Journal of Economics, Finance and Accounting Studies
Founded in 2019, the Journal of Economics, Finance and Accounting Studies (JEFAS) is a double-blind peer-reviewed, open-access monthly journal published by Al-Kindi Center for Research and Development. The journal is led by expert international editorial board members who take an objective approach to peer review, ensuring each research paper is reviewed and evaluated on its own scholarly merits and research integrity. Publishing your work in the Journal of Economics, Finance and Accounting Studies means you will receive prompt and reliable publishing and a global platform for your research to reach its full potential. The journal is published in online version only.
Al-Kindi Center for Research and Development
Reconstruction as a Lever for Sustainable Development in Gaza: Between Reality and Prospects for Recovery After the 7 October 2023 War
The 2023 conflict in the Gaza Strip resulted in unparalleled human, material, and structural damage, valued at approximately USD 18.5 billion, equivalent to almost a full year of Palestinian GDP. Beyond the extent of physical devastation, the hostilities revealed the profound vulnerability of Gaza’s economy, public systems, and communal structure, and severely aggravated hardship, joblessness, and involuntary movement. This analysis explores how post-2023 rebuilding can surpass a brief humanitarian reaction to become a strategic instrument for enduring progress in Gaza. Employing a descriptive–analytical and specific-example method, the research utilizes recent evaluations by the World Bank, the UN, and ESCWA (2024–2025) to chart the extent and sectoral nature of the harm, pinpoint major organizational, fiscal, and political barriers, and dissect the connections between rebuilding and the economic, societal, and ecological foundations of permanence. The paper suggests a conceptual model that regards reconstruction as an evolving procedure rooted in “build back superior” tenets, strong local administration, and alignment with the 2030 Agenda. It then details a policy plan for 2025–2035 connecting accommodation, essential structures, earning potential, welfare provisions, rubble handling, and clean power into a unified national blueprint. The paper finishes by asserting that progress in Gaza will only serve as a true impetus for lasting development if it revitalizes populations, the marketplace, and organizations concurrently, under a domestically controlled structure that harmonizes emergency assistance with protracted structural changes.
Reconstruction; Gaza Strip; 2023 conflict; Enduring progress; Post-conflict recuperation; Relief effort; Durability; Leadership; Rubble handling; Clean economy; Societal fairness.
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How Smart City Initiatives Economically Affect Small and Medium-Sized Enterprises (SMEs): Innovation Capacity and Competitiveness – A Comparative Study of Japan and the UK
This paper will look at the impact of smart city projects on the competitiveness and economic performance of small to medium enterprise (SMEs) in the United Kingdom and Japan. The selection of a qualitative comparative research design to be used was based on the systematic analysis of the academic literature, national policy papers and institutional reports. The results indicate that the Japanese long-term, highly centralized type of governance helps in facilitating systemic coordination, structural integration, whereas the UK model that is decentralized, market-driven encourages flexibility, fast experimentation, and data-driven innovation. Nevertheless, having differences, both models unveil that smart city programs can only be translated into the competitiveness of SMEs in case of coherent policies, digital literacy, and collaborative ecosystems. The research has implications on policymakers who want to reconcile technology change with an inclusive economic development in the urban settings.
Smart cities, Small and medium-sized enterprises (SMEs), Innovation and competitiveness, Society 5.0 (Japan), Digital transformation, Open data and innovative governance, Comparative economic analysis (Japan–UK)
Tourism as a Catalyst for Economic Diversification in Saudi Arabia: Vision 2030 and Beyond
Saudi Arabia’s Vision 2030 reform program positions tourism as a cornerstone for economic diversification, aiming to reduce reliance on oil revenues and foster a knowledge-based economy. This paper critically examines the evolution of Saudi Arabia’s tourism sector under Vision 2030, analyzing its economic, social, and cultural impacts while identifying key challenges and opportunities. Drawing on secondary data from the Saudi Ministry of Tourism, OECD, UNWTO, and World Bank, the study reviews flagship projects such as NEOM, the Red Sea Project, and Diriyah Gate. It also explores the role of technology, sustainability, and global partnerships in shaping the sector’s future. The paper concludes with a comprehensive policy framework and actionable recommendations to ensure sustainable, inclusive, and competitive tourism growth by 2030.
