FinTech and Sustainable Innovation (FSI) is an international interdisciplinary journal that reports activities on cutting-edge, in-depth research and practices in FinTech and sustainable innovation transforming the financial industries and economy, offering valuable archival materials in the field through reader friendly knowledge dissemination platform. As an open source, peer-reviewed journal. FSI welcomes submissions that focus on the development, implementation and evaluation of digital technologies, sustainable business models and managerial analytics that is relevant to FinTech and sustainable innovation. FSI is also dedicated to advancing research that supports the United Nations Sustainable Development Goals (SDGs) by exploring the intersection of financial technology and sustainability. Our platform supports key SDGs by promoting FinTech innovations that enhance financial inclusion, improve financial literacy and promote educational equity, drive sustainable economic growth, foster innovation, and create employment opportunities. We also focus on green finance and socially responsible investments to advance sustainable economic development, support climate action through financial technologies, and explore how FinTech can facilitate international cooperation, trade, and global economic integration. The journal is a Gold Open Access journal, online readers don't have to pay any fees. The journal is currently free to the authors, and all Article Processing Charges (APCs) are waived until 31 December 2025.
FinTech powered by digital innovation is reshaping the global economy. With its disruptive power and digital agility, FinTech holds immense potential to accelerate the transition toward a greener and more inclusive future. It is no longer merely a financial disruptor—it is emerging as a key engine for sustainable progress. By aligning technological advancement with the urgent environmental and social needs of our time, FinTech is poised to help redefine the rules of finance for the better. A crucial contribution of FinTech lies in promoting financial inclusion—a cornerstone of sustainable development. Mobile banking, digital wallets, and microfinance platforms are extending essential financial services to unbanked populations, especially in developing regions. In doing so, they help lift communities out of poverty, enhance economic participation, and strengthen local resilience. Meanwhile, a parallel revolution is underway through the rise of artificial intelligence (AI). As AI systems grow in complexity and scale, so does their environmental footprint—raising urgent questions about the sustainability of widespread AI adoption. Addressing this challenge requires balancing technological progress with responsible innovation and energy efficiency. In this evolving landscape, FinTech has the power to drive meaningful change for sustainability. Sustainable innovation demands responsible investment with intentional focus on equity, inclusivity, and environmental stewardship. By prioritizing these values, FinTech can lead the charge toward a future where technology and sustainability converge for the greater good. FinTech can lead this shift by leveraging current progress in AI to enhance ESG transparency and steer responsible investment.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
Auditing and Decision-Making in the Context of Industry 5.0: An Exploratory Study in Costa Rica
This research looks at how auditing affects strategic decision-making during Costa Rica's shift to Industry 5.0. It uses a mixed methods approach that includes a systematic literature analysis and survey data from 50 certified audit professionals. The study finds major problems with the use of audits: 90% of companies don't have formal audit procedures, and only 10% use audit data strategically. Demographic analysis shows that older (50% aged 45+) and more experienced (65% with 10+ years) decision-makers may be less likely to adopt new technology. Comparative regional study shows that Costa Rica has the same problems as other Latin American countries, such as regulatory fragmentation and lack of resources. However, it also shows that organized frameworks like COBIT may help improve compliance rates. The report suggests three evidence-based actions: (1) harmonizing regulations to cut compliance costs by 37%, (2) targeted upskilling initiatives to close gaps in digital literacy, and (3) blockchain and AI solutions focusing on small and medium-sized businesses to improve transparency and efficiency. These results help us understand how to modernize audits in developing countries by measuring the “experience-technology paradox” and setting standards for how ready each industry is for Industry 5.0. The study gives politicians and business executives useful information that shows how important it is to find a balance between technological progress and workforce development. Some useful suggestions are to start pilot projects for continuous auditing technologies and policy efforts to make compliance frameworks more uniform. Received: 28 April 2025 | Revised: 18 August 2025 | Accepted: 11 September 2025 Conflicts of Interest The author declares that he has no conflict of interest in this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Gabriel Silva Atencio: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Credit Card Fraud Detection Through Deep Learning and Real-Time Data Streams: A Comparison and New Directions
Credit card fraud detection has become a critical concern for financial institutions as the volume of digital transactions grows rapidly. This paper presents a comparative study of two advanced methodologies – deep learning and real-time data stream analysis – applied to credit card fraud detection. Deep learning models, particularly Long Short-Term Memory networks, demonstrated high accuracy (up to 92%) in predicting customer behaviors and contributing to the detection of fraudulent transactions. However, they require large amounts of historical data and may not be ideal for real-time detection. In contrast, real-time data stream analysis, powered by innovative techniques like the patented BEReTiC system, provides immediate fraud detection but with lower initial accuracy. This paper explores the trade-offs between these approaches, highlighting the strengths of deep learning in pattern recognition and the adaptability of real-time data mining in dynamic financial environments. We evaluate both techniques on real-world data, measuring accuracy, false positives, and adaptability to novel fraud patterns. Results indicate that while deep learning offers high accuracy, BEReTiC enables faster detection with fewer false alarms and enhanced responsiveness. The findings suggest that a hybrid model integrating both techniques may offer a more effective solution for tackling the complexities of credit card fraud in real time, since it would combine the predictive power of deep models with the agility of real-time analytics, opening new directions in fraud prevention for high-velocity financial environments. Received: 7 May 2025 | Revised: 22 July 2025 | Accepted: 11 August 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data are not publicly available as they were provided to the author under a non-disclosure agreement for the purposes of this study. Author Contribution Statement Elias Polytarchos: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Sociotechnical Transformation: A Systematic Review on the Impact of Artificial Intelligence on Society and Organizations
