Journal of Economics & Management Research
Editor-in-Chief: Simon Grima Associate Professor Department of Insurance and Risk Management Faculty of Economics, Management and Accountancy University of Malta ORCID ID: https://orcid.org/0000-0003-1523-5120 Journal of Economics & Management Research (JESMR) is an international, open-access, peer-reviewed. The journal provides a platform for researchers, academics, and professionals to share innovative research and emerging insights in Economics and Management. JESMR welcomes Research Articles, Reviews, Analyses, Editorials, Opinions, Communications, Mini-Reviews, Short Communications, Book Reviews, Video Articles, and PowerPoint Presentations (PPTs), with a focus on the global dissemination of scholarly knowledge. Special Issue Title: "War Effect on Global Economy" Researchers working on inflation, supply chains, energy markets, financial volatility, trade disruption, and geopolitical risks are encouraged to submit. You can submit your article through online: https://srcpublishers.com/economics-management-research/user/register or through Email attchment: economics@onlinescientificresearch.info 1.5 Impact Factor 84.5% Citations 70% Acceptance
The Green Reallocation Insurance Hypothesis: Carbon Exposure, Renewable Capacity and Macroeconomic Resilience
Climate policy creates a structural-reallocation problem. Carbon-intensive economies must redirect capital, technology and labor toward lower-emission activities, yet the same policy shock may generate very different outcomes across countries and regions. This paper develops the Green Reallocation Insurance (GRI) framework, which treats renewable capacity and adjacent productive capabilities as an endogenous form of transition insurance. The framework predicts that carbon exposure raises adjustment pressure, while renewable capacity and related capabilities can reduce the social cost of reallocation. Using 2000–2024 World Bank country data for up to 260 countries and territories, the paper builds a lagged panel with carbon emissions per capita, renewable electricity output, unemployment, GDP per capita and GDP per person employed. Because the renewable-electricity series is complete only through 2021 in the acquired release, the principal lagged regressions cover 2001–2021. Two-way fixed-effects models with country-clustered standard errors find no statistically significant association of lagged carbon exposure, renewable electricity or their interaction with unemployment or labor productivity. The interaction is positive and statistically significant in the GDP model, with a coefficient of 0.000940 and a p-value of 0.007, although this association is not causal and may reflect selection into productive, energy-intensive economies. The results reject the idea that renewable capacity automatically operates as labor-market insurance. They instead support a more disciplined policy principle: transition insurance requires renewable capacity to be combined with worker mobility, training, fiscal capacity and related industrial capabilities. The paper contributes a mechanism-based framework, a reproducible global baseline and a clear agenda for regional and firm-level identification.
Copyright (c) 2026 Journal of Economics & Management Research
Modeling of Pedagogical Technology in Teaching Organizational and Technological Design of Low-Rise Residential Buildings
This article presents a scientific and methodological model of pedagogical technology for teaching organizational and technological design of low-rise residential buildings. The study responds to the need to connect construction-process planning, rational fuel-and-energy resource consumption, digital modelling and Competency-Based assessment in architectural education. The methodology is based on design-based pedagogical modelling, analysis of construction-sector indicators of Uzbekistan, published energy-efficiency evidence and the transformation of professional actions into learning tasks. The proposed model includes four interconnected blocks: construction-context diagnosis, digital organizational-technological modelling, energy and process optimization, and reflective competency assessment. Real secondary data show that completed construction works in Uzbekistan reached 91,621.5 billion UZS in January-May 2025, while buildings and structures accounted for 68.4% of the total construction volume. These indicators justify the use of real project cases in teaching low-rise residential design. The results demonstrate that students can develop project-based thinking when they compare alternative technological solutions through measurable schedule, resource, energy and quality indicators. The model may be applied in architecture, construction engineering and applied technical qualification programmes
Green Human Resource Management from 2020-2026: A Comprehensive Review and Framework for Sustainable Organizations
The article advocates for the amalgamation of the predominantly distinct bodies of literature in Environmental Management and Human Resource Management research. The research classifies the current literature according to the Ability–Motivation–Opportunity (AMO) theory, elucidating the function of Green Human Resource Management procedures popular in personnel management practices. The study employs a systematic literature review approach by integrating peer-reviewed publications published between 2020 and 2026 to analyse the evolution, practices, and theoretical foundations of Green Human Resource Management. The contributions of the paper include synthesising existing literature, mapping the terrain of the discipline, finding gaps in current research, and proposing possible productive future study guidelines. The study findings indicate that comprehension of the impact of Green Human Resource Management practices on employee motivation to engage in environmental activities is less advanced than the understanding regarding how organisations cultivate green capabilities and offer opportunities for employees to participate in Environmental Management initiatives. Organisations are not fully utilizing the scope of Green Human Resource Management techniques, which may limit their efficacy in enhancing Environmental Management. The analysis also provides practical implications for enterprises by emphasizing the strategic integration of GHRM approaches to increase environmental sustainability, green innovation, and organizational performance. The uniqueness of this research stems from the amalgamation of Environmental Management and Human Resource Management literature via the AMO theoretical framework, while indicating theoretical, empirical, methodological, and contextual research deficiencies and proposing an extensive research agenda for forthcoming GHRM investigations.
