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 2026.
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.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
A Comparative Study of Penalized Regression Methods in Estimating and Predicting Economic Growth in Nigeria
This study investigated macroeconomic indicators-internal debt (INDT), external debt (EXDT), interest rate (RINR), exchange rate (REXR), and economy openness (OPEN) in modeling, estimating, and predicting Nigeria's economic growth (RGDP). To address multicollinearity and outlier problems, penalized least squares regression methods (LASSO, ridge, and elastic net) were employed. Results from the LASSO model indicated that INDT, RINR, REXR, and OPEN positively influenced RGDP, which contributed 4.27%, 0.40%, 0.49%, and 0.52%, respectively, whereas the influence of EXDT was negative (−0.97%). Ridge and elastic net estimations supported these results, with slight variations in coefficient magnitudes. All models emphasized the adverse effect of EXDT on RGDP. The evaluation and predictive power of the model metrics revealed that LASSO outperformed the other methods, revealing minimum root mean square error (0.2895), mean absolute error (0.2174), and mean absolute percentage error (2.13%), while explaining 91.9% variations in RGDP. Consequently, the results affirmed LASSO penalized technique as the most efficient variable selection with stable prediction under correlated and highly extreme dataset. Overall, the findings highlighted the crucial roles of internal borrowing and economy openness as growth-driven predictors, while stressing the adverse implications of reliance on foreign loans. Hence, the government should ensure proper management of the internally borrowed funds and channeled such funds by investing heavily in infrastructure, technology advancement, and innovation systems to maximize the growth benefits of the loans; application of penalized regression methods, particularly LASSO, should be mainstreamed into economic forecasting units within the government and research institutions to improve evidence-based policy formulation. Received: 13 June 2025 | Revised: 4 January 2026 | Accepted: 23 January 2026 Conflicts of Interest The author declares that he has no conflicts of interest in this work. Data Availability Statement The data that support the findings of this study are openly available in the central bank of Nigeria statistical bulletin at https://www.cbn.gov.ng. Author Contribution Statement Ayooluwade Ebiwonjumi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0/
The Analysis of Climate Risk Impacts on Food Price Volatility: Moderating Role of Green Innovation Technology
The increasing temperature anomaly and greenhouse gas emission and other related climate-events have imposed serious threats to agricultural output, leading to volatility of commodity prices across the globe. This paper analyzes the asymmetry between climate risks, green innovation, and food price volatility in Nigeria. Time-series data were collected and analyzed using generalized autoregressive conditional heteroscedasticity in conjunction with non-linear autoregressive distributed lag modelspanning from 1990 to 2023. Data for the variables are generated from World Bank, World Development Indicators and Central Bank of Nigeria Statistical Bulletin online database. We regress climate risk measured by carbon emission (CO2) and global average temperature anomaly on food price index and incorporate green innovation technology as a moderating variable while renewable energy consumption and population growth rate as control variables. To ascertain the asymmetric impacts, temperature anomaly and carbon emission (CO2) were decomposed into positive (+ve) and negative (−ve) partial sums. We find that positive temperature (Temp+) and CO2 shocks significantly increase food price volatility in the long and short terms. Conversely, negative shocks in CO2 and temperature anomaly yield long-term volatility reduction, suggesting mitigation potentials. We also find that green innovation technology improves environmental quality and boost food production. The study recommends that the Nigerian government should encourage adoption of smart agricultural practices, build climate mitigation strategies that will improve environmental quality, and sustainable food production. Received: 19 June 2025 | Revised: 22 September 2025 | Accepted: 11 November 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 Nuhu Musa: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing—original draft, Writing—review & editing, Visualization, Supervision, Project administration.
