This article examines coaching as a strategic growth tool for the corporate environment of developing companies, with particular emphasis on practically applying the methodology. To that end, this paper analyzes the effectiveness of leading organizations' coaching in resolving critical growth challenges: scaling operations, change management, and talent retention. Businesses must be oriented to dynamically changing market conditions; hence, coaching emerged as a crucial resource for developing leaders, ensuring an organizational culture's agility, and attaining sustainable competitive advantages. The scientific novelty consists in systematizing industry practices in determining the linking coaching to financial results and proposing a model for adjusting the methodology to various economic sectors. The methodological base of the study combined qualitative and quantitative analysis of 11 sources, including academic works, industry reports, and company cases (Google, Intel, MD Anderson Cancer Center, Verizon). The main findings confirm that coaching is a strategic growth catalyst but is effective only through integration with corporate culture and top management support. Universal solutions are less effective: healthcare emphasizes staff stability, while technology focuses on innovation and flexibility. The key condition for success is the establishment of clear KPIs, such as an increase in internal appointments as well as improved team interactions and financial indicators. This study, therefore, contributes to management theory by showing that investment in coaching can be transformed not just into intangible benefits (employee confidence, communication) but also into measurable economic returns that enhance long-term business sustainability. It will serve company executives, HR managers, business coaches, and researchers in corporate governance who wish to incorporate coaching as a strategic tool in overcoming challenges related to growth and increasing the potential for sustainability by integrating leadership into an organization.

This article examines the methodological aspects of applying Transformer architectures for sales forecasting, demand analysis, and price optimization. The focus is on the development, adaptation, and integration of models in the context of processing large volumes of data and operating complex market mechanisms. The paper explores approaches to combining time series, identifying factor relationships, and improving the accuracy of analytical conclusions.

The methodology includes adapting basic Transformer architectures, such as Transformer with Multihead Attention Mechanism, to the specific characteristics of the data. The preparatory steps cover information aggregation, creation of temporal features, identification of categorical variables, and handling missing data. Historical datasets supplemented with external information sources are used for training. The sources include scientific articles by international authors published in open access, as well as materials available on the internet, allowing for a broad examination of the topic.

The results demonstrate the effectiveness of these architectures in forecasting tasks, identifying temporal dependencies, and improving business process quality. Examples of model implementation illustrate their successful use in commercial information systems. The conclusions emphasize the role of these approaches in decision-making automation and strategic planning.

The materials of the article are intended for professionals working in machine learning, data analytics, and process management improvement.

The article systematizes and analyzes strategic directions, the implementation of which makes it possible to increase the profitability of commercial real estate through the use of innovative approaches. The relevance of the topic under discussion is due to increased competition, changes in the investment climate, and the rapid development of new technologies. The methodological arsenal in the field of real estate management requires a revision of the usual tools, additions to the conceptual framework, as well as consistent and competent integration of digital solutions, which sets scientists the task of finding effective adaptation options. The purpose of the study is to substantiate innovative approaches aimed at increasing the profitability of commercial real estate (taking into account current changes in investment behavior and management methods). The work reveals contradictions in the literature: some of the research is based on traditional approaches, while others focus on the need for digital transformation and the use of analytical data. The problems of strategies for increasing the profitability of commercial real estate through innovative approaches from the standpoint of financial technologies are considered. The author's view on highlighting the advantages and limitations of specific strategic steps is formulated. It is summarized that a successful increase in profitability is possible through the integration of professional management, new technological developments and a systematic approach to analysis. The presented materials will be useful to researchers studying management mechanisms in relation to commercial real estate, investors interested in increasing the profitability of assets, specialists in the field of digitalization working on automation of operations and functions.

Detecting fraudulent bank transactions is crucial for maintaining the integrity of financial institutions and preserving customer trust. This study introduces a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model designed to enhance the accuracy and efficiency of fraud detection systems. Utilizing the European Credit Card Fraud Detection dataset comprising 284,807 transactions with significant class imbalance, extensive preprocessing techniques were applied, including Min-Max scaling and Synthetic Minority Over-sampling Technique (SMOTE). Recursive Feature Elimination (RFE) identified the top 20 impactful features, optimizing model performance. The proposed hybrid model demonstrated remarkable effectiveness, achieving superior accuracy (99.5%), precision (93.1%), recall (92.1%), F1-score (92.6%), and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 97.5%. Comparative analyses revealed that the hybrid CNN-LSTM model significantly outperformed traditional machine learning algorithms such as Logistic Regression, Random Forest, and XGBoost. These findings underscore the potential of CNN-LSTM hybrid models in addressing complex fraud detection scenarios, providing financial institutions with a robust and reliable tool for transaction anomaly detection.

