FinTech and Sustainable Innovation
type: channel
description: 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 March 2026.
publisher: Bon View Publishing Ptd., Ltd
language: en
publication name: FinTech and Sustainable Innovation
items: Credit Card Fraud Detection Through Deep Learning and Real-Time Data Streams: A Comparison and New Directions
items: The Analysis of Climate Risk Impacts on Food Price Volatility: Moderating Role of Green Innovation Technology
items: Similarities Between Cloud Computing and Insurance: Feasibility Study of Secondary Cloud Infrastructure
items: A Review on Bridging Brain-Inspired Mechanisms and Large-Scale Pre-trained Models: Toward Adaptive, Efficient, and Interpretable AI
items: Fintech as a Catalyst for Sustainable Development in Food Systems
items: An Empirical Study on the Adoption Determinants of Permissioned Blockchain as Decentralized Inter-organizational Systems
items: Sociotechnical Transformation: A Systematic Review on the Impact of Artificial Intelligence on Society and Organizations
items: Impact of Job Stress on Employee Health and Performance: Evidence from Private Banks in Hailakandi District of Assam
items: Auditing and Decision-Making in the Context of Industry 5.0: An Exploratory Study in Costa Rica
items: The Potential Impacts of Some Economic and Financial Variables on Private Investment Using the Augmented ARDL Approach
items: Effective Cybersecurity Strategies for Mitigating Remote Work and IoT Risks in Enterprises
items: The Impact of Oil Prices on Guyana's Real Exchange Rate: An AlphaFold-Decomposition Analysis
items: A Digital Economy Approach to Enhance Transparency in Property Valuation via Proptech
items: Analyzing the Impact of International E-commerce Regulations and WTO Frameworks on Corporate Governance in Digital Enterprises
items: Emergence of Fintech in the Financial Landscape: Stakes of Fintech and Competition with Traditional Banks
items: Toward Fish Silage Emulsion from Barbados: A Mixture of Experts Analysis
items: Digital Business Transformation in Advertising: Effects on E-services Revenue
items: The Hurricane of Debt Hammering International Trade of Selective CARICOM Member States: Why the Region Needs Structural Reform of Official Development Assistance
items: Credit Rating in the Age of Artificial Intelligence
items: Marketing Strategies and Tactics in the Metaverse: A Systematic Literature Review and Future Research Agenda
items: Stock Price Prediction with LLM-Guided Market Movement Signals and Transformer Model
items: Navigating Innovation, Inclusion, and Ethical Challenges in AI-Driven Fintech: The Philippines
items: Next-Gen Threat Hunting: A Comparative Study of ML Models in Android Ransomware Detection
items: On the Efficient and Optimal Predictive Values for Economic Growth with Covariate Predictors: Ordinary Least Square and Ridge Regression Approach
items: Predictive Analytics for Personalized Debt Management: Leveraging Machine Learning to Provide Actionable Financial Advice
items: Artificial Intelligence in Sustainable Industrial Transformation: A Comparative Study of Industry 4.0 and Industry 5.0
items: Understanding Elon Musk's Acquisition of Twitter Through the Hubris Hypothesis: An Examination of Behavioral Finance, Governance, and Decision‑making
items: An Enhanced Data Collection System for Social Enterprises: Securing Impact with Machine Learning and Cryptography
items: Distributed Harmony: Intelligent Load Balancing in Heterogeneous Server Clusters Using Mobile Agents
items: Cryptocurrency and Financial Sector: A Bibliometric Analysis

Credit Card Fraud Detection Through Deep Learning and Real-Time Data Streams: A Comparison and New Directions
type: item
description: 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: …
subject: Research Articles
  topic: section
subject: clustering algorithms
  topic: keywords
subject: data streams
  topic: keywords
subject: pattern recognition
  topic: keywords
subject: time series analysis
  topic: keywords
subject: deep learning
  topic: keywords
subject: fraud detection
  topic: keywords
creator: Elias Polytarchos
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-09-01
publication date: 2025-09-01
starting page: 1
ending page: 9
doi: 10.47852/bonviewFSI52026108