Tourism, Economic Diversification, Vision 2030, Saudi Arabia, NEOM, Red Sea Project, Sustainability, Cultural Heritage, Employment, Smart Tourism
Does Corporate Governance Improve Bank Performance? A study of Rural Banks in West Java, Indonesia
The objective of this research is to examine the correlation between Sound Corporate Governance and Financial Performance within Rural Banksing Institutions. The design/methodology uses balanced panel data multiple regression analysis on EViews 13 software. The study sample comprises Rural Banks located in West Java Province. The study's findings demonstrate that the inclusion of female directors significantly enhances ROA. The directors adversely impact both ROA and ROE. Furthermore, the board of commissioners and independent commissioners have no impact on ROA but negatively impact ROE. This study is unique in that it focuses on Rural Banks, which have received less attention in earlier research.
Keywords: Good Corporate Governance, Financial Performance, Rural Banks.
Identifying Hidden Fraud Indicators through Longitudinal Analysis of U.S. Financial Misconduct
Systemic risk of fraud in U.S. finance and health care has continued to have an adverse impact on the economy, stretch government programs and reduce public trust in institutions. This study provides a 10 year cross-sectoral (finance and health care) study (years 2010-2020) utilizing databases from the Consumer Financial Protection Bureau (CFPB), Securities Exchange Commission (SEC), Department of Justice (DOJ), Center for Medicare Services (CMS) and Health and Human Services Office of Inspector General (HHS-OIG) to identify potential early warning signs which may occur prior to the detection of fraud by regulatory bodies. Using Longitudinal Trend Analysis, Correlation Mapping, K-means Clustering, and Regression Modeling; this study demonstrates a sustained increase in Fraud Occurrence Rates (FOR) for each sector, and a high positive correlation between the two sectors (Sectoral Correlation Index SCI= .72). The three early warning signs identified within this study: Complaint Escalation Rate (CER), Detection Lag (DL), and Fraud Method Diversity (FMD); were found to possess statistically significant predictive capability for future confirmed fraud events. The trends also demonstrated that spikes in public complaints and healthcare recoveries could potentially serve as an actionable signal to regulators of the need for increased oversight or investigation. Based upon these findings, the study proposes an Early Warning Fraud Detection Framework (EW-FDF) and advocates for the development of a Unified Fraud Intelligence Network (UFIN) to enable predictive analytic capabilities across agency boundaries.
Hidden Fraud Indicators, Fraud Analysis Longitudinal, Financial Misconduct, Predictive Fraud Detection and U.S. Regulatory Oversight
Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
ESG and Commercial Credit Financing: Based on Government Subsidies and Polluted Areas
This paper investigates whether and how firms’ Environmental, Social and Governance (ESG) performance affects commercial credit financing (SCF) in the context of Chinese listed companies. Using panel data on A-share firms and ESG ratings, this study constructs a comprehensive measure of SCF based on supplier-provided trade credit and estimate fixed-effects models. The results show that ESG performance is positively and significantly associated with SCF. Further analysis reveals pronounced heterogeneity: ESG enhances SCF only in non-heavily polluting industries and among firms receiving government subsidies, while the effect is insignificant for heavily polluting and non-subsidised firms. These findings suggest that ESG operates as a credible financing signal when it reflects voluntary engagement. The results imply that firms can treat ESG as part of their financing strategy and use it to improve access to supplier credit. Suppliers can also incorporate ESG indicators into credit-risk assessment. Policymakers may strengthen ESG disclosure rules and subsidy design to support more efficient, sustainability-oriented supply-chain finance.
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Current Snapshot of Business Climate and Sustainable Economy. Does AI Play a Crucial Role?
This study aims to provide an analysis of the business climate on a sustainable economy by considering the effects of AI adoption in the era of global uncertainty (2024). The study obtained data from 50 countries, which were selected based on the specified criteria. The main analysis of this study applied moderation regression analysis, while the additional analysis applied multiple regression analysis. This research uses the EViews 13 application to analyze the data. The main analysis indicates that the business climate has a positive effect on the sustainable economy, and AI does not moderate the influence of the business climate on the sustainable economy. Additional analysis shows consistency that all indicators in the business climate have a positive effect on the sustainable economy. The findings of this study are contributing to the motivation and evaluation of each country to enhance a sustainable economy through the implementation of a stable and supportive business climate, as well as the promotion of the adoption of AI as a catalyst for sustainability in various sectors, specifically business and the economy.