This article presents a systematic literature review (SLR), conducted in accordance with PRISMA 2020 guidelines, to explore how artificial intelligence (AI) is reshaping the architecture of sociotechnical systems. Drawing from 64 peer-reviewed Q1 publications published between 2023 and early 2025, the review distils four interwoven thematic domains: labor and organizational transformation, social inequality, surveillance and data governance, and the evolving dynamics of human–machine interaction and identity. These themes illuminate a crucial insight: AI is not merely optimizing processes or enhancing efficiency; it is recalibrating social hierarchies, reshaping epistemic authority, and redefining institutional accountability. The studies reviewed the span of a range of sectors, from credit scoring algorithms and automated hiring systems to predictive policing and AI-mediated educational platforms. What emerges is a consistent finding: these systems are far from neutral. They are entangled with cultural assumptions, political agendas, and economic imperatives that shape both their design and their deployment. To support transparency, the article includes a comprehensive metadata table that categorizes the 64 studies by topic, method, and publication source. Beyond synthesis, the review raises an urgent call for human-centered AI development, participatory design processes, and equitable governance frameworks that address the regulatory asymmetries between the Global North and South. In a world increasingly governed by algorithmic logic, these measures are not optional, they are foundational. Received: 4 May 2025 | Revised: 3 July 2025 | Accepted: 31 July 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study Author Contribution Statement Marc Selgas-Cors: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Impact of Job Stress on Employee Health and Performance: Evidence from Private Banks in Hailakandi District of Assam
Job stress is a critical factor that impacts employee performance across all professional domains, including both paid and unpaid roles. In today's competitive environment, organizations, whether public or private, rely on their human capital to achieve efficiency, effectiveness, and sustained profitability. This study explores the connection between job stress and employee health and how these elements collectively impact job performance, with a specific focus on private sector banks in the Hailakandi district of Barak Valley, Assam. The study was conducted across four major private banks: HDFC, ICICI, Axis, and Bandhan Bank. Data were collected through structured questionnaires administered via personal interviews with 86 randomly selected employees. Descriptive statistics and regression analysis were used to examine the extent and nature of stress-related effects on health and work performance. Data were analyzed using SPSS (Statistical Package for the Social Sciences), enabling detailed insight into the correlations between job stress, employee well-being, and productivity. The study's findings revealed that job stress has a significant impact on employee health and a moderate impact on employee performance. Received: 30 April 2025 | Revised: 6 August 2025 | Accepted: 14 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Panchali Dey: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft, Writing –review & editing. Soumendra Bhattacharjee: Conceptualization, Methodology, Validation, Resources, Data curation, Visualization, Supervision, Project administration.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
Credit Rating in the Age of Artificial Intelligence
The advent of artificial intelligence (AI) has revolutionized financial systems, particularly in the domain of credit rating, where traditional methodologies are being reshaped by data-driven innovation. This manuscript explores the transformative impact of AI on credit assessment, focusing on its ability to enhance accuracy, efficiency, and inclusivity while raising critical ethical and regulatory challenges. Historically, credit rating has relied on standardized models using limited datasets, such as payment histories and debt-to-income ratios, often excluding underserved populations lacking formal financial records. AI, with its capacity to process vast, unstructured datasets—spanning social media activity, transaction patterns, and alternative financial indicators—offers a paradigm shift toward more dynamic and personalized risk profiling. Machine learning algorithms can identify subtle patterns invisible to conventional approaches, promising improved predictive power and reduced default rates. However, this evolution is not without pitfalls. The opacity of AI models, often described as "black boxes," complicates accountability, while biases embedded in training data risk perpetuating or exacerbating inequalities. Furthermore, the integration of non-traditional data sources raises privacy concerns, necessitating robust governance frameworks. This manuscript evaluates case studies of AI-driven credit rating systems, contrasts their performance with legacy methods, and analyzes emerging regulatory responses globally. It argues that while AI holds the potential to democratize access to credit and refine risk assessment, its deployment must balance innovation with transparency, fairness, and consumer protection. By addressing these tensions, stakeholders can harness AI to build a more equitable and resilient financial ecosystem in the digital age. Received: 17 March 2025 | Revised: 19 May 2025 | Accepted: 09 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Milad Shahvaroughi Farahani: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization, Supervision, Project administration. Gholamreza Mahmoudi: Conceptualization, Writing – review & editing. Ghazal Ghasemi: Conceptualization, Validation, Investigation.
Effective Cybersecurity Strategies for Mitigating Remote Work and IoT Risks in Enterprises
This research examines the best ways to protect against the dangers associated with working from home and using Internet of Things (IoT) devices in the workplace through a qualitative analysis of three organizational case studies. The study finds and assesses multilayered defense mechanisms, showing that integrated technological-policy-human frameworks are better at reducing threats. Zero-trust architectures (89% lateral movement containment) and artificial intelligence (AI)-enhanced Extended Detection and Response platforms (88% threat visibility improvement) are two examples of frameworks that work particularly well. The results add a lot to the cybersecurity literature by (1) empirically confirming that the National Institute of Standards and Technology Cybersecurity Framework adaptations work for hybrid work infrastructures, (2) measuring IoT security holes (42% of critical vulnerabilities come from unpatched firmware), and (3) setting behavioral benchmarks (62% less phishing with role-based training). A comparative study reveals a 5:1 return on investment for advanced solutions, highlighting the constraints on small and medium-sized enterprise adoption. This moves the conversation further to scalable cyber-resilience. The research uses triangulated validation, which combines Security Information and Event Management data, expert interviews, and framework-aligned evaluations. This creates a model that other organizations may use to reduce threats. The findings show that traditional perimeter-based security models need to be reevaluated. Instead, they suggest using dynamic, intelligence-driven techniques that are more suited to dispersed work and IoT attack surfaces. This study gives businesses that are dealing with the hazards of digital transformation both theoretical foundations and practical plans. It also points out important areas of research in AI-driven threat prevention and cost-effective security frameworks for companies with limited resources. Received: 19 April 2025 | Revised: 11 July 2025 | Accepted: 13 August 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Gabriel Silva Atencio: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision.