The Analysis of Climate Risk Impacts on Food Price Volatility: Moderating Role of Green Innovation Technology
type: item
description: 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 Contributi…
subject: Research Articles
  topic: section
subject: climate change
  topic: keywords
subject: environmental threats
  topic: keywords
subject: agriculture
  topic: keywords
subject: low-carbon economy
  topic: keywords
subject: green technologies
  topic: keywords
subject: resilience
  topic: keywords
subject: sustainable development
  topic: keywords
creator: Nuhu Musa
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-12-01
publication date: 2025-12-01
starting page: 1
ending page: 10
doi: 10.47852/bonviewFSI52026524

Similarities Between Cloud Computing and Insurance: Feasibility Study of Secondary Cloud Infrastructure
type: item
description: 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.
subject: Research Articles
  topic: section
subject: cloud-insurance isomorphism
  topic: keywords
subject: risk aggregation var
  topic: keywords
subject: cloud resource pricing
  topic: keywords
subject: secondary cloud infrastructure
  topic: keywords
creator: Xiaobo Cai
creator: Haifei Qin
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-12-01
publication date: 2025-12-01
starting page: 1
ending page: 9
doi: 10.47852/bonviewFSI52026541

A Review on Bridging Brain-Inspired Mechanisms and Large-Scale Pre-trained Models: Toward Adaptive, Efficient, and Interpretable AI
type: item
description: 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 a…
subject: Review
  topic: section
subject: large-scale pre-trained models
  topic: keywords
subject: brain-inspired optimization
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: event-driven computation
  topic: keywords
subject: continual learning
  topic: keywords
subject: interpretability
  topic: keywords
creator: Jingzhe Wang
creator: Yajing Wang
creator: Zongwei Luo
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-11-28
publication date: 2025-11-28
starting page: A15
ending page: A15
doi: 10.47852/bonviewFSI52026630

Fintech as a Catalyst for Sustainable Development in Food Systems
type: item
description: 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 & ed…
subject: Review
  topic: section
subject: fintech
  topic: keywords
subject: sustainable development
  topic: keywords
subject: food system
  topic: keywords
subject: climate resilience
  topic: keywords
subject: agriculture
  topic: keywords
subject: value chain
  topic: keywords
creator: Shoba Suri
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-11-10
publication date: 2025-11-10
starting page: 1
ending page: 9
doi: 10.47852/bonviewFSI52026787

An Empirical Study on the Adoption Determinants of Permissioned Blockchain as Decentralized Inter-organizational Systems
type: item
description: 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.
subject: Research Articles
  topic: section
subject: decentralized inter-organizational systems
  topic: keywords
subject: permissioned blockchain
  topic: keywords
subject: innovation adoption
  topic: keywords
subject: TOE framework
  topic: keywords
subject: business-to-business
  topic: keywords
creator: Leo Yeung
creator: Yulin Fang
creator: Ting Xu
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-01-21
publication date: 2026-01-21
starting page: 1
ending page: 13
doi: 10.47852/bonviewFSI62027046

Sociotechnical Transformation: A Systematic Review on the Impact of Artificial Intelligence on Society and Organizations
type: item
description: 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, …
subject: Review
  topic: section
subject: sociotechnical transformation
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: algorithmic governance
  topic: keywords
subject: labor and identity
  topic: keywords
subject: surveillance
  topic: keywords
subject: social inequality
  topic: keywords
creator: Marc Selgas-Cors
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-08-26
publication date: 2025-08-26
starting page: 1
ending page: 16
doi: 10.47852/bonviewFSI52026076

Impact of Job Stress on Employee Health and Performance: Evidence from Private Banks in Hailakandi District of Assam
type: item
description: 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.
subject: Research Articles
  topic: section
subject: job stress
  topic: keywords
subject: employee health
  topic: keywords
subject: employee performance
  topic: keywords
subject: private banking sector
  topic: keywords
subject: workplace well-being
  topic: keywords
creator: Panchali Dey
creator: Soumendra Bhattacharjee
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-10-31
publication date: 2025-10-31
starting page: 1
ending page: 6
doi: 10.47852/bonviewFSI52026057