Fixing Uncertain, Strategic Failure, or Collapse During Unexpected Crises Via Leadership Cycle
In numerous definitions of leadership maybe the simplest model is The Circle of Analysis & Intervene & Balcony. Based on this simple model we can explain complex and very advanced DBA (Doctor of Business Administration) development of DBA carrier through very uncertain market. The Circle Analysis & Intervene & Balcony is infinity process, could be infinity, as circle (after Balcony again Analysis and again Intervention and again Balcony), but under control of specific individual. This paper aims to suggest to readers how to lead yourself and to achieve successful DBA carriers, potential guideline for DBA carrier development. However, to develop yourself and carrier we need to follow suggested definition and/or instruction, but for personal development, to affect carrier you need quite some of time, so this is process. Every individual can make this process shortly. Developing in a desired way—personally or professionally—requires a structured approach combining clear goal setting, habitual action, and intentional mindset shifts. Research suggests moving from goal-oriented thinking to building systems that ensure continuous improvement of personal achievements and sustainable personal growth.
Bridging the Workforce Divide: A Stakeholder-Based Model for Aligning Youth Talent Development, Economic Growth and Industry Demand
The United States faces a persistent workforce paradox: employers across critical industries report difficulty finding qualified workers, while many young people struggle to identify clear pathways from education into meaningful careers. This challenge is frequently characterized as a skills gap; however, the deeper issue is a coordination gap among the institutions responsible for workforce preparation. Educational institutions, employers, Economic Development Councils, Chambers of Commerce, workforce agencies, and community organizations often pursue workforce goals independently rather than through integrated regional strategies. This article argues that youth workforce development should be repositioned as a central economic development strategy. Drawing upon human capital theory, stakeholder theory, institutional economics, regional innovation systems and collective impact theory, this article proposes a Regional Stakeholder Workforce Alignment Framework (RSWAF) designed to intentionally bridge education and employment. The framework positions Economic Development Councils and Chambers of Commerce as regional conveners responsible for translating labor market intelligence into education and training strategies. Comparative analysis of Germany, Switzerland, Singapore, Canada, Australia, and the United Kingdom demonstrates that successful workforce systems share common characteristics: employer engagement, coordinated governance, work-based learning, and continuous adaptation. The article concludes that the future competitiveness of American communities depends on transforming workforce development from a collection of disconnected programs into a coordinated ecosystem where stakeholders share responsibility for developing human capital.