The Impact of Green Credit Policy on the Corporate Value of High-Polluting Enterprises
China's 1980s strategic focus on economic development initiated comprehensive reforms, propelling rapid industrialization and modernization. Concurrently, this growth, alongside accelerated urbanization and industrial advancements, has exacerbated environmental challenges, severely impacting urban areas and public health. To reconcile economic progress with environmental stewardship and mitigate associated risks, the China Banking Regulatory Commission introduced the Green Credit Guidelines in 2012. These guidelines aimed to foster green credit within banking institutions, support the transformation of traditional industries, and facilitate sustainable economic restructuring. This paper analyzes the impact of the Green Credit Guidelines’ implementation on the valuation of high-polluting enterprises following the policy's 2012 release. The analysis encompasses theoretical underpinnings, a review of existing literature, and rigorous empirical testing. For the empirical component, the study employs a dataset of nearly 30,000 observations from Chinese A-share listed companies. Categorizing firms based on industry heterogeneity, a difference-in-differences model with firm fixed effects is utilized for the empirical investigation. Robustness checks and parallel trend tests are conducted to ensure the reliability and validity of the results. The regression results indicate a statistically significant positive effect of the green credit policy on the value of high-polluting firms. This suggests that the issuance of the Guidelines has played a constructive role in promoting the transformation and upgrading of enterprises in China, thereby supporting the nation's sustainable economic development objectives. Received: 13 April 2025 | Revised: 11 July 2025 | Accepted: 19 August 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Li Jun: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.
The Impact of Oil Prices on Guyana's Real Exchange Rate: An AlphaFold-Decomposition Analysis
In 2015, Guyana, a small South American country, experienced a major economic turning point after ExxonMobil identified substantial commercial oil reserves in its waters. By 2020, Guyana had started producing and exporting oil commercially, shifting its economy toward oil exports. A key factor shaping Guyana's economic future is the global price of oil, since it directly impacts the country's export income and government revenue from oil. Subsequently, the effect of oil prices on the real exchange rate now becomes an important issue for Guyana due to the potential to affect the country's economic stability and competitiveness. Therefore, the relevant research question is: (i) to what degree have oil prices impacted Guyana's real effective exchange rate? This study created a decomposition technique called AlphaFold-Decomposition, hereinafter referred to as AlphaFold-D, based on the AlphaFold methodology. Drawing inspiration from AlphaFold's ability to model complex folding patterns with high accuracy, the subsequent methodology provides a more accurate means of decomposing time series into its underlying intrinsic modes than empirical mode decomposition. As such, this methodology is characterized by the integration of AlphaFold's attention mechanisms, multi-stage refinement processes, and noise reduction techniques. Received: 11 April 2025 | Revised: 13 June 2025 | Accepted: 26 June 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in https://github.com/doncharles005/AlphaFoldDandtheexchangerate and https://statistics.cepal.org/portal/databank/index.html?lang=en&indicator_id=1901 Author Contribution Statement Don Charles: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Analyzing the Impact of International E-commerce Regulations and WTO Frameworks on Corporate Governance in Digital Enterprises
This research analyzes the intersection of international e-commerce law and corporate governance practices of digital companies, with a particular focus on the role of WTO legal instruments. The goal of the research is to analyze how international trade rules, both binding and non-binding, influence governance practices and compliance choices of digital companies whose operations span multiple jurisdictions. The research is motivated by the growing complexity of international digital trade and the limits of traditional legal systems to address regulatory challenges such as data sovereignty, cross-border consumer protection, and accountability of platforms. Using doctrinal legal research methodology along with comparative methods, the study explores international normative texts, regional instruments, and governance practices from selected jurisdictions. The study employs a critical review process with legal instruments and corporate practices to uncover areas of influence and gaps in governance innovation. The findings of the study demonstrate that while WTO frameworks impose normative constraints on corporate behavior, there is increasing regulatory fragmentation, shifting enforcement practices, and asymmetries in governance across developed and developing economies because of the lack of binding global standards. The study makes recommendations for legal harmonization and improved regulatory coordination to enhance governance in digital companies and enable fair participation for digital enterprises in the global digital economy. Received: 3 April 2025 | Revised: 17 July 2025 | Accepted: 26 September 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing does not apply to this article as no new data were created or analyzed in this study. Author Contribution Statement Kolawole Afuwape: Conceptualization, Methodology, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
General Data Protection Regulation (GDPR)
Toward Fish Silage Emulsion from Barbados: A Mixture of Experts Analysis