Sustainable Economy, Business Climate, Artificial Intelligence
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The Economics of Fast Food: Impact on Society, Employment, and Consumption Patterns: A Comprehensive Study Integrating Global and Indian Perspectives
This research article examines the economics of the fast-food industry, focusing on its societal impact, employment dynamics, and evolving consumption patterns. Integrating global and Indian perspectives, the study traces the historical development of fast food, analyses employment trends including youth and gig economy participation, and investigates consumer behavior and pricing strategies. It further scrutinizes public health and environmental challenges, evaluates policy responses, and considers the industry's future in light of technological and regulatory trends. The study employs a data-driven approach, incorporating infographics, and tables to illustrate key trends. The findings offer valuable insights for students, researchers, and policymakers into the complexities and implications of the fast-food sector worldwide and in India.
Fast food industry, economics, consumption patterns, public health, consumer behavior, pricing strategies, gig economy, environmental challenges, sustainability initiatives, market adaptation.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Internal, External, and Forensic Audits as Determinants of Fraud Prevention in Ghana’s Metropolitan, Municipal, and District Assemblies
Fraud and corruption are major global challenges that have a profound effect on individuals, countries, and economies. They incur substantial costs and hinder societal advancement. In Ghana, fraud and corruption present serious obstacles for the Metropolitan, Municipal, and District Assemblies (MMDAs), compromising governance and development efforts. This research assesses how well internal, external, and forensic audits function in curbing fraudulent financial activities within Ghana's MMDAs. A quantitative cross-sectional survey design was utilised to gather data from 343 finance directors, accountants, and internal auditors across 261 MMDAs. Descriptive statistics and multiple regression analysis were used to evaluate the data. The results indicate that internal audits are viewed as effective for identifying early indicators of financial mismanagement (mean = 4.87) and for preventing fraud (mean = 4.60), although issues like lack of independence (mean = 4.23) and insufficient resources (mean = 4.23) exist. External audits are conducted regularly (mean = 5.44) but face challenges with the weak enforcement of their recommendations (mean = 2.56). Forensic audits, despite their limited usage (mean = 1.68 regarding the hiring of forensic auditors), show the most substantial association with fraud reduction (β = 0.228, p < 0.001). Together, audit activities account for 12.2% (R² = 0.122) of the variance in fraudulent practices, revealing their significant yet constrained effectiveness. The study concludes that although audits are vital for preventing fraud, their success is undermined by systemic shortcomings. It suggests enhancing forensic audit capabilities, ensuring the enforcement of external audit recommendations, and preserving the independence of internal audits to improve fraud prevention in Ghana's local governments. These findings add value to both academic discussions and practical policy reforms in the management of public sector finances.
Predictive Modeling of U.S. Stock Market Trends Using Hybrid Deep Learning and Economic Indicators to Strengthen National Financial Resilience
The stock markets are too complex to predict, despite their non-linearity, volatility, and multifactorial nature. Current econometric and deep learning models often do not take into account the macroeconomic context, which restricts predictive accuracy at times of financial volatility. This paper attempts to fill this gap by proposing a Hybrid Deep Learning Model (HDLM), a machine that combines Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and an attention mechanism that can jointly include both the dynamic behavior of the market and the economic links in the trends of the U.S. market. The analysis is based on a strictly tested data set of 20102023, which was retrieved from the Federal Reserve Economic Data (FRED), Yahoo Finance, and the Bureau of Economic Analysis. These include all significant equity indexes such as the S&P 500, NASDAQ Composite, and Dow Jones Industrial Average, prime macroeconomic indicators such as the GDP growth, inflation rates, interest rates, and unemployment levels. Correlation analysis, multiple regression, Granger causality tests, Johansen cointegration procedures, and volatility modeling using ARCH GARCH models are the statistical methods used. The findings show that GDP growth has a statistically significant positive impact on market returns ( = 0.58, p = 0.001), though inflation ( = -0.21, p = 0.021), interest rates ( = -0.34, p = 0.002), and unemployment ( = -0.42, p = 0.001) have significant negative predictive power (R 2 = 0.68). The HDLM achieves a better predictive performance with RMSE = 1.89, MAPE = 4.9% and DA = 83.6% > 23% better than the baseline LSTM and CNN- LSTM settings. The model minimizes prediction error by 36.1% in cases of strong market shocks, which occur under simulated stress conditions, confirming an increased financial strength. Taken together, the results of the studies support the idea that the incorporation of macroeconomic intelligence into hybrid neural systems significantly enhances forecast reliability and stability of the system. The current research, therefore, adds a strong, interpretive, and policy-related framework to the research fields of predictive finance and resilience engineering systems to predict market upheavals.