Minimizing Entrepreneurs Financial Risks through Managerial Equity Ownership Investment: Insights from Manufacturing Firms in Emerging Economies
Managerial ownership is a yardstick for entrepreneur ‘protection and business survival, motivating this study to investigate how managerial equity ownership investment minimizes entrepreneurs’ financial risks. The research uses a quantitative approach to obtain panel data from 576 listed manufacturing firms in twenty selected in emerging economies between 2001 and 2024. The research employed descriptive statistics and random-effects regression model for data analysis. Findings reveal that 5% and 10% managerial equity ownership investments significantly demonstrate lower entrepreneur financial risk in the emerging economies, implying that the executive members owning substantial amount of equity investment are directly responsible for the company's better performance, relieving the entrepreneurs from the burden of seeking for funding sources to ensure long-term financial stability of their businesses. This study concludes that managerial equity ownership investment reduces the entrepreneurs’ financial risks. The research recommend that the entrepreneurs should incorporate risk monitoring systems into executive evaluation frameworks to ensure that managerial equity ownership investment results in quantifiable financial stability outcomes.
Manufacturing Firms in Emerging Economies
Education and Knowledge in a Sustainable Environment, Through Digital Literacy for Transformation
Education (as a term derived from the Latin: education - upbringing, nurture), as a concept and process, is an organized activity that contributes to the development of positive human qualities by harmonizing social and individual relationships.
Digital Payments and Financial Psychology: Understanding Human Behavior in Cashless Economies
The rapid proliferation of digital payment systems has fundamentally transformed consumer financial behavior, creating unprecedented opportunities to examine the psychological mechanisms underlying economic decision-making in cashless economies. This mixed-methods study investigates the complex interplay between digital payment adoption and financial psychology, examining how technological affordances reshape spending patterns, impulse buying, trust formation, and financial well-being. Phase 1 employed a quantitative survey (n=487) utilizing validated instruments including the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), Trust Scale, and Financial Anxiety Scale to assess adoption determinants and behavioral outcomes. Phase 2 conducted semi-structured interviews (n=32) to explore lived experiences and psychological processes underlying digital payment usage. Quantitative findings reveal that perceived ease of use (β=0.42, p
Women’s Empowerment and Entrepreneurship in the UNICAS Credit and Savings Unions, Peru - 2024
This study aimed to determine the relationship between empowerment and female entrepreneurship within the Savings and Credit Unions (UNICAS) in Cajamarca in 2024. The research was basic, quantitative, non-experimental, correlational, and cross-sectional in nature. The population consisted of 536 UNICAS in the Cajamarca region; however, a sample of 15 UNICAS-comprising exclusively women and totalling 195 individuals-was selected. The study yielded a Spearman’s Rho coefficient of 0.886, indicating a strong positive correlation. This suggests that higher levels of empowerment increase the likelihood of women starting a business. Therefore, it is essential to continue promoting economic empowerment programs and facilitating access to microfinance in order to foster economic and social development in the region.
Automated Multi-Class Classification of Anterior Segment Eye Diseases Using Deep Learning
Accurate and timely detection of anterior segment eye diseases is critical for preventing permanent loss of sight. In this paper, we present a framework for applying deep learning to identify five different types of external eye-related diseases at once, namely: cataracts; corneal ulcers; pterygia; conjunctivitis; and eyelid abnormalities. Labeled clinical images of the eyeball were collected from both publicly available and clinician-curated sources. We tested three different deep Learning-Based Convolutional Neural Network (CNN) models: (1) a custom designed 5-layer CNN, (2) VGG16 implemented with transfer learning, and (3) EfficientNet-B0. All three models were tested under the same preprocessing algorithms, stratified data splitting, and augmentation techniques. The multi-class performance of each model was measured using the four common performance metrics (i.e., accuracy, precision, recall, and F1-score). The experimental results show that the best performing model was the EfficientNet-B0, which achieved a maximum accuracy of 90.5% with also demonstrating superior computational performance compared to the other two CNNs. It is suggested that the proposed framework could be used for automated diagnostic screening and/or tele-ophthalmologic applications.