Fish silage emulsion is a sustainable alternative to synthetic fertilizers. Fish silage emulsion offers several benefits, including fertilizing crops, reducing methane emissions, and helping reduce dependence on nitrogenous fertilizers. To assess the market for fish silage emulsion in Barbados, one needs to consider the fertilizer market. This assessment should consider market demand, price, supply, and the potential for export to neighboring countries. This study uses the mixture of experts (MoE) model to forecast fertilizer prices, measured by the Fertilizer Price Index (FPI). The MoE model is a machine learning framework intended to optimize model parameters efficiently while minimizing the corresponding processing requirements. It accomplishes this by utilizing a set of specialized submodels, known as “experts,” along with a gating system that dynamically identifies the most pertinent experts for each input. Using monthly FPI data from January 2021 to February 2025, forecasts were generated. The model projects the index to rise from 133.47 in February 2025 to 153.1040 in the 5-step ahead forecast and further to 200 in the 10-step ahead forecast. Received: 2 April 2025 | Revised: 26 June 2025 | Accepted: 17 July 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in https://github.com/doncharles005/MoE and https://wits.worldbank.org/. Author Contribution Statement Don Charles: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Digital Business Transformation in Advertising: Effects on E-services Revenue
In this heyday of the digital economy, digital advertising has emerged as a powerful means for businesses to engage with consumers. Despite its vital role in the microeconomy by helping companies to grow, there is still limited empirical evidence on how digital advertising expenditures influence revenue generation at the macro level, particularly within the e-services sector in the Middle East and North Africa (MENA) region. To address this gap, this study investigates the long- and short-term effects of digital media market revenue and total digital ad spending on e-services revenue across five MENA economies: Qatar, the UAE, Saudi Arabia, Egypt, and Iraq. Using annual panel data from 2018 to 2023, an era shaped by the accelerating digital shift triggered by the COVID-19 pandemic, and applying an Autoregressive Distributed Lag model, the analysis reveals that both variables have no significant short-term impact but produce a positive and statistically significant effect on e-services revenue in the long run. The study offers actionable insights for business leaders and policymakers, emphasizing the need for robust data governance, sustained digital advertising strategies, artificial intelligence-integrated marketing strategies, and supportive digital infrastructure to accelerate economic transformation in emerging markets. Received: 1 April 2025 | Revised: 13 June 2025 | Accepted: 17 July 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Ahmad Haidar: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
The Hurricane of Debt Hammering International Trade of Selective CARICOM Member States: Why the Region Needs Structural Reform of Official Development Assistance
This study sought to examine the effects of public debt and official development assistance (ODA) on the balance of trade in selected Caribbean Community (CARICOM) member states. The goal was to provide actionable policy insights that could help enhance regional economic stability. This study created a hybrid methodology that combined a panel ordinary least squares model with a deep residual network model and used it to examine data from seven Caribbean countries over the 2013–2022 period. The results indicated a significant negative dependence between public debt and trade balance, with a corresponding coefficient of −0.6153 indicating that a 1% rise in public debt was associated with a 0.62% decline in the trade balance. Additionally, ODA was found to have a positive impact, which was reflected by a 1% increase in ODA corresponding with a 1.95% improvement in the logarithmic value of the trade balance. Due to these results, this study lobbies in favor of the implementation of debt-for-climate swaps as a policy mechanism to alleviate debt burdens while facilitating the allocation of resources to climate-resilient infrastructure. This strategy seeks to facilitate long-term economic resilience in the region by addressing both fiscal sustainability and environmental challenges. A limitation of this study is that due to limitations of data, all 15 CARICOM full member states and 5 associate member states were not included. Nevertheless, the findings of this study remain valuable, as they justify the need for debt relief in the region. Received: 18 March 2025 | Revised: 14 May 2025 | Accepted: 28 May 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://github.com/doncharles005/POLS-ResNet. Author Contribution Statement Don Charles: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Marketing Strategies and Tactics in the Metaverse: A Systematic Literature Review and Future Research Agenda
Marketing has traversed a long path from traditional to digital marketing to metaverse marketing in Industry 4.0. Different marketing scholars and academicians have predicted that the rules of marketing game are going to take a paradigm shift in the era of metaverse marketing. Hence, the primary objective of this literature review is to understand the application of metaverse in marketing or how marketing strategies and tactics can take place in the metaverse. In order to do this review research, 68 research articles have been identified from two prominent databases: Google Scholar and Scopus. Widely used PRISMA protocol has been used for this research paper identification. After selecting and downloading the papers, authors have manually reviewed each. Finally, the application of marketing strategies and tactics suggested by different researchers identified and recorded with Microsoft Excel and then analyzed with an advanced data visualization software called RAWGraph. The findings indicate that marketing strategies and tactics in the metaverse can be categorized into six (6) major categories. These are (1) segmentation, targeting, and positioning, (2) 4Ps, (3) branding, (4) customer relationship marketing and partner relationship marketing, (5) competitive strategies, and (6) marketing metrics and automation. The contributions of this study are as follows: (a) it developed a timeline map demonstrating the evolution of marketing technology over time and (b) it synthesized existing literature and presented how marketing strategies and tactics can be applied in the metaverse or immersive world. Received: 17 March 2025 | Revised: 4 July 2025 | Accepted: 19 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Mohammad Faruk: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, and Project administration. Sagor Ejarder: Software, Formal analysis, Resources, Writing – original draft, and Visualization. Rabeya Jannat: Formal analysis, Resources, and Writing – original draft. Md. Imran Hossain: Software, Resources, Writing – original draft, and Visualization. Md. Hafizur Rahman: Methodology, Resources, and Writing – review & editing. Md. Shahanur Islam: Resources, Writing – review & editing, and Visualization.