Auditing and Decision-Making in the Context of Industry 5.0: An Exploratory Study in Costa Rica
type: item
description: 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: …
subject: Research Articles
  topic: section
subject: audit management
  topic: keywords
subject: blockchain technology
  topic: keywords
subject: decision-making
  topic: keywords
subject: Industry 5.0
  topic: keywords
subject: organizational compliance
  topic: keywords
subject: SMEs
  topic: keywords
creator: Gabriel Silva Atencio
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-09-30
publication date: 2025-09-30
starting page: 1
ending page: 8
doi: 10.47852/bonviewFSI52026035

The Potential Impacts of Some Economic and Financial Variables on Private Investment Using the Augmented ARDL Approach
type: item
description: 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 available on request from the corresponding author upon reasonable request. Author Contribution Statement Fahad Huzayran: Conceptualization, Methodology, Investigation, Writing – original draft, Supervision, Project administ…
subject: Research Articles
  topic: section
subject: financial variables
  topic: keywords
subject: economic variables
  topic: keywords
subject: macroeconomic variables
  topic: keywords
subject: augmented ARDL
  topic: keywords
subject: Iraq
  topic: keywords
creator: Fahad Mghemish Huzayran
creator: Amer Mohammed Ahaimed
creator: Hebat Allah Mustafa Al-Sayyid Ali
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-11-28
publication date: 2025-11-28
starting page: 1
ending page: 9
doi: 10.47852/bonviewFSI52025982

Effective Cybersecurity Strategies for Mitigating Remote Work and IoT Risks in Enterprises
type: item
description: 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…
subject: Research Articles
  topic: section
subject: cybersecurity
  topic: keywords
subject: IoT
  topic: keywords
subject: remote work
  topic: keywords
subject: risk mitigation
  topic: keywords
subject: threat detection
  topic: keywords
subject: zero-trust
  topic: keywords
creator: Gabriel Silva Atencio
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-09-01
publication date: 2025-09-01
starting page: A13
ending page: A13
doi: 10.47852/bonviewFSI52025962

The Impact of Oil Prices on Guyana's Real Exchange Rate: An AlphaFold-Decomposition Analysis
type: item
description: 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 adm…
subject: Research Articles
  topic: section
subject: oil prices
  topic: keywords
subject: real effective exchange rate
  topic: keywords
subject: Guyana
  topic: keywords
subject: Dutch disease
  topic: keywords
subject: AlphaFold-Decomposition
  topic: keywords
creator: Don Charles
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-07-10
publication date: 2025-07-10
starting page: 1
ending page: 15
doi: 10.47852/bonviewFSI52025903

A Digital Economy Approach to Enhance Transparency in Property Valuation via Proptech
type: item
description: 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 – reviewing & editing, Visualization, Supervision, Project administration. Peter Finn: Conceptualization, Validation, Investigation, Resources, Writing – reviewing & editing. Tiago Marques: Conceptualization, Resources, Writing – reviewing & editing, Visualization, Supervision.
subject: Research Articles
  topic: section
subject: real estate
  topic: keywords
subject: proptech
  topic: keywords
subject: market crisis
  topic: keywords
subject: crowdsource and data transparency
  topic: keywords
subject: AI and machine learning
  topic: keywords
subject: digital economy
  topic: keywords
creator: Pedro Faria
creator: Peter Finn
creator: Tiago Navarro
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-11-07
publication date: 2025-11-07
starting page: 1
ending page: 7
doi: 10.47852/bonviewFSI52025859

Analyzing the Impact of International E-commerce Regulations and WTO Frameworks on Corporate Governance in Digital Enterprises
type: item
description: 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 draf…
subject: Research Articles
  topic: section
subject: international e-commerce regulations
  topic: keywords
subject: WTO frameworks
  topic: keywords
subject: corporate governance
  topic: keywords
subject: cross-border trade
  topic: keywords
subject: data protection
  topic: keywords
subject: General Data Protection Regulation (GDPR)
  topic: keywords
subject: consumer protection and liability
  topic: keywords
creator: Kolawole Afuwape
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-10-14
publication date: 2025-10-14
starting page: 1
ending page: 17
doi: 10.47852/bonviewFSI52025834