Deep learning, financial resilience, macroeconomic indicators, stock market forecasting, U.S. economy
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc/4.0
The Internal Audit Roles and Financial Discipline of the Technical Universities in Ghana: The Moderating Role of Effective Leadership
This study examined the relationship between internal audit roles and financial discipline, with effective leadership as a moderating factor, in Ghana’s technical universities. Using an explanatory research design, data were collected from 127 key officeholders across the Technical Universities in Ghana. The findings show that internal auditors play a pivotal role in promoting financial discipline through strategic advisory services, operational efficiency, risk management, reporting, and compliance enforcement. More importantly, the results demonstrate that effective leadership practices centred on integrity, transparency, accountability, strategic vision, and sound financial decisions significantly strengthen the positive effects of internal auditing on financial discipline. The financial discipline remains a significant challenge, as evidenced by ongoing issues such as fund misallocation, procurement manipulation, weak budgeting practices, ineffective debt management, and the neglect of internal audit recommendations. The study recommends that technical universities complement national policies and legal frameworks with internally developed financial discipline policies tailored to their specific contexts. Central to this should be the establishment of a comprehensive Code of Ethics for Financial Discipline, with explicit provisions requiring strict adherence by leaders and senior officials and enforcing stringent penalties for breaches. Such context-specific policies, aligned with national directives, will reinforce ethical conduct, strengthen financial integrity, and promote sustainable accountability in technical universities.
Financial Discipline, Strategic Advisory Services, Risk Management, Policies, Financial Mismanagement, Sound Financial Decisions, budgeting
Corporate Investment in Indonesia: Literature Review
This research aims to conduct in-depth research on corporate investment decisions in Indonesia. The company's investment decision is an important factor in a business that will determine the company's condition in the future. There are cases of failure due to investment decisions made by companies, which is not in line with several studies that show the importance of corporate investment decisions. The investment decisions made by a company are different from those made by individual investors, this study will only analyze the investment decisions made by a company. Analysis is needed to find out the factors that affect a company's investment decisions or are influenced by investment decisions. The method used in this study is systematic literature review analysis. Using a database from a journal indexed by Sinta with the addition of the Scopus base with cases in Indonesia. Data collection uses systematic stages, which are then reviewed manually by the document. A company's investment decision is influenced by several variables and divided into financial and non-financial categories. This research has implications for investment decisions made by a company and is an additional literature for researchers in the future.
Investment Decisions, Corporate Investment Decisions, Literature Review, Corporate Investment
Cryptocurrency Accounting Frameworks and Tax Compliance Challenges: Toward a Unified Digital Financial Reporting Model
The swift growth of cryptocurrencies has highlighted several issues with current accounting and tax compliance regimes given that there is little to no accounting guidance in the current standards like IFRS or US GAAP regarding the measurement, recognition and disclosure of crypto-assets . (Rajaram & Aboluwodi, 2024; Mpanza, ). This paper analyzes the existing literature along with case practices to present the main concerns behind unreliable financial reporting and inefficient tax enforcement as being those of volatility, lack of clarity in legal categorization, problems of tax jurisdiction in cross- border transactions, and anonymity. Drawing on and comparing existing structures found in policy documents, financial statements, and guidelines, it makes the case for a theory of a singular digital financial reporting method utilizing the blockchain as a financial reporting and auditing platform through standardized measures and advanced disclosures of tax information. The suggested model attempts to harmonize accounting measurement with sound tax compliance as well as safeguard the users of financial statements from artificial liquidity, profitability and asset measurement or valuation. The results apply particularly to standard setters, regulators, and crypto-assets firms to move towards coherence in regulation and risk reduction in the digital financial ecosystem.
field of accounting for cryptocurrencies. Financial reporting of digital assets. Blockchain accounting standards. Crypto taxation issues. Consolidated model of reporting. IFRS and cryptocurrency value
Cash Flow Volatility in Ghanaian Banks (2004– 2024): A CV, Moving Average, and GARCH-Based Analysis
In this paper, the researcher will examine the volatility of cash flow among commercial banks in Ghana in the years 2004-2024 using simulated monthly data. It represents both cyclical behaviour and systemic shocks by using both descriptive metrics (coefficient of variation and moving averages) and sophisticated econometric modelling (GARCH (1,1