Similarities Between Cloud Computing and Insurance: Feasibility Study of Secondary Cloud Infrastructure
This study reveals the fundamental isomorphism between cloud computing resource allocation and insurance risk dispersion mechanisms through mathematical modeling. Under the independent and identically distributed (i.i.d.) assumption (demand/loss variables Xi ∼ Poissson(λi)), both systems share the core operational logic: VaR (p) < nVaRi (p), demonstrating that centralized management of resource/risk pools significantly enhances efficiency. Key findings include 1. Shared pricing strategy: The maximum price that users are willing to accept, calculated as PMi = λi(e k − 1)/k, is always greater than the provider’s minimum price λi. This implies that, under the assumptions of this paper, cloud computing transactions are always achievable. 2. Secondary Cloud Infrastructure: Inspired by excess-of loss reinsurance, we design a cross-cloud resource protocol where optimal threshold Ymi minimizes costs Ci = Ymi + Fi . This research provides a theoretical foundation for applying actuarial models to cloud computing, particularly for elastic resource pricing and disaster recovery. Received: 22 June 2025 | Revised: 17 October 2025 | Accepted: 14 November 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 Xiaobo Cai: Conceptualization, methodology, validation, formal analysis, investigation, data curation, writing – original draft, writing – review & editing, visualization, supervision, project administration. Haifei Qin: Software, validation, investigation, resources, data curation, supervision, funding acquisition.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
A Review on Bridging Brain-Inspired Mechanisms and Large-Scale Pre-trained Models: Toward Adaptive, Efficient, and Interpretable AI
Large-scale pre-trained models, such as GPT, greatly improve numerous areas of artificial intelligence (AI), including natural language understanding, image recognition, and the integration of various data types. However, these models continue to encounter significant challenges, including excessive computational resource requirements, inadequate adaptability to dynamic environments, susceptibility to catastrophic forgetting, and limited internal interpretability. By comparison, the human brain exhibits efficient learning from sparse data, demonstrates robust adaptability across diverse contexts, operates with minimal energy consumption, retains information over extended periods, and can be elucidated through its underlying cognitive processes. This review examines recent research and is the first to categorize brain-inspired methods into three key dimensions. It explores how mechanisms of the human brain, such as hierarchical and modular designs, biologically inspired attention mechanisms, memory enhancement strategies, synaptic plasticity, and predictive coding, could inspire optimizations for large-scale models. This review not only synthesizes the current state of the field but also proposes potential directions for future research. There remains a need for stronger theories, better testing methods, improved hardware–software coordination, and careful consideration of ethical issues. Overcoming these challenges will require closer collaboration between neuroscientists, cognitive scientists, machine learning experts, and engineers. Through such collaboration, the field may develop the next generation of AI systems that perform at the highest levels while being more efficient, flexible, and transparent. Received: 28 June 2025 | Revised: 27 October 2025 | Accepted: 21 November 2025 Conflicts of Interest Zongwei Luo is the Editor-in-Chief of FinTech and Sustainable Innovation and was not involved in the editorial review or the decision to publish this article. 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 Jingzhe Wang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Yajing Wang: Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Zongwei Luo: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.