Emergence of Fintech in the Financial Landscape: Stakes of Fintech and Competition with Traditional Banks
type: item
description: 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.
subject: Research Articles
  topic: section
subject: fintech
  topic: keywords
subject: traditional banks
  topic: keywords
subject: competition
  topic: keywords
subject: digitalization
  topic: keywords
subject: financial innovation
  topic: keywords
creator: Fateh Saci
creator: Sajjad M. Jasimuddin
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-11-12
publication date: 2025-11-12
starting page: 1
ending page: 12
doi: 10.47852/bonviewFSI52025824

Toward Fish Silage Emulsion from Barbados: A Mixture of Experts Analysis
type: item
description: 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.
subject: Research Articles
  topic: section
subject: mixture of experts model
  topic: keywords
subject: fertilizer prices
  topic: keywords
subject: fish silage emulsion
  topic: keywords
subject: Barbados
  topic: keywords
creator: Don Charles
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-08-01
publication date: 2025-08-01
starting page: 1
ending page: 18
doi: 10.47852/bonviewFSI52025821

Digital Business Transformation in Advertising: Effects on E-services Revenue
type: item
description: 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.
subject: Research Articles
  topic: section
subject: digital business transformation
  topic: keywords
subject: e-services revenue
  topic: keywords
subject: digital media
  topic: keywords
subject: MENA countries
  topic: keywords
subject: digital marketing strategy
  topic: keywords
subject: advertising effectiveness
  topic: keywords
subject: ARDL model
  topic: keywords
creator: Ahmad Haidar
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-09-11
publication date: 2025-09-11
starting page: 1
ending page: 10
doi: 10.47852/bonviewFSI52025810

The Hurricane of Debt Hammering International Trade of Selective CARICOM Member States: Why the Region Needs Structural Reform of Official Development Assistance
type: item
description: 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, Res…
subject: Research Articles
  topic: section
subject: CARICOM
  topic: keywords
subject: balance of trade
  topic: keywords
subject: public debt
  topic: keywords
subject: official development assistance
  topic: keywords
subject: deep residual network
  topic: keywords
creator: Don Charles
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-06-12
publication date: 2025-06-12
starting page: A5
ending page: A5
doi: 10.47852/bonviewFSI52025727

Credit Rating in the Age of Artificial Intelligence
type: item
description: 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 Mahm…
subject: Review
  topic: section
subject: credit rating
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: big data
  topic: keywords
subject: ethical concerns
  topic: keywords
subject: default rate
  topic: keywords
creator: Milad Shahvaroughi Farahani
creator: Gholamreza Mahmoudi
creator: Ghazal Ghasemi
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-09-26
publication date: 2025-09-26
starting page: A9
ending page: A9
doi: 10.47852/bonviewFSI52025716

Marketing Strategies and Tactics in the Metaverse: A Systematic Literature Review and Future Research Agenda
type: item
description: 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, Soft…
subject: Review
  topic: section
subject: metaverse marketing
  topic: keywords
subject: marketing strategies
  topic: keywords
subject: digital marketing
  topic: keywords
subject: influencer marketing
  topic: keywords
subject: virtual marketing
  topic: keywords
subject: metaverse consumer behavior
  topic: keywords
subject: systematic literature review (SLR)
  topic: keywords
creator: Mohammad Faruk
creator: Sagor Ejarder
creator: Rabeya Jannat
creator: Md. Imran Hossain
creator: Md. Hafizur Rahman
creator: Md. Shahanur Islam
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-09-10
publication date: 2025-09-10
starting page: 1
ending page: 17
doi: 10.47852/bonviewFSI52025719

Stock Price Prediction with LLM-Guided Market Movement Signals and Transformer Model
type: item
description: 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 Statemen…
subject: Research Articles
  topic: section
subject: deep learning
  topic: keywords
subject: financial news
  topic: keywords
subject: LLM
  topic: keywords
subject: stock price prediction
  topic: keywords
subject: sentiment analysis
  topic: keywords
subject: transformer
  topic: keywords
creator: Qizhao Chen
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-07-23
publication date: 2025-07-23
starting page: A12
ending page: A12
doi: 10.47852/bonviewFSI52025703

