying digital transformation and technology governance.