Stock Price Prediction with LLM-Guided Market Movement Signals and Transformer Model
Accurate stock price prediction is difficult because financial markets are complex and affected by various factors. Traditional methods often fail to analyze financial news and market trends effectively. Recent improvements in large language models (LLMs) allow better extraction of insights from text and stock data. Motivated by these advances, this paper introduces a new approach that combines LLM-generated stock predictions with a Transformer model to forecast prices. Specifically, a structured prompt fed with financial news and stock features is designed. Then, the prompt-based LLM approach is used to generate the predicted trend in the form of a one-hot vector and its corresponding probability. Finally, the LLM-generated output, together with historical closing prices, is used as input to a Transformer model to predict the stock price for the next day. To examine the effectiveness of the proposed framework, models such as Long Short Term Memory (LSTM), Temporal Convolutional Network, Convolutional Neural Network (CNN), CNN-LSTM, Random Forest, support vector regressor, XGBoost, and vanilla Transformer are chosen for comparison. In addition, an ablation study is conducted under several configurations. The findings indicate that the proposed framework exhibits superior performance compared to all the baseline models. Moreover, the ablation study demonstrates that integrating LLM-predicted features has the potential to improve stock price prediction performance. Received: 16 March 2025 | Revised: 13 June 2025 | Accepted: 26 June 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The stock data that support the findings of this study can be downloaded from Yahoo Finance using the Python library finance. The news data can be downloaded using the Financial News Feed and Stock News Sentiment data API (https://eodhd.com/financial-apis/stock-market-financial-news-api). Author Contribution Statement Qizhao Chen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Navigating Innovation, Inclusion, and Ethical Challenges in AI-Driven Fintech: The Philippines
The purpose of this study is to investigate the ethical risks associated with the implementation of artificial intelligence (AI)-driven social fintech initiatives in the Philippines and how they support financial inclusion. The study examines nine fintech companies that operate in a variety of industries, such as digital banking, microlending, and blockchain-based waste-to-cash programs, using a qualitative multiple case study methodology. According to the research, fintechs use AI to provide automated onboarding, alternative credit scoring, and customized financial services. These tools are implemented using AI-enabled, mobile-first platforms that allow for quick expansion into underserved areas without depending on conventional banking infrastructure. Findings show that while such innovations significantly improve reach and efficiency, they also introduce ethical risks due to low financial literacy, data privacy concerns, cybersecurity vulnerabilities, and uneven consumer protections. The necessity of responsible AI integration, improved consumer protection, and flexible regulatory frameworks is highlighted by these risks. This study adds to the body of knowledge on inclusive digital finance in low-capacity digital environments by shedding light on the intricate trade-offs between innovation and ethics. Received: 15 March 2025 | Revised: 14 August 2025 | Accepted: 2 September 2025 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Tetiana Schipper: Conceptualization, Methodology, Resources, Writing – original draft, Supervision.
Next-Gen Threat Hunting: A Comparative Study of ML Models in Android Ransomware Detection
Ransomware attacks on Android devices have been increasing in recent years, posing a significant threat to users' data and privacy. In the finance sector, ransomware increasingly targets banking applications that impact their financial operations. This research presents a comprehensive evaluation of four popular machine learning algorithms – K-nearest neighbors (KNN), neural networks (NN), random forest (RF), and support vector machines (SVM) – in classifying Android ransomware. In this work, we utilize an open-source ransomware dataset available on Kaggle that comprises 10 types of ransomware and benign instances of Android applications, extracting relevant features for analysis. The performance of each classifier is assessed using various evaluation metrics, including accuracy, precision, recall, and F1-score. The experimental work shows that the RF classifier achieves the highest accuracy of 96.22%, followed by SVM with an accuracy of 83.51%, NN at 81.91%, and finally KNN at 70.49%. Furthermore, the research explores the strengths and limitations of each algorithm, providing insights into their suitability for real-world ransomware detection scenarios. The findings contribute to the development of robust and efficient security mechanisms for safeguarding Android devices against the evolving threat of ransomware. Received: 14 March 2025 | Revised: 20 June 2025 | Accepted: 30 June 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/subhajournal/android-ransomware-detection?resource=download. Author Contribution Statement Linet Momposhi: Conceptualization, Methodology, Software, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Alana Maurushat: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Rodrigo N. Calheiros: Validation, Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration, Funding acquisition. Shawal Khan: Validation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration.
Long-Memory Modeling and Forecasting of High-Carbon Intensity Rating Exchange-Traded Funds (ETFs)
This paper offers statistical insights into the predictability of returns and volatility for exchange-traded funds (ETFs) with low to high-carbon intensities, utilizing three configurations of long-memory models: autoregressive fractionally integrated moving average (ARFIMA) combined with generalized autoregressive conditional heteroskedasticity (GARCH), ARFIMA integrated with fractionally integrated GARCH, and ARFIMA paired with hyperbolic GARCH. The findings reveal that high-carbon intensity ETFs generally yield higher positive returns and exhibit decreased volatility than their low-carbon intensity counterparts. Additionally, the study identifies volatility clustering, where lagged conditional variances exert a greater influence than significant lagged mean returns. The analysis also demonstrates the presence of positive long-term dependence in the time series of several high- and low-carbon intensity ETFs, indicating that forecasting using fractionally integrated models is feasible. However, the results suggest no definitive differences in the characteristics of high and low-carbon intensity ETFs concerning short-term, intermediate-term, and long-term memory processes, as some ETF datasets yielded insignificant findings. Notably, the papers observe anti-persistent characteristics, which caution investors against holding these ETFs for extended periods or relying heavily on current trends for decision-making. Received: 20 December 2024 | Revised: 24 June 2025 | Accepted: 9 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement John Francis Diaz: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Florian Gerth: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – review & editing. Michael Young: Conceptualization, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Project administration.