Navigating Innovation, Inclusion, and Ethical Challenges in AI-Driven Fintech: The Philippines
type: item
description: 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.
subject: Review
  topic: section
subject: artificial intelligence (AI)
  topic: keywords
subject: fintech
  topic: keywords
subject: financial inclusion
  topic: keywords
subject: ethical AI
  topic: keywords
subject: Philippines
  topic: keywords
creator: Tetiana Schipper
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-09-22
publication date: 2025-09-22
starting page: 1
ending page: 11
doi: 10.47852/bonviewFSI52025696

Next-Gen Threat Hunting: A Comparative Study of ML Models in Android Ransomware Detection
type: item
description: 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, Da…
subject: Research Articles
  topic: section
subject: ransomware detection
  topic: keywords
subject: machine learning
  topic: keywords
subject: random forest
  topic: keywords
subject: K-nearest neighbors
  topic: keywords
subject: neural networks
  topic: keywords
subject: Android ransomware
  topic: keywords
subject: cybersecurity
  topic: keywords
creator: Linet Momposhi
creator: Alana Maurushat
creator: Rodrigo N. Calheiros
creator: Shawal Khan
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-07-11
publication date: 2025-07-11
starting page: A3
ending page: A3
doi: 10.47852/bonviewFSI52025685

On the Efficient and Optimal Predictive Values for Economic Growth with Covariate Predictors: Ordinary Least Square and Ridge Regression Approach
type: item
description: This study examined an efficient and optimal estimate and predicted value for economic growth using the explanatory variables that include internal and external debts, interest and exchange rate, and trade openness, which were correlated to cause a multicollinearity problem. The ordinary least square and ridge regression (RR) technique were adopted to analyze data gathered from the Central Bank of Nigeria (1986–2022). The diagnostic test conducted using the variance inflation factor revealed that the ordinary least square technique established a multicollinearity problem, which was addressed when the RR method was used with an appropriate ridgeconstant. The predictive performance metrics such as root mean square error, the mean absolute error, the mean absolute percentage error, and the bias proportion for the fitted RR were smaller when compared with the values obtained using the ordinary least square regression technique. Consequently, an RR was chosen as the most efficient and optimal technique to predict stable and reliable values for the economic growth. Therefore, this study was beneficial to the policymakers because it provided the needed understanding that captured the contributions of the identified economic growth drivers. The negative influence of the external debt on the economic growth was not in doubt. In addition, this study was significantly beneficial to the researchers by strengthening their understanding of the appropriate estimation technique to be adopted for efficient, optimal, stable, and reliable predictive values in the presence of multicollinearity. Received: 7 March 2025 | Revised: 22 July 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 the findings of this study are openly available in the Central Bank of Nigeria Statistical Bulletin via the link: https://www.cbn.gov.ng Author Contribution Statement Ebiwonjumi A…
subject: Research Articles
  topic: section
subject: debt
  topic: keywords
subject: monetary policy
  topic: keywords
subject: trade openness
  topic: keywords
subject: economic growth
  topic: keywords
subject: multicollinearity
  topic: keywords
subject: ridge regression
  topic: keywords
creator: Ebiwonjumi Ayooluwade
rights: Copyright (c) 2024 Ebiwonjumi Ayooluwade https://creativecommons.org/licenses/by/4.0/
date: 2025-11-25
publication date: 2025-11-25
starting page: 1
ending page: 8
doi: 10.47852/bonviewFSI52025624

Predictive Analytics for Personalized Debt Management: Leveraging Machine Learning to Provide Actionable Financial Advice
type: item
description: 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 differe…
subject: Research Articles
  topic: section
subject: personal finance
  topic: keywords
subject: debt management
  topic: keywords
subject: machine learning
  topic: keywords
subject: financial advice
  topic: keywords
subject: credit risk assessment
  topic: keywords
creator: Amir Ahmad Dar
creator: Trushal Hirani
creator: Vanshita Arora
creator: Sukriti Kakkar
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-07-16
publication date: 2025-07-16
starting page: A4
ending page: A4
doi: 10.47852/bonviewFSI52025591