Empowering Long-Tail Investors Through Financial Technology: Evidence from Robo-Advisory Adoption in China
Financial technology (FinTech) has emerged as a catalyst for global sustainability projects as technology advances. Digital breakthroughs such as blockchain, artificial intelligence-powered analytics, and inclusive digital platforms are being used to democratize climate resilience investments and enhance environmental, social, and governance alignment across financial institutions. The purpose of this study is to perform an empirical inquiry of the potential of robo-advisors to see if they can make wealth management more accessible to individual investors by increasing affordability. The authors use longitudinal panel data from the China Household Finance Survey and the Digital Financial Inclusion Index to investigate the impact of FinTech infrastructure on automated investment advising service acceptance. The findings indicate that robo-advisors dramatically increase the number of retail investors. The inclusion of a larger number of people in the financial system can promote and encourage more logical investment decisions. Given these findings, it is clear that FinTech-driven solutions have the ability to close investment service gaps and promote equitable and sustainable financial inclusion in developing economies. Received: 7 July 2025 | Revised: 10 December 2025 | Accepted: 17 April 2026 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 Fei Wei: Formal analysis, Data curation, Writing – original draft, Funding acquisition. Jianting Zhao: Formal analysis, Investigation, Data curation, Visualization. Yuting Zheng: Formal analysis, Investigation, Data curation, Visualization. Fangyuan Zhu: Formal analysis, Investigation, Data curation, Visualization. Yu Zhu: Formal analysis, Investigation, Data curation, Visualization. Ruyi Ding: Formal analysis, Investigation, Data curation, Visualization. Anna Xi Chen: Methodology, Software, Writing – original draft. Sonia Chien-I Chen: Conceptualization, Validation, Formal analysis, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
Fintech as a Catalyst for Sustainable Development in Food Systems
As global food systems face increasing pressures from climate change, resource scarcity, and financial exclusion, fintech has emerged as a transformative force with the potential to drive sustainable development. Production, processing, distribution, and consumption of food have become increasingly unsustainable, with severe environmental, social, and economic implications. This threatens the fulfillment of UN Sustainable Development Goals (SDGs), particularly the ambitious target of ending hunger and malnutrition by 2030. Current trajectories reveal the urgency with which we need to address emerging and existing challenges, such as climate change, inefficiencies that lead to the wastage of nearly one-third of all food produced, and problems that perpetuate deep-rooted inequalities, leaving billions without access to adequate nutrition. This paper adopts a narrative review approach, synthesizing existing literature and case studies to explore the role of financial technologies (fintech) in addressing challenges within global food systems in improving supply chain transparency and strengthening the resilience of smallholders and agribusinesses. By bridging gaps in credit, risk management, and market connectivity, fintech offers innovative pathways to support inclusive, efficient, and climate-resilient food systems. By combining an analysis of fintech’s current applications with a call for greater awareness and action, this review underscores the critical role of financial innovation in building sustainable food systems and achieving the SDGs by 2030. Received: 12 July 2025 | Revised: 12 September 2025 | Accepted: 23 October 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 Shoba Suri: Formal analysis, Investigation, Writing – original draft, Writing – review & editing.
An Empirical Study on the Adoption Determinants of Permissioned Blockchain as Decentralized Inter-organizational Systems
Permissioned blockchain is a technology relevant for business-to-business transactions among partners with low trust. An extensive literature review indicates an inadequate comprehension of the adoption determinants of permissioned blockchain as the next generation of decentralized inter-organizational systems. To address this gap, this study builds the theoretical model by extending the technology–organization–environment framework to the permissioned blockchain context and incorporating inter-organizational factors. A mixed methodology is adopted to identify and investigate the determinants, including interviews with 10 senior information technology (IT) executives and a questionnaire survey of IT managers with blockchain experience from 212 organizations. A total of 11 factors is identified through literature review and interviews. The empirical results indicate that top management support is the most significant determinant, followed by perceived benefits, technology maturity, trading partner readiness, and perceived advantages of the blockchain industry consortium. Besides, competition intensity is identified as a weak determinant. The implications for theoretical development and professional practice are examined. Longitudinal and latitudinal studies are recommended for future research. Received: 2 August 2025 | Revised: 16 October 2025 | Accepted: 4 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Leo Yeung: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Yulin Fang: Writing – review & editing, Supervision. Ting Xu: Writing – original draft, Writing – review & editing.