This article examines various approaches to developing an effective government relations (GR) strategy in project management. Against the backdrop of rapid and pronounced changes in the external environment and the increasing complexity of regulatory mechanisms, this topic has become particularly relevant. The study aims to analyze and integrate traditional project management methods with modern practices of engagement with governmental institutions to mitigate regulatory risks and enhance competitiveness. A review of scientific literature and online sources reveals contradictions between the strategic importance of GR approaches and the risks of excessive governmental influence on decision-making. Additionally, a lack of empirical assessment regarding the effectiveness of applied methods has been identified. The study concludes that optimizing a GR strategy requires synthesizing classical management practices with flexible models of governmental engagement to establish adaptive operational frameworks. The author's contribution lies in formulating recommendations that combine theoretical concepts with practical steps, making them relevant for scholars and project managers. The findings contribute to a deeper understanding of the mechanisms for adapting GR approaches within a regulatory environment. The presented material is valuable for those involved in developing innovative management solutions and strategic planning.

This article explores technological solutions aimed at scaling Shopify platforms as they expand into international markets. The relevance of the topic is driven by the growing number of online stores that require high service availability, even under rapidly increasing traffic. The novelty of the study lies in the comprehensive analysis of architectural strategies used to distribute load, ensure fault tolerance, and reduce latency during global operations. The paper outlines key principles of flexible scaling through client isolation (pods), geographic distribution of servers across regions, the use of content delivery networks (CDNs) for accelerated content delivery, and load testing approaches designed to simulate peak scenarios such as flash sales. Additionally, it examines methods for balancing and dynamically reallocating resources to protect the system from failure. The study aims to offer practical recommendations for those seeking to maintain platform stability amid international expansion. To achieve this, the article applies comparative analysis of architectural solutions and model’s scalability potential. The research also draws on the experiences of Shopify engineers to deepen the understanding of practical challenges in global growth. The conclusion emphasizes the importance of constant monitoring of throughput and strategic distribution of key services across global data centers. This article will be useful to SaaS professionals, developers, and analysts responsible for planning large-scale e-commerce projects.

This article analyzes contemporary approaches to financial planning and budgeting in international infrastructure projects. The relevance of this topic is driven by the need to enhance resource efficiency, minimize risks, and ensure the long-term sustainability of large-scale initiatives, particularly amid the growing interest in green financial instruments and public-private partnerships. The study's scientific novelty lies in synthesizing perspectives from various authors who propose integrating classical risk distribution models, ESG criteria, and digital analytical tools, including large language models. The research outlines key factors determining project effectiveness and examines fundamental planning and budgeting approaches as reflected in academic literature. Special attention is given to attracting external investors, the distribution of roles between the public and private sectors, and the consideration of local community interests. The study aims to summarize existing practices and identify promising directions for future development. To achieve this goal, comparative analysis, source evaluation, and data systematization methods were employed. The conclusions highlight the effectiveness of the examined approaches. This article will be valuable to professionals in finance, project management, and sustainable development research.

This paper proposes innovative methodological approaches to project monitoring within the framework of multimillion-dollar programs aimed at modernizing electric distribution systems. The study is based on a comparative analysis of traditional deterministic planning methods and contemporary digital tools, including multidomain modeling and the integration of distributed energy resources (DER). It provides an in-depth review of existing methodologies, with a particular focus on the use of aggregated baseline scenarios, AC optimal power flow (AC OPF) methods, and the capabilities of network analysis platforms such as PSS®E, OpenDSS, and GridLAB-D. The identified research gap lies in the lack of a comprehensive approach that simultaneously addresses the technical, economic-regulatory, and digital dimensions of distribution system management. The purpose of the study is to examine methodological approaches to project monitoring within multimillion-dollar electricity distribution programs. The novelty of the work lies in offering a new perspective on the existing methodological practices applied to project tracking in such programs, made possible through the analysis of findings from previous research. The insights presented in the article are expected to be of interest to professionals in energy management and experts responsible for implementing large-scale electricity distribution programs, particularly those seeking to enhance project control, evaluation, and adjustment processes through the adoption of advanced methodological tools.

E-government has emerged as a critical tool in enhancing the effectiveness of disaster management across the globe. The integration of information technology (IT) in governmental processes has improved the capacity for early warning, response coordination, and recovery operations during disasters. This article reviews the existing literature on the role of e-government in disaster management, focusing on the benefits, challenges, and technological advancements in the field. The review identifies key themes, including the use of e-government platforms for disaster communication, the role of social media, and the development of decision-support systems. The findings suggest that while e-government has significantly improved disaster preparedness and response, challenges remain, including digital divides, infrastructure limitations, and cybersecurity concerns. Future research should explore ways to address these challenges and improve the integration of e-government into disaster management systems.

This article examines the essence and specific features of applying geolocation data for personalizing financial services. In the context of the rapid digitalization of this sector (as well as others), the use of such data is becoming strategically significant both today and in the future. Banks and financial institutions are actively integrating spatial analytics into their services to enhance service quality and personalize offerings. However, along with the beneficial effects, several critical challenges and contradictions arise, including the interpretation of geodata, privacy concerns, and the efficiency of processing algorithms. The objective of this study is to analyze the ways geolocation data is utilized to personalize financial services, identify key advantages, highlight problematic areas, and outline future directions for its application. The article reviews the primary technological approaches and emphasizes that despite the evident benefits, large-scale implementation of geolocation technologies requires careful consideration of personal data protection and transparency of algorithms. The author’s contribution lies in the comprehensive examination of the issue, presenting a classification of geolocation applications and identifying gaps in the academic literature. The findings will be valuable for financial institutions, fintech solution developers, digital marketing specialists, and analysts working with spatial data.