Does Prudential Regulation Improve Solvency? A Structural Break Analysis of Pension Fund Dynamics
This paper examines whether the 2019 prudential regulatory event improved pension-fund solvency dynamics. The analysis relies exclusively on previously estimated results reported in the thesis archive and combines ARIMA intervention models, polynomial time-trend regressions, Chow structural-break tests, and time-series decompositions. ARIMA results show no statistically significant intervention effect for the solvency-related index in either group (Control: xreg = 0.0024, p = 0.7423; Treatment: xreg = 0.0190, p = 0.3090). Time-trend regressions likewise show no differential post-event effect, although the treatment coefficient is negative and significant (-0.0491; p = 0.002). Chow tests, however, detect significant structural breaks in 2019 for both groups, with stronger evidence in treatment (Control: F = 1.710325, p = 0.0114; Treatment: F = 1.985284, p = 0.0015). Decomposition results show mild deterioration in the control trajectory and continued upward trend in treatment. The evidence therefore points to structural adjustment and heterogeneous resilience rather than a clean causal solvency gain attributable to the intervention regressor alone.
The Cost of Prudential Regulation: Evidence from Operational Expenditure per Capita in Pension Funds
This paper evaluates whether the 2019 prudential regulatory event is associated with persistent changes in pension-fund cost dynamics. The analysis uses only estimates already reported in the thesis results archive and combines ARIMA intervention models, polynomial time-trend regressions, Chow structural-break tests, and time-series decompositions. For dpc, the ARIMA intervention term is positive and significant in the control group (xreg = 370.0129; p = 0.0041) but negative and non-significant in treatment (xreg = -156.3812; p = 0.6023). Chow tests reveal strong breaks in both groups (Control: F = 3.218528, p < 0.0001; Treatment: F = 2.641218, p < 0.0001). Complementary expenditure regressions show a large baseline treatment coefficient (64.8547; p < 0.001), but no significant event or event-treatment effect. Decomposition results indicate that post-2019 change is expressed primarily through greater volatility around trend rather than a clean level shift. Taken together, the evidence supports the existence of meaningful post-2019 cost reconfiguration, while suggesting caution in attributing a differential treated-group increase to the intervention alone.
Convergence of AI, Blockchain and Quantum Computing in Financial Risk Management and Regulatory Compliance
Financial institutions now face three pressures that do not pull in the same direction. Risk detection has to work at microsecond timescales. Compliance rules demand verifiable provenance for every decision that gets made. And the optimisation problems underlying modern portfolio construction and derivative pricing are growing faster than classical computing can keep up with. Most of the existing scholarship treats artificial intelligence, blockchain, and quantum computing as three separate technological trajectories, each studied for the incremental improvement it promises in the problem it addresses. This study argues that the more consequential transformation lies in their convergence, not in their individual adoption. The literature has not yet articulated an integrated architecture that assigns complementary roles to these three technologies within a single risk-and-compliance stack, and that is the gap the paper addresses. Drawing on recent work in financial economics, cryptography, and quantum information science, the paper proposes the ABQ Financial Risk Architecture, which organises three layers: an AI-driven Detection Layer, a blockchain-anchored Trust and Audit Layer, and a quantum-enabled Optimisation Layer. A testable proposition follows. Institutions that integrate all three layers should achieve materially better risk-adjusted compliance outcomes than institutions deploying any subset on its own, with the gap widening as the underlying problems grow more complex. The paper then examines economic implications for developed markets, regulators, and emerging economies, with particular attention to India's National Quantum Mission and the Reserve Bank of India's evolving regulatory posture. Contributions include the architectural conceptualisation itself, three analytical figures depicting layer structure and information flows, a comparative mapping of technology roles, and a research agenda for validating convergence effects empirically. The analysis suggests that real leapfrog opportunities exist for jurisdictions without deeply entrenched legacy systems, though three constraints will bind near-term deployment: quantum hardware immaturity, regulatory asymmetry across jurisdictions, and unsolved interoperability across the three technology domains.