Artificial Intelligence in Sustainable Industrial Transformation: A Comparative Study of Industry 4.0 and Industry 5.0
type: item
description: 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 Contri…
subject: Review
  topic: section
subject: artificial intelligence
  topic: keywords
subject: emerging technologies
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: sustainability
  topic: keywords
subject: Industry 5.0
  topic: keywords
creator: Muhammad Waqas
creator: Adila Naseem
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-05-15
publication date: 2025-05-15
starting page: A2
ending page: A2
doi: 10.47852/bonviewFSI52025321

Understanding Elon Musk's Acquisition of Twitter Through the Hubris Hypothesis: An Examination of Behavioral Finance, Governance, and Decision‑making
type: item
description: This study analyzes Elon Musk's acquisition of Twitter through the lens of the hubris hypothesis and theories of managerial overconfidence. Using a qualitative case study design, the research triangulates news reports, press releases, and academic literature to meticulously reconstruct the deal's chronology and evaluate observable behavioral indicators. These indicators, including prior performance, media acclaim, leadership concentration, premium pricing, and the waiver of due diligence, are systematically coded against established measures in the behavioral corporate finance literature. The evidence strongly supports behavior consistent with the hubris hypothesis, helping to explain the controversial financing choices and the heightened post-deal governance risks. The analysis integrates agency theory and behavioral finance to demonstrate how typical institutional constraints can sometimes temper managerial overconfidence. Crucially, it also uncovers boundary conditions where both market discipline and effective board oversight fail to curb excessive risk-taking, particularly in high-profile, nontraditional mergers. By explicitly operationalizing hubris indicators in a replicable way, this research contributes to the expanding behavioral corporate finance literature by establishing a link between overconfidence and digital platform acquisitions, where both substantial financial leverage and potent ideological motives are salient factors. Policy implications advocate for stronger board independence, mandatory due-diligence disclosures, stricter leverage discipline, and robust governance safeguards for social media firms that operate as quasi-public spheres. Received: 15 October 2025 | Revised: 10 December 2025 | Accepted: 8 January 2026 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 C…
subject: Research Articles
  topic: section
subject: hubris hypothesis
  topic: keywords
subject: managerial overconfidence
  topic: keywords
subject: corporate governance
  topic: keywords
subject: social media platforms
  topic: keywords
subject: Elon Musk
  topic: keywords
creator: Jia-Ying Lyu
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0/
date: 2026-01-22
publication date: 2026-01-22
starting page: 1
ending page: 11
doi: 10.47852/bonviewFSI62027903

An Enhanced Data Collection System for Social Enterprises: Securing Impact with Machine Learning and Cryptography
type: item
description: 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 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 – r…
subject: Research Articles
  topic: section
subject: automated data collection system
  topic: keywords
subject: cryptography
  topic: keywords
subject: machine learning
  topic: keywords
subject: sustainable development goals
  topic: keywords
subject: web scraping
  topic: keywords
creator: Fardin Muttaki
creator: Aqeel Sahi
creator: Shahab Abdulla
creator: Kaled Aljebur
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-05-15
publication date: 2025-05-15
starting page: A11
ending page: A11
doi: 10.47852/bonviewFSI52025209

Distributed Harmony: Intelligent Load Balancing in Heterogeneous Server Clusters Using Mobile Agents
type: item
description: 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 redu…
subject: Research Articles
  topic: section
subject: network-traffic
  topic: keywords
subject: mobile agent
  topic: keywords
subject: load balancing
  topic: keywords
subject: distributed harmony
  topic: keywords
subject: heterogeneity
  topic: keywords
subject: data center
  topic: keywords
creator: John Ugah
creator: Steven Ikporo
creator: Ifeanyi Achi
creator: Ugochukwu Onwudebelu
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-09-05
publication date: 2025-09-05
starting page: A10
ending page: A10
doi: 10.47852/bonviewFSI52025168

Cryptocurrency and Financial Sector: A Bibliometric Analysis
type: item
description: 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 furth…
subject: Review
  topic: section
subject: cryptocurrency
  topic: keywords
subject: blockchain currency
  topic: keywords
subject: financial sector
  topic: keywords
subject: bibliometric analysis
  topic: keywords
creator: Karima Sayari
creator: Mayssa Ben Belgacem
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-06-05
publication date: 2025-06-05
starting page: A8
ending page: A8
doi: 10.47852/bonviewFSI52025073