This article examines the potential of building information modeling (BIM) technologies in managing the reconstruction of residential and commercial properties, with a focus on cultural heritage preservation. The theoretical foundation includes an analysis of BIM technologies, generative design, and digital twins, as well as their integration for precise digital modeling, optimization of design solutions, and dynamic monitoring of operational parameters. A case study of the reconstruction of the former Santa Barbara cinema in Paternò demonstrates that the comprehensive application of these technologies significantly enhances design accuracy, improves energy efficiency, optimizes operational performance, and preserves the historical integrity of the structure. The findings confirm the hypothesis that integrating BIM with modern digital tools is an effective approach for managing reconstruction projects, providing substantial practical value for professionals in architecture, construction, and real estate management. The article also presents recommendations for further research aimed at improving generative design algorithms and enhancing the reliability of digital twins. The topic of BIM technology application in reconstruction management will be of interest to leading specialists in architectural and engineering design, strategic construction project management, and urban studies, as it offers an innovative methodological framework for integrating digital tools to optimize reconstruction processes, minimize technological risks, and ensure the sustainability of urban infrastructure.

This article analyzes the current problems, modern and innovative mechanisms of economic resource management in services of enterprises. In the conditions when the service sector is taking a leading position in the economy of Uzbekistan, the effective use of economic resources is considered an important factor of competitiveness. The article describes the classification of resources in the service sector, their importance, management mechanisms, practical analysis based on statistics and scientific proposals for solving problems.

This article examines modern approaches to minimizing legal risks in the negotiation and implementation of international investment agreements, addressing both legal and socio-environmental aspects. Based on an analysis of recent research, key factors contributing to safer and more balanced interactions between investors and host states are identified. Specifically, detailed descriptions of contractual obligations, the introduction of clear sanction and compensation mechanisms, and flexible dispute resolution procedures significantly reduce the likelihood of conflicts. Additionally, the role of proactive contract design is emphasized, which involves a simplified and transparent structure to engage all stakeholders, including local communities. The article also highlights the growing significance of digital trends, from the recognition of virtual assets and cross-border data flows to the adaptation of agreement provisions reflecting the rapidly evolving digital economy. The findings conclude that comprehensive risk management measures not only enhance trust in the international investment environment but also promote more sustainable development while preserving regulatory sovereignty and protecting the key rights and interests of all parties involved. This article is of interest to specialists in international law, legal professionals, economists, government officials, and corporate consultants involved in the development, negotiation, and analysis of investment agreements.

This article explores the impact of modern digital tools on corporate financial management. It highlights the interplay between robotics, automation, big data, AI, and blockchain systems in optimizing financial processes and introduces new approaches aimed at enhancing transparency and reducing error rates. The study outlines methodologies that improve analytics and forecasting while identifying potential cybersecurity threats. The novelty of this research lies in its comprehensive review of digital solutions through the lens of FinTech adoption and migration to cloud platforms. Particular attention is given to the challenges of scaling technologies and enhancing resilience against cyberattacks. The objective is to formulate practical recommendations for improving financial decision-making and minimizing operational risk. To achieve this, the study employs comparative analysis, expert reviews, and empirical insights from recent literature. The final sections provide applied findings and guidance for professionals seeking to integrate innovation into corporate finance. This article is intended for financial analysts, CFOs, and managers aiming to enhance the effectiveness of financial operations.

This study investigates the availability and effectiveness of support systems for church ministers in Nairobi County, Kenya, focusing on their psychological well-being, coping mechanisms, and ministerial performance. Drawing from a target population of 1,300 church ministers, including pastors, youth leaders, and administrative personnel, a sample of 306 participants was selected using stratified random sampling. A descriptive research design was employed to analyze the interplay between psychological stressors, support resources, and coping strategies in addressing the challenges faced by church ministers. The findings highlight the multifaceted nature of stress experienced by church ministers, including emotional labor, congregational expectations, and personal pressures, which often affect their mental health and professional output. Despite the significant role of institutional support systems, such as counseling services and peer support groups, their accessibility and effectiveness remain inconsistent. Many ministers report insufficient resources to address burnout and unique ministerial challenges, pointing to a need for structured and culturally sensitive interventions. Family and community support systems emerge as crucial yet often compromised by the demanding nature of ministry work. Spiritual practices, including prayer and meditation, serve as key coping mechanisms, though their impact is enhanced when combined with professional psychological care. The study also underscores the potential of peer-mentoring programs and online support platforms in fostering emotional resilience among church ministers, though digital and structural barriers limit their widespread adoption. The study recommends enhancing the organizational framework of religious institutions to prioritize the mental health of church ministers. This includes integrating structured counseling services, expanding peer-support programs, and addressing socio-cultural and economic factors that hinder access to care. Further, it advocates for the adoption of holistic approaches, such as the Rehabilitation Model, to create personalized and sustainable support systems for clergy. By addressing these gaps, the findings aim to inform policies and practices that promote the well-being and effectiveness of church ministers in Nairobi County.