Development of Wastewater Treatment System for Coconut-Based Food Production
Coconut-based food production, particularly coconut jelly processing, is a key industry among SMEs in Samut Songkhram province. However, a manufacturing process generates a large volume of wastewater per day that released into the surface water causing water pollution and foul odors. This study aims to develop and implement an appropriate wastewater treatment system for coconut jelly and white coconut meat processing industries, with the goal of establishing practical design specifications for SME applications. The design of the developed wastewater treatment system depends on the volume of wastewater generated from the production process. The treatment system consists of an anaerobic pond (serving as a combined equalization and grease trap pond), an aeration pond, and a sedimentation pond. In 2024, the pilot plant wastewater treatment system for coconut jelly production was installed at SME entrepreneurs in Samut Songkhram province. The pilot plant wastewater system has been designed for the influent 5 m3/day. The designed consist of (1) 25 m3 anaerobic pond with retention time 5 day, (2) the 6 m3 aeration pond with oxygen transfer rate 5.3 Kg.O2 /day, retention time 1.2 day and (3) 1.8 m3 sedimentation pond with retention time of 0.4 day. The results indicate that, when compared with the Notification of the ministry of natural resources and environment on the establishment of wastewater discharge standards from small enterprises producing goods or providing specific services, B.E. 2564 (Group 2). The results of the implementation of this system demonstrate that the effluent pH of 6.1 – 6.4, within the standard range (5.5 – 9.0). BOD ranged from 5 – 58 mg/L, meeting the standard (≤60 mg/L) with 96.77 – 98.27% removal. TSS ranged 6 – 20 mg/L, within the standard (≤ 50 mg/L), with 87.26 – 97.62% removal. Oil and grease were undetectable, achieving 100% removal (≤ 20 mg/L). The developed wastewater treatment system effectively reduced organic pollutants and improved effluent quality, providing a sustainable and practical model for wastewater management in coconut-processing SMEs.
This research explores the relationship between personal values and the tendency to take cyber-risks. We first develop and add six cyber-risk items to the well-known Domain-Specific Risk-Taking (DOSPERT) scale. Importantly, we show that like the other five domains examined by the scale (Health, Ethical, Financial, Recreational, and Social), these cyber items form a separate cluster. We then examine the association between values and cyber risk-taking behavior. We find that “conservation (conservatism)” values are negatively correlated with the tendency to take cyber risks. Thus, individuals prioritize conservation are less likely to take cyber risks. We also find that “openness to change” values are positively associated with the tendency to take cyber-risks. Individuals who value openness to changes are prone to take cyber risk. The findings suggest possible “values based” interventions in order to increase cyber-security.
Banking Innovations and Agricultural Sector Performance in Nigeria
The study investigated banking innovations and agricultural sector performance in Nigeria. An Ex-Post Facto research design was adopted, utilizing monthly time-series data from 1980 to 2024, sourced from the Central Bank of Nigeria and the Nigerian Bureau of Statistics. The data were analyzed using descriptive statistics and the Autoregressive Distributed Lag (ARDL) model to capture both short-run and long-run dynamics. The study's diagnostic tests confirmed the stability and reliability of the models. The study's ARDL long-run analysis revealed that ATM transactions had a significant positive impact on agricultural GDP with a coefficient of 0.004691 (p = 0.0000), while EFT transactions had a significant negative effect with a coefficient of -0.015411 (p = 0.0008). Mobile banking and POS transactions had no significant long-run impact. In the short run, negative lag effects of ATM and mobile banking were observed, while EFT showed positive short-run contributions. The error correction term indicated that 39.4% of disequilibrium was corrected each period. The model also passed diagnostic tests for serial correlation, heteroskedasticity, and model specification, confirming its reliability. The study concluded that while ATM transactions support agricultural growth, mobile banking, POS, and EFT have mixed or negative impacts on agricultural productivity. It recommended that policies should focus on improving the integration of digital banking tools with the agricultural sector and address structural constraints such as low digital literacy and weak rural infrastructure.
Autoregressive Distributed Lag (ARDL) Model
U.S. Semiconductor Export Structure under Innovation and Trade Policy
Semiconductors are a foundational input for modern economic activity, and their international trade is shaped by both market forces and government policy. This study examines how U.S. innovation and trade policies are associated with changes in the structure of semiconductor exports, focusing on shifts in export allocation, destination concentration, and specialization rather than export volumes alone. Using bilateral UN Comtrade data classified by harmonized system (HS) codes from 2015 to 2024, the analysis applies measures of export concentration and comparative export strength to assess changes in market dependence and positioning. The results indicate that major policy changes coincide with export reallocation across destination markets, while U.S. exports remain concentrated in technologically advanced segments. Overall, the findings link policy environments to observable changes in export structure in a technology-intensive industry.