high-carbon intensity and low-carbon intensity ETFs
Predictive Analytics for Personalized Debt Management: Leveraging Machine Learning to Provide Actionable Financial Advice
In today's complex financial environment, managing personal debt effectively has become a significant challenge, often leading to increased loan defaults. This study aims to develop a machine learning-based framework for personalized debt management by analyzing borrower data to identify risk levels and offer tailored financial advice. The scientific novelty of this research lies in its integration of both supervised and unsupervised learning techniques to gain deeper insights into the characteristics of defaulters and predict their risk levels. This methodology improves prediction accuracy and interpretability and applicability in real-world lending. The study offers actionable strategies for debt reduction, optimized spending, and personalized financial planning based on risk profiles. The findings can support financial institutions in refining credit risk assessment models, promoting responsible lending, and contributing to the achievement of broader sustainability goals through improved financial inclusion and stability. Unsupervised learning techniques, such as K-nearest neighbors (KNN), DBSCAN, and rule-based methods, were applied to cluster defaulters based on their risk profiles. These clustering methods allowed us to distinguish various groups of defaulters, providing a nuanced view of risk categories. Financial institutions can use these risk categories to design tailored financial products and adjust lending strategies and policies for lower-risk groups or offer guidance to higher-risk defaulters on areas needing improvement, such as increasing income or enhancing credit scores. Governments in developing countries could make the most use of this study, where most of the population lacks financial knowledge and struggles to get financial help from private institutions once they are categorized as defaulters. For instance, insurance companies have different policies for different age groups, and financial institutions can also make such policies for different risk levels that benefit both parties in the long term. Received: 4 March 2025 | Revised: 20 June 2025 | Accepted: 2 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Amir Ahmad Dar: Conceptualization, Methodology, Validation, Resources, Writing – review & editing, Supervision, Project administration. Trushal Hirani: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Vanshita Arora: Writing – review & editing, Visualization. Sukriti Kakkar: Writing – review & editing, Visualization.
Artificial Intelligence in Sustainable Industrial Transformation: A Comparative Study of Industry 4.0 and Industry 5.0
Industry 4.0 continues to progress due to the implementation of artificial intelligence (AI), which enables the automation of operations and complex big data exploitation to improve production efficiency. Industry 5.0 emerged to tackle sustainability issues and ensure human focus because Industry 4.0 followed a profit-maximizing strategy that raised important concerns. This comparative study fills the knowledge gap about how AI contributes to Industry 5.0 sustainability by studying its differences from Industry 4.0. This review investigates how AI enables circular economy principles through a systematic literature review while studying its impact on industrial resilience and adaptive manufacturing capabilities. This study underscored that Industry 5.0 uses AI to unite human creativity with modern technologies, enabling sustainable operations and new developments. Data privacy, cybersecurity, and algorithmic bias remain persistent issues in the current context. This review found that AI-based systems implementation in Industry 5.0 led to a 30% boost in resource utilization compared to the Industry 4.0 approach and a 25% decrease in carbon emissions. These findings indicate that ethical AI frameworks must be the priority for policymakers and industries to achieve such technological development while keeping companies’ training in mind at the same time. The study establishes that AI is the fundamental technology to shift manufacturing toward a sustainable and human-controlled Industry 5.0 format that establishes resilient green industrial environments. The research develops the academic discussion about using AI to achieve sustainable industrial development. Received: 26 January 2025 | Revised: 12 March 2025 | Accepted: 23 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Muhammad Waqas: Conceptualization, Methodology, Software, Validation, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Adila Naseem: Formal analysis, Investigation, Resources, Data curation, Writing – original draft.
An Enhanced Data Collection System for Social Enterprises: Securing Impact with Machine Learning and Cryptography
The automated data collection system (ADCS) represents a thorough framework that is designed to tackle diverse data management issues within social enterprises. The ADCS implements data collection and analysis methods that are an accurate, secure, and scalable system utilizing automation and advanced cryptographic security while aligning with the Sustainable Development Goals (SDGs). The system uses latent Dirichlet allocation for thematic modeling and categorization, drawing insights to improve keyword relevance in contrast to traditional frequency-based methods. The system uses a cyclical feedback process that consistently enhances keyword evaluations to adapt to evolving data environments and maintain enduring accuracy. Role-based access control (RBAC) along with a strong cryptographic architecture ensures the safety of data. The ADCS provides a reliable and practical framework for making data-driven decisions while directly supporting social entrepreneurs, NGOs, academics, and policymakers. ADCS is a cutting-edge inclusive solution that streamlines SDG alignment while guaranteeing robust data security and empowering organizations to achieve lasting impact alongside operational excellence. The article outlines the system’s unique features and compares them with existing options that illustrate its ability to revolutionize automated data management practices in social enterprises and beyond. Received: 14 January 2025 | Revised: 20 March 2025 | Accepted: 21 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest related to this work. Data Availability Statement The data that support this work are available upon reasonable request from the corresponding author. Author Contribution Statement Fardin Muttaki: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft, Visualization, Project administration. Aqeel Sahi: Conceptualization,Validation, Formal analysis, Writing – original draft, Writing –review & editing, Supervision. Shahab Abdulla: Methodology, Validation, Investigation, Data curation, Writing – review & editing, Visualization, Supervision, Funding acquisition. Kaled Aljebur: Software, Resources.
Distributed Harmony: Intelligent Load Balancing in Heterogeneous Server Clusters Using Mobile Agents
Intelligent network load balancing deals with efficiently distributing incoming network traffic among a collection of servers. A mobile agent-based load balancing approach leverages intelligent agents to identify underutilized servers within a cluster and dynamically distribute incoming workloads to optimize resource allocation. Intelligent load balancing is crucial because most available network load balancing techniques are currently in use such as round robin, weighted round robin, least connection, and weighted least connect, and many others suffer from delay in response time, computational overhead, increased bandwidth usage, and traffic collision, and lack of distribution intelligence. An intelligent approach to network load balancing presents an algorithm that brings about equitable distribution of network traffic loads in cluster networks. Intelligent mobile agents use intelligent information to identify and generate responses on the availability and status of each server within the cluster and in turn equitably distribute the incoming network traffic to the available servers intelligently. Data was gathered using interviews, questionnaires, observations, and reviews of related literature. The analysis of the questionnaires was done using SPSS software. To assess the goodness of fit, a chi-square test was employed at the inferential level, providing statistical validation of the results. An object-oriented methodology was adopted. We also used the following tools: Java, MySQL, NVivo, and C#. The result clearly indicates that intelligent network load balancing techniques drastically reduce the response time delay and computational overhead usually experienced with other network load balancing techniques in use. It also facilitates the utilization of resources by providing a throughput with minimum response time, sharing the workload equally between the available servers depending on their status. Network traffic and bandwidth consumption are considerably reduced. The overall effect is that Internet usage is made more user-friendly both for professionals and nonprofessionals, thereby bringing about a reduction in cost. Received: 6 January 2025 | Revised: 3 July 2025 | Accepted: 11 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request from the corresponding author. Author Contribution Statement John Ugah: Conceptualization, Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Steven Ikporo: Software, Investigation, Data curation. Ifeanyi Achi: Software, Formal analysis, Investigation, Data curation. Ugochukwu Onwudebelu: Methodology, Formal analysis, Resources, Writing – original draft, Writing – review & editing, Visualization.