decentralized inter-organizational systems
Impact of Corruption-Driven Vulnerabilities of an Emerging Economy in Achieving Economic Prosperity and Strengthening the Anti-Money Laundering Legal Framework
This paper investigates the impact of corruption on economic growth. It discusses the corruption-driven vulnerabilities in achieving economic prosperity and preventing money laundering in emerging economies where corruption is prevalent. This paper employs a combination of quantitative and qualitative approaches, utilizing robust regression in the first phase to investigate the relationship between corruption and economic growth. The second phase employs qualitative analysis, in which 66 convictions are categorized and analyzed to provide micro-level evidence supporting the empirical findings on corruption and economic growth. The empirical results indicate that corruption is detrimental to economic growth, posing a significant challenge for emerging economies. The second-phase analysis reveals that widespread corruption is a significant barrier to implementing an effective regime to combat money laundering and terrorist financing in emerging countries with high levels of corruption. The corrupt policymaking, government administration, and management of resources, including lands, the education system, and law enforcement processes, hinder the effective implementation of the anti-money laundering (AML) regime due to higher human intervention. Therefore, this study proposes to develop a comprehensive payment ecosystem that reduces human intervention to improve the effectiveness of the AML regime and address many macroeconomic issues driven by the government's fiscal limitations. This paper provides only a conceptualized model based on the analysis of convictions related to bribery and corruption in the Sri Lankan context. Received: 12 August 2025 | Revised: 21 January 2026 | Accepted: 28 February 2026 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 Transparency International at https://www.transparency.org/en/gcb and https://www.transparency.org/en/cpi/2024 and the World Bank at https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS and https://data.worldbank.org/indicator/NY.GDP.MKTP.KD. Author Contribution Statement Sisira Dharmasri Jayasekara: 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.
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.
The Potential Impacts of Some Economic and Financial Variables on Private Investment Using the Augmented ARDL Approach
The study examined the effectiveness of some financial and economic variables in Iraq on private investment and whether the impact was positive or negative. Based on the assumptions of the study, which stated that government spending, gross domestic product (GDP), inflation, and unemployment significantly affect the performance of private investment in the Iraqi economy, causing to retreat in the level of private investment in Iraq. and for the purpose of solving the research problem, which is represented by to retreat in the role of private investment in Iraq due to its influence on financial and macroeconomic variables such as government spending. The GDP suffers from significant fluctuation as a result of weak economic diversification, as well as high rates of inflation and some variables. Also, how the independent variables (financial and economic) affect the dependent variable (private investment), augmented autoregressive distributed lag approach was relied upon, relying on annual data from 1990 to 2023. The study reached several conclusions, the most important of which are: It was found that there is a long-term cointegration relationship between the variables, as well as the existence of short-term equilibrium relationships, and that the decline and fluctuation in the independent variables had a negative impact on the dependent variable. As for the recommendations, the most important are that there should be a major role for the private sector and that laws and legislation give more space to the private sector in economic activity. Received: 21 April 2025 | Revised: 26 September 2025 | Accepted: 27 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Fahad Mghemish Huzayran: Conceptualization, Methodology, Investigation, Writing – original draft, Supervision, Project administration. Amer Mohammed Ahaimed: Software, Formal analysis, Investigation. Hebat Allah Mustafa Al-Sayyid Ali: Validation, Writing – review & editing, Visualization.
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 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.
A Digital Economy Approach to Enhance Transparency in Property Valuation via Proptech
Property valuation, a foundational method for governments, financial institutions, and insurers to gauge economic stability, remains hindered by opaque, fragmented data practices. Despite technological advancements like Artificial Inteligence (AI) and Web3, valuation processes rely on siloed, non-standardized data that institutions rarely share—even internally. This paper identifies systemic barriers to global transparency and proposes a Proptech framework to resolve this disconnect. Unlike market valuation, which leverages AI and algorithms to predict prices, housing valuation depends on manual audits and confidential metrics. This lack of transparency limits governments' capacity to preempt real estate crises or curb speculative risks. By integrating blockchain-enabled data sharing and AI analytics, a decentralized Proptech platform, sharing a global network, could standardize and democratize valuation data, enabling real-time insights for crisis management and evidence-based policymaking. The study highlights how such innovation could transform urban planning, financial markets, and economic resilience, positioning Proptech as a catalyst for equitable, transparent valuation ecosystems. Received: 7 April 2025 | Revised: 29 July 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 Pedro Faria: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Peter Finn: Conceptualization, Validation, Investigation, Resources, Writing – review & editing. Tiago Navarro: Conceptualization, Resources, Writing – review & editing, Visualization, Supervision.