African Continent Free Trade Area Development Strategy Analysis
The establishment of the African Continental Free Trade Area (AfCFTA) will promote the agglomeration of industries in the African region, the extension of the industrial chain and the growing economies of scale, and promote the participation of African countries in the global supply chain. From the perspective of how Africa should deal with the difficulties in the development process of the free trade area, this paper discusses how the development strategy and strategy of the free trade area on the African continent can help its stable development from the aspects of the construction of the free trade area on the African continent should learn from China's experience, promote the development of African port economy, help the development of agriculture and industrialization in Africa, increase and improve the investment in infrastructure, and connect the development of urbanization in Africa.
Copyright (c) 2024 Journal of Economics & Management Research
Building a Tariff-Proof Economy: Ecosystem Rules for A Sustainable Economic Future
Humanity suffered a wide range of health concerns until we gained understanding of the inner-workings of the human body, and the nature of pathogens that threaten it. We lack an equivalent understanding of our economy, leaving many nations vulnerable to supply chain disruptions, volatile energy markets, declines in soil fertility, shifting global trade policy, and a litany of other social and environmental challenges. These challenges will likely remain unresolved until we address the economic flaws responsible for their creation. Drawing from thirty years of research and practice in business, economics, and ecology, this seminar outlines three foundational components governing sustainable, tariff-proof, economies—diversity, energy, and trade. The identification of these foundational (governing) components, and the dynamic relationships between them, is the central theme of this talk. This talk presents a fresh perspective on the structure of a sustainable economy, as well as traditional and nontraditional measures of economic productivity, the role of economic succession in stimulating productivity, an ecological explanation of the wealth gap, and the natural geography of a sustainable economy. Rather than perceive tariffs and volatile energy markets as threats, how might nations navigate around such forces while increasing GDP and resilience. The talk closes with a path for economic restoration, with case studies from the U.S., Ecuador, Sri Lanka, Italy, and elsewhere. Policy implications are provided for national and local levels. To guide the public’s attention and resources toward building a sustainable economy, and to catalyze change, the structure of a sustainable economy must first be understood. And the paths toward economic restoration must be clearly visible.
Copyright (c) 2025 Journal of Economics & Management Research
Advancing Scalable and Secure AI Systems: A Decade of Innovation in AI Infrastructure, MLOps, and Data EngineeringSafeguarding Financial Data
An AI and MLOps innovator with a decade of experience in designing and deploying scalable, secure, and intelligent AI-driven systems. This work focuses on pioneering advancements in AI infrastructure, MLOps automation, and cloud-scale data platforms that empower organizations to operationalize machine learning with efficiency and confidence. With a strong foundation in data engineering, the approach emphasizes building end-to-end pipelines that ensure data integrity, model reproducibility, and seamless deployment. Key contributions include the integration of robust CI/CD frameworks for ML, scalable data ingestion and processing architectures, and governance mechanisms to support compliance and security. This journey reflects a commitment to driving the next generation of intelligent systems by aligning technological innovation with real-world business outcomes.
Comprehensive Guide to Insurance and AAA Membership Benefits: What You Need to Know Safeguarding Financial Data
In today’s fast-paced world, protecting what matters most your family, assets, and peace of mind is more important than ever. From auto and home insurance to specialized coverage like RV, boat, and event insurance, understanding your options is key to making informed decisions. This guide also explores the valuable perks of AAA membership, including roadside assistance, travel services, and exclusive discounts. Whether you’re securing your future with life insurance or planning for the unexpected with long-term care and annuities, this comprehensive overview breaks down the essentials to help you stay protected and prepared.