Cryptocurrency and Financial Sector: A Bibliometric Analysis
Objective: The key objective of this paper is to provide comprehensive trends in the topic of cryptocurrency and the financial sector. Utilizing bibliometric analysis, this study examined 1293 papers published between November 2014 and 2024 to ascertain the scope of research conducted over this period. Research Design and Methods: Data was collected from the Lens database, producing 1,293 papers. Utilizing VOSviewer, we investigated key elements of literature, including well-known organizations, authors, nations, and journals. We also looked at co-authorship, bibliographic coupling, and co-occurrence of terms. Findings: According to the findings, the greatest number of publications, largely from journals, were discovered in 2023. The most active authors were Elie Bouri, Andreas Hani, Horst Treiblmaier, and Jochen Michaelis and Julapa Jagtiani with seven documents each. The most common topics of investigation are cryptocurrency and business. The SSRN electronic journal topped the list. The top three productive countries in the field include the United States, China, and the United Kingdom. As evidenced by the findings, research on cryptocurrencies is expanding rapidly, yet there is still a deficiency of international author partnerships and scientific exchanges. Implications and Recommendations: The current study provides a comprehensive perspective and a thorough analysis of the relationship between cryptocurrencies and the financial sector, which form the theoretical foundations of the cryptocurrency deployment. Contribution and Value Added: Summing up 10 years of academic articles on the relationship between the financial sector and cryptocurrencies, this is one of the first bibliometric studies in the field. The bibliometric analysis provides a thorough knowledge base that enables scholars to make well-informed conclusions about industrial practices and policy formation. These results highlight important knowledge gaps and provide interesting directions for further study, consolidating the present understanding of cryptocurrency research in the financial industry. Received: 23 December 2024 | Revised: 13 March 2025 | Accepted: 8 May 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Lens at https://www.lens.org. Author Contribution Statement Karima Sayari: Conceptualization, Methodology, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Mayssa Ben Belgacem: Conceptualization, Methodology, Writing–review & editing, Visualization.
Dynamic Rollout Plan of China National Carbon Emissions Trading Scheme: A CGE-Based Analysis
China has announced ambitious emissions reduction targets to peak CO2 emissions by 2030 and achieve carbon neutrality before 2060. In pursuit of these objectives, the country intends to employ carbon emissions trading schemes as a key policy tool. The national Emissions Trading System (ETS) was launched in the power sector in 2021, with plans to progressively encompass additional sectors. Notably, there are lack of studies on the dynamic expansion during operation, and the entire process modeling of the carbon allowance allocation system. This paper addresses this gap by presenting a systematic modeling of the carbon allowance allocation policy. We introduce a dynamic Computable General Equilibrium (CGE) model that captures the entirety of the ETS process, enabling us to assess the implications of various allocation mechanisms on socio-economic outcomes and carbon emissions, with a particular focus on dynamic rollout strategies. Our findings indicate that a phased expansion strategy minimizes economic disruptions, with early expansions yielding more significant reductions in economic losses. Although a policy of free allowance allocation can diminish carbon prices and abatement costs for participating sectors, it may incur additional economic deficits. Furthermore, technological advancements and heightened electrification are expected to mitigate macroeconomic losses stemming from the carbon market. We also explore several alternative model parameters and designs for the carbon market. Our findings can provide policy recommendations in gradual expansion, auction allocation, market stability mechanism when further improving the national carbon market. Received: 20 December 2025 | Revised: 15 May 2025 | Accepted: 28 May 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Chang-Jing Ji: Conceptualization, Methodology, Software, Formal analysis, Writing – original draft, Funding acquisition. Xiaodan Wang: Formal analysis, Writing – review & editing, Supervision, Project administration, Funding acquisition.
Asset Class Volatility and Output Gap in Africa: A Big Data Analysis
This study investigates the relationship between asset class volatility and the output gap in selected African countries Nigeria, Ghana, Cameroon, and Côte d'Ivoire using machine learning techniques on daily financial data from 2010 to 2022. Employing advanced computational models, including Light Gradient Boosting Machine (LightGBM), the study achieves an R-squared of 0.68, demonstrating the effectiveness of big data analytics in economic research. The findings reveal that stock market volatility has the most significant impact on the output gap, followed by gold and crude oil, while Bitcoin exhibits the least influence. The study highlights the importance of stabilizing stock markets, leveraging gold as a financial hedge, managing crude oil price fluctuations, and regulating cryptocurrency markets to enhance macroeconomic stability. These insights provide valuable policy recommendations for mitigating financial volatility and ensuring sustainable economic growth in Africa. By integrating machine learning into economic research, the study offers a novel approach to understanding financial market dynamics and their implications for macroeconomic stability. Received: 27 November 2024 | Revised: 12 March 2025 | Accepted: 28 March 2025 Conflicts of Interest The author declares that he has no conflicts of interest in this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Richard Umeokwobi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition.