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)
Emergence of Fintech in the Financial Landscape: Stakes of Fintech and Competition with Traditional Banks
The purpose of this paper is to empirically study the stakes of Fintech and competition with traditional banks. Through interviews with 18 specialists working in various fintechs, the empirical results show that most of the fintechs elaborate on their in-house technology available via an online customizable platform, while other functionalities considered as out of the core Fintech activities are developed via external outsourcing. Fintechs face significant systemic challenges, including an evolving regulatory framework that can constrain ambitions and the need for rigorous compliance processes. Protecting personal data against hackers is also vital for these start-ups. The paper further shows that banks are not fundamentally threatened by fintech, given their essential role in the economy, especially credit intermediation. The relationship between the two appears more cooperative than competitive, with growing momentum for banks to acquire fintechs to accelerate digitalization and foster innovation. The scientific novelty lies in framing dual tech as a common operating pattern and showing that banks and fintechs are complementary through partnerships and acquisitions. Received: 2 April 2025 | Revised: 14 October 2025 | Accepted: 27 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 Fateh Saci: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Sajjad M. Jasimuddin: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
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.
Credit Rating in the Age of Artificial Intelligence
Recent advancements in technology, particularly artificial intelligence (AI), have profoundly impacted financial systems. One area undergoing significant change is credit scoring and rating, where traditional methods—long reliant on limited, standardized data—are now being reshaped by advanced algorithms. AI-driven credit assessment leverages vast and diverse datasets, including nontraditional sources such as transaction behaviors and digital footprints, enabling more accurate and personalized risk evaluations. This shift not only improves efficiency but also expands access to credit for underserved populations who lack conventional financial records. However, these innovations come with challenges. The complexity of AI models often makes them difficult to interpret, raising concerns about accountability and bias. Additionally, the use of alternative data sources introduces privacy and ethical considerations, calling for stronger regulatory frameworks. This paper examines real-world applications of AI in credit scoring, compares its effectiveness with traditional methods, and discusses emerging regulatory responses. While AI holds great promise in democratizing credit and refining risk assessment, its responsible implementation requires balancing innovation with transparency, fairness, and consumer protection. Addressing these challenges will be key to building a more inclusive and resilient financial system 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.
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.
Apartment Purchase Suitability Prediction Using Explainable Machine Learning
Apartment purchase suitability checking is important for customers due to apartment price, location, and customer's income issues. Existing research works could not incorporate essential features and ML models for apartment purchase suitability checking. The accuracy of their work is also low. They did not investigate proper feature selection method, data preparation, class imbalance issue solving, and hyper parameter tuning techniques. This paper introduces a ML based framework for predicting the suitability of apartment purchases by considering a number of new factors, advanced preprocessing, feature selection, and hyper parameter tuning methods. This work’s preprocessing procedures included class balancing, addressing missing values, normalization, and ensemble feature selection technique. This paper examined 6 ML models for best prediction model selection and Grid Search CV is used for hyper parameter tuning. The random forest appears as most suitable ML model with 89.48% accuracy and 89.50% F1 score. According to the results, the proposed RF model outperformed previous works by offering more than 2 percent gain in accuracy and F1 score. This paper also included feature importance analysis based on SHAP. Received: 10 December 2025 | Revised: 17 March 2026 | Accepted: 9 May 2026 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 Asim Foize Aimon: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Visualization. Mahfuzulhoq Chowdhury: Conceptualization, Methodology, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Blockchain in REDD+ Projects: Potential and Challenges for Transparency and Efficiency in the Forest Carbon Market