Transforming the Automotive Industry: From Manufacturing and Mobility to AI, Cloud, and Sustainable SolutionsSafeguarding Financial Data
The automotive industry is undergoing a profound transformation driven by rapid advancements in technology, evolving consumer expectations, and increasing pressure for environmental sustainability. This paper explores the comprehensive evolution of the sector, encompassing vehicle manufacturing and distribution, Research & Development (R&D), sales and marketing, and financial services. It delves into the integration of connected services and next-generation mobility solutions, such as ride-sharing and autonomous driving. A key focus is placed on the role of emerging technologies Artificial Intelligence (AI), Machine Learning (ML), data engineering, and cloud computing platforms like AWS and Azure in revolutionizing automotive operations. These technologies are enabling predictive maintenance through “engine heartbeat” monitoring, enhancing customer engagement, optimizing supply chains, and accelerating innovation cycles. Additionally, the paper highlights environmental sustainability initiatives, examining how automakers are leveraging digital tools and green technologies to reduce emissions, improve energy efficiency, and meet global regulatory standards. By analyzing the convergence of traditional automotive practices with cutting-edge digital ecosystems, this paper provides a strategic overview of how the industry is reshaping itself for a smarter, more connected, and sustainable future.
AI-Enhanced Chatbots for Real-Time Symptom Analysis and Triage in Telehealth Services Safeguarding Financial Data
AI-enhanced chatbots are transforming telehealth services by enabling real-time symptom analysis and automated patient triage. Traditional healthcare systems often face challenges such as long wait times, resource constraints, and accessibility barriers. AI-driven chatbots leverage Natural Language Processing (NLP) and Machine Learning (ML) to analyze patient symptoms, assess risk levels, and provide preliminary medical guidance. These intelligent systems enhance telehealth by streamlining patient intake, prioritizing urgent cases, and reducing the burden on healthcare providers. Additionally, chatbot-driven triage improves patient engagement, ensures timely interventions, and facilitates remote healthcare access. However, challenges such as diagnostic accuracy, data privacy, and regulatory compliance must be addressed to ensure ethical and effective deployment. This study explores the capabilities, benefits, and limitations of AI-powered chatbots in telehealth, highlighting their potential to improve efficiency, accessibility, and patient outcomes in modern healthcare ecosystems.
The Role of Business Interruption Insurance in Economic Resilience: A Post-Pandemic Analysis of Policyholder Compensation and Legal ChallengesSafeguarding Financial Data
Business Interruption (BI) insurance plays a critical role in economic resilience by providing financial protection to businesses facing operational disruptions. This study examines the effectiveness of BI insurance in compensating policyholders during crises, with a focus on post-pandemic recovery. It analyses key challenges, including ambiguities in policy coverage, legal disputes over pandemic-related claims, and the role of government interventions in supporting affected businesses. The research explores how insurers and policymakers can enhance BI insurance frameworks to improve risk assessment, policy clarity, and claims processing. Additionally, emerging solutions such as parametric insurance models and AI-driven risk analytics are discussed to address future economic uncertainties. By evaluating the lessons learned from the COVID-19 pandemic, this study provides insights into strengthening BI insurance as a tool for fostering economic stability and business continuity in the face of global disruptions.
AI-Driven Treasury Management: Reinforcement Learning Models for Liquidity Optimization and Fraud Prevention in Large-Scale Financial Institutions
AI-driven treasury management is transforming financial institutions by leveraging reinforcement learning models to optimize liquidity and enhance fraud prevention. This study explores how AI-powered algorithms analyze real-time financial data, predict cash flow trends, and dynamically allocate liquidity to maximize capital efficiency. Reinforcement learning enables adaptive decision-making, allowing treasury systems to respond proactively to market fluctuations, interest rate changes, and regulatory requirements. Additionally, AI-driven fraud detection mechanisms use anomaly detection and predictive analytics to identify suspicious transactions, mitigate risks, and strengthen financial security. The research highlights key challenges, including data privacy, model interpretability, and integration with legacy financial systems, while proposing strategies to enhance AI adoption in treasury management. By combining reinforcement learning with big data and cloud computing, large-scale financial institutions can achieve greater operational resilience, improved risk management, and a more agile approach to liquidity optimization and fraud prevention.