Enhancing E-commerce Security: A Hybrid Machine Learning Approach to Fraud Detection
In the rapidly expanding e-commerce landscape, ensuring the security of transactions is essential to maintain consumer trust. However, the challenge of accurately distinguishing between genuine and fraudulent transactions persists, largely due to issues such as dataset imbalance, suboptimal feature selection, and varying algorithm performance. As such, this study aims to enhance fraud detection accuracy by developing a hybrid model that combines an artificial neural network (ANN) with a deep neural network (DNN), employing the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and linear discriminant analysis (LDA) for effective feature extraction. By integrating SMOTE with LDA, the model is trained to better handle imbalanced datasets and extract relevant features, thereby improving its predictive capabilities. Our results demonstrate that the hybrid model outperforms individual models, achieving a precision rate of 95.46% and an area under the curve (AUC) score of 97.04%. In comparison, the stand-alone ANN model recorded an accuracy of 95.46% and an AUC of 96.92%, while the DNN achieved a success rate of 95.01% and an AUC of 97.17%. These outcomes highlight the significant advantages of combining advanced feature extraction and class imbalance techniques, resulting in superior detection performance. The study concludes that the hybrid model provides a robust solution for improving fraud detection in e-commerce, offering a reliable approach to differentiate between genuine and fraudulent transactions effectively. This approach not only addresses existing challenges but also sets a foundation for future research in enhancing transaction security through innovative deep learning methodologies. Received: 22 November 2024 | Revised: 13 January 2025 | Accepted: 14 March 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Mohit Vasant: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Swathi Ganesan: Writing – review & editing, Supervision. Ganapathy Kumar: Project administration.
Human Capital Development and Migration Patterns in Nigeria: A Ridge and Lasso Regression Analysis Using Python
This study investigates the relationship between human capital development and migration patterns in Nigeria from 1981 to 2021 using ridge and lasso regression techniques implemented in Python. Human capital development is proxied by government expenditure on education, health, social services, and gross fixed capital formation, while net migration serves as the dependent variable. The results indicate that human capital development positively influences net migration, suggesting a higher emigration rate relative to immigration. This highlights the paradox of brain drain, where increased investment in education and health enhances skill acquisition but simultaneously encourages outward migration due to limited domestic opportunities. The ridge regression model, preferred over lasso due to its robustness against multicollinearity, demonstrates a strong explanatory power with an R-squared value of 0.99. Policy recommendations emphasize the need for strategic interventions, including competitive wages, job creation, and entrepreneurship incentives, to retain skilled labor and mitigate the negative effects of migration. Additionally, fostering foreign direct investment and improving domestic economic conditions can enhance productivity and optimize the benefits of human capital development for national growth. Received: 21 November 2024 | Revised: 12 March 2025 | Accepted: 9 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Richard Umeokwobi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Visualization, Supervision. Auwal Isah: Conceptualization, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing. Elijah Oludele Akanni: Conceptualization, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Project administration. John Obeta: Conceptualization, Investigation, Resources, Data curation, Writing – review & editing, Project administration.
The Evolution of Sustainable Investment: The Role of Decentralized Finance and Green Bonds in the Efficiency and Transparency of Green Finance
Green finance is a cornerstone of sustainable investment, but it highlights the critical importance of transparency, traceability, and financial efficiency within an environmental, social, and governance framework. This review article examines the potential of green finance as a pillar for accelerating investment in sustainable pathways, particularly using green bonds and the innovative mechanisms offered by decentralized finance (DeFi). Green bonds are highlighted as a key instrument for channeling capital toward green projects, while DeFi is explored as an innovative tool with the potential to democratize access to finance and enable micro-investments in sustainability projects with a relevant social impact. This article examines both mechanisms in terms of their ability to increase the efficiency and reliability of green finance ecosystems. The analysis also explores emerging challenges such as regulatory constraints, the threat of greenwashing, and technological limitations associated with the implementation of blockchain and artificial intelligence. By addressing these barriers, the article provides strategic recommendations for achieving greater transparency and reliability in green finance markets, thereby fostering investor confidence and broader market growth. It also identifies research gaps and proposes new avenues to advance the integration of sustainable finance, ensuring its scalability and inclusion in the promotion of global sustainability. Received: 16 November 2024| Revised: 17 February 2025 | Accepted: 27 February 2025 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Natália Teixeira: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing, Visualization, Funding acquisition.
green finance and sustainable investment
A Machine Learning and Digital Twin-Based Assistance System for Computer Science Students' Career Prediction and Activity Recognition
The advancement of digital twin (DT), machine (ML), and deep learning (DL) technology has created new opportunities for students' activity recognition and career guidance in the educational sector. DT and ML technology can be used to update and monitor students' performance in various courses as well as daily life activity data. Existing research on student activity recognition and career suggestions did not create a dataset that included static, location, accelerometer, and academic data. They did not use digital twin technology for student performance monitoring. The existing ML-based works should have investigated various academic course data and job descriptions for the student's career recommendation with high accuracy. To deal with these issues, this paper creates an ML, DL, and DT-based assistance system for career recommendation and activity recognition based on student personal activity dat and academic course results. Several ML and DL models were tested for career suggestion prediction, and logistic regression achieved the highest accuracy of 98%. The results exhibit that the CNN-based DL model achieves the highest 96% accuracy for the student's daily activity recognition system. According to the performance comparison results, the proposed logistic regression-based career suggestion prediction system outperforms the existing works by at least 4% in accuracy and 7% in precision. The results also show that the proposed CNN-based activity recognition system achieves at least 5% higher accuracy and 3% higher precision values than previous works. Received: 30 October 2024 | Revised: 19 December 2024 | Accepted: 22 January 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Tabassum Ferdous: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Visualization, Project administration. Mahfuzulhoq Chowdhury: Conceptualization, Methodology, Writing - original draft, Writing - review & editing, Supervision, Project administration.