Green finance and carbon markets are key to the global fight against climate change. Among them, Reducing Emissions from Deforestation and Forest Degradation (REDD+) projects represent a critical mechanism for channeling resources into forest conservation. However, these projects face challenges related to monitoring, reporting, and verification, which require transparency and low operating costs. These challenges, including opaque data, high operational costs, manual processes, and reliance on trusted intermediaries, fuel market skepticism, increase fraud risks, and ultimately constrain project financing. This systematic review examines the growing role of Blockchain technology in addressing these systemic challenges. By synthesizing the existing literature, this article analyzes how the inherent properties of Blockchain, such as decentralization, immutability, and transparency, can increase the credibility of carbon credit management, mitigate the risks of double-counting and fraud, and democratize access to carbon markets through credit tokenization. However, the findings indicate a significant gap between theoretical promise and practical application. Conceptual proposals, lacking empirical validation and detailed technical architectures, dominate the field. The review also critically assesses persistent barriers, including regulatory uncertainty, technological complexity, and concerns about energy consumption and governance. The study's main contribution is a synthesis that reframes the problem, moving beyond technological optimism to underscore the necessity of holistic, interdisciplinary integration. It concludes that successful integration of Blockchain technology requires multidisciplinary collaboration, standardized frameworks, and inclusive governance models. Finally, the review identifies key research gaps and proposes research directions for advancing the field beyond conceptual discourse toward practical implementation. Received: 16 November 2025 | Revised: 21 January 2026 | Accepted: 9 February 2026 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 Marcela Ceschim Caburlão: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Carlo Kleber da Silva Rodrigues: Conceptualization, Methodology, Validation, Writing – review & editing, Visualization, Supervision, Project administration.
Building a Digital Infrastructure for Blue Economy Investment Through FinTech
The sustainable blue economy represents a critical yet underfinanced frontier in global climate and biodiversity governance. Despite its potential to drive inclusive growth and ocean resilience, investment flows into marine and coastal sectors remain fragmented, opaque, and institutionally constrained. This article examines how financial technology (FinTech) innovations can build a digital infrastructure to mobilize and govern blue economy investments effectively. Using a mixed-method framework that integrates case studies of digital blue bonds, blockchain-based traceability systems, and artificial intelligence-driven impact assessment platforms, the paper develops a conceptual model for a digital blue finance infrastructure. The model highlights the interoperability between tokenized financing instruments, distributed ledger verification, and data analytics for transparent monitoring of marine investment outcomes. Empirical findings suggest that FinTech-enabled systems can significantly enhance accountability, reduce transaction costs, and attract blended capital into sustainable ocean projects. Furthermore, the study situates digital blue finance within emerging global policy frameworks such as the EU Sustainable Finance Strategy, the United Nations Ocean Decade, and the Association of Southeast Asian Nations blue finance initiatives. By linking FinTech architecture with environmental governance, the paper contributes to both sustainable finance theory and practical innovation design. It argues that digitalization is not merely a financial enabler but a structural catalyst for scaling equitable, accountable, and climate-aligned blue economy investments. Received: 26 October 2025 | Revised: 26 January 2026 | Accepted: 25 February 2026 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 Kolawole Afuwape: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
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.
Blue Finance: Financial Innovation for Sustainable Oceans
We review the literature on blue finance to develop a conceptual framework. We include peer-reviewed studies and selected policy reports in our analysis. These aid in the mapping of instruments, governance mechanisms, and financial technology (fintech)-enabled monitoring. This nascent topic links capital raising and allocation to the use of ocean resources. It is aligned with United Nations Sustainable Development Goal 14 (conserving and sustainably using oceans, seas, and marine resources). Through the literature, we examine blue bonds, debt-for-nature swaps, and blended finance. We assess how the literature links risk and return to impact credibility. We illustrate how fintech supports monitoring, verification, and disclosure. Our PRISMA analysis identifies substantive research on governance, particularly in the areas of marine protected financing, coastal resilience, and regulatory frameworks. These concepts ensure credible measurement and reduce information asymmetry. In this context, we discuss how digital finance mechanisms, including blockchain verification and blue carbon markets, can enhance transparency and traceability. We argue that blue finance can shift investor preferences and reduce the risk of "blue-washing," the equivalent of greenwashing. Received: 21 October 2025 | Revised: 2 February 2026 | Accepted: 5 March 2026 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 Daniel Broby: Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Jose Vicente Camus: Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing.
Sustainable Development Goal 14 (SDG 14)