The Future of Digital Financial Security: Integrating AI, Cloud, and Big Data for Fraud Prevention and RealTime Transaction Monitoring in Payment Systems Safeguarding Financial Data
The rapid evolution of digital payment systems has heightened the need for advanced security measures to combat financial fraud and ensure real-time transaction monitoring. This study explores the integration of Artificial Intelligence (AI), cloud computing, and big data analytics in enhancing digital financial security. AI-driven fraud detection models leverage machine learning algorithms to identify suspicious patterns, mitigate risks, and automate threat response mechanisms. Cloud computing provides scalable infrastructure for secure data processing, enabling seamless real-time transaction monitoring and fraud prevention. Big data analytics enhances predictive modelling, allowing financial institutions to detect emerging fraud trends and adapt security protocols dynamically. Additionally, this research examines challenges such as data privacy, regulatory compliance, and adversarial AI risks while proposing strategies for strengthening financial cybersecurity. By integrating AI, cloud, and big data, the future of digital financial security promises greater transparency, resilience, and consumer protection in an increasingly digital economy.
AI-Driven Identity Protection: The Intersection of Gen AI, Credit Monitoring, and Fraud Alerts in Safeguarding Financial Data
As financial fraud and identity theft threats continue to rise, AI-driven solutions are becoming essential for safeguarding financial data. This study explores the intersection of Generative AI (Gen AI), credit monitoring, and fraud alerts in enhancing identity protection. AI-powered models analyze vast datasets in real time to detect anomalies, predict fraudulent activities, and automate rapid response mechanisms. By leveraging Gen AI, financial institutions can improve authentication processes, personalize fraud prevention strategies, and enhance anomaly detection through adaptive learning. Additionally, AI-driven credit monitoring systems offer proactive alerts, enabling consumers to take preventive action against potential breaches. This research also examines challenges such as data privacy, ethical AI implementation, and the risk of adversarial attacks. By integrating AI with financial security frameworks, organizations can strengthen consumer trust, mitigate financial risks, and establish a more resilient, data-driven approach to identity protection.
Analyzing Behavioral Trends in Credit Card Fraud Patterns: Leveraging Federated Learning and Privacy-Preserving Artificial Intelligence Frameworks
As credit card fraud becomes increasingly sophisticated, financial institutions must adopt advanced security measures to detect and prevent fraudulent activities while ensuring user privacy. This study explores the use of Federated Learning (FL) and privacy-preserving Artificial Intelligence (AI) frameworks to analyse behavioural trends in credit card fraud patterns. By leveraging FL, financial institutions can train AI models collaboratively across decentralized datasets without exposing sensitive customer information. Machine learning algorithms detect anomalies, predict fraud attempts, and adapt to emerging attack strategies in real time. Additionally, privacy-preserving AI techniques such as differential privacy and homomorphic encryption enhance data security while maintaining model accuracy. This research also examines challenges related to scalability, computational efficiency, and regulatory compliance in implementing FL-based fraud detection systems. By integrating AI-driven fraud analysis with privacy-preserving technologies, financial institutions can improve fraud prevention strategies, strengthen cybersecurity, and enhance consumer trust in digital transactions.
Lakshminarayana Reddy Kothapalli Sondinti
Transformative Applications of Generative AI in Socially Responsible Financial Models: A Machine Learning Perspective
Generative AI is redefining socially responsible financial models by enhancing decision-making, risk assessment, and financial inclusion. This study explores the transformative applications of Generative AI in developing ethical, transparent, and sustainable financial systems from a machine learning perspective. Traditional financial models often struggle with biases, inefficiencies, and limited adaptability to dynamic economic conditions. By leveraging Generative AI, institutions can create predictive models for impact investing, fraud detection, credit risk evaluation, and financial forecasting, ensuring fairer and more inclusive financial practices. Additionally, AI-driven simulations enable scenario analysis for sustainable investments, helping organizations align with Environmental, Social and Governance (ESG) goals. However, challenges such as algorithmic bias, data privacy, and regulatory compliance remain critical considerations. This paper examines the potential and limitations of Generative AI in fostering ethical finance, emphasizing the need for responsible AI deployment to enhance transparency, efficiency, and accessibility in financial systems.