Journal of Sustainable Development and Green Technology

This research investigates the evolving landscape of remote work and distributed teams in the context of the growing phenomenon of digital nomadism. As remote work becomes increasingly prevalent, organizations face unique challenges in managing geographically dispersed teams while accommodating the preferences and lifestyles of digital nomads. This study combines qualitative interviews with HR professionals and digital nomads, along with a quantitative survey of remote workers, to identify key challenges and strategies in managing remote and distributed teams amidst the rise of digital nomadism.

The aim of this research is to examine the convergence of intelligent frameworks and financial fraud detection as a strategic approach for strengthening business sustainability in the banking industry. A rigorous preprocessing regimen, which includes data cleansing, normalization, and SMOTE algorithm application for class rebalancing, sets the stage for a refined dataset. Our proposed framework employs Logistic Regression, Decision Trees, and Gradient Boosting models to conduct a multifaceted analysis that accommodates both linear and non-linear relationships within the data. The results are presented through visual representations such as distribution plots and RoC curves that confirm the effectiveness of the framework in detecting potentially fraudulent activities. The comparative analysis offers detailed insights into how versatile the framework is. This study contributes to the broader discourse on intelligent systems in financial fraud detection with practical implications for businesses seeking to enhance their sustainability through advanced risk management strategies.

This study explores the integration of machine learning methodologies in stock analysis to enhance the understanding of the relationship between sustainable business practices and financial performance. Against the backdrop of a shifting investment landscape that emphasizes responsible and informed decision-making, our research addresses the need for innovative approaches in evaluating stocks within a sustainability framework. Leveraging a combination of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and linear regression, we systematically analyze a dataset comprising sustainability metrics and stock performance. The DBSCAN clustering identifies distinct groups of stocks based on sustainability profiles, offering novel insights into market segmentation. Concurrently, linear regression models quantitatively reveal the impact of sustainability metrics on stock outcomes. The results affirm the significance of sustainability considerations in investment decisions, presenting a compelling case for the adoption of machine learning techniques in responsible investing strategies.

This research paper responds to the growing global demand for environmentally and socially responsible financial practices by outlining a strong framework for incorporating sustainable and green finance into effective trading portfolio management. The study acknowledges the current difficulties of reconciling financial goals with sustainability criteria and uses a methodological approach that includes risk-sensitive asset allocation, mean-variance optimization, and strategic maximization of the Sharpe ratio. By carefully examining and analyzing this research, it explores the complex dynamics of sustainable finance, thus providing a holistic understanding of how financial success is related to environmental and social responsibility. The findings of this study provide important insights into ongoing discussions on responsible investment strategies, thereby giving investors and policymakers a guide on how to align their financial objectives with sustainable development imperatives.

The research aims to fill a gap in the current sustainability strategies by investigating how business intelligence can be integrated into advanced companies to improve green financial practices. We will apply our proposed framework to Mutual Funds and Exchange-Traded Funds (ETFs) as we recognize the need for environmentally responsible financial decisions. Our study uses statistical analysis and predictive modelling with Random Forest and Ordinary Least Squares based on a comprehensive dataset obtained from Yahoo Finance. The results, presented through sector distributions, risk ratings, and distribution by category, provide detailed insights into the multifaceted impacts of business intelligence. Our findings indicate that the suggested framework optimises financial decisions and emphasizes the importance of customized approaches across different financial instruments. This study provides a valuable roadmap for practitioners, policymakers, and researchers navigating the changing landscape of environmentally responsible financial strategies in an era where advanced corporations grapple with the complexities of sustainable finance.

The complexity of the business environment, which is shaped by dynamic economic, environmental, and social factors, makes it more important for companies to develop sustainable risk management practices. This research proposes a novel approach that combines traditional methods with modern machine learning techniques in response to the complex challenges faced by contemporary businesses in balancing risk and sustainability. Our study uses a public bank loan default dataset as a case study to address missing data systematically through robust imputation mechanisms and transform categorical variables using feature encoding. Spearman correlation analysis helps us understand complex variable relationships and guides subsequent feature selection. The decision tree classifier, a powerful machine learning algorithm known for its interpretability, is applied to identify key factors contributing to risk assessment. The hierarchical structure of the decision tree not only reveals important variables but also provides an explicit representation of the decision-making process. ROC curve analysis shows how well our predictive model can differentiate potential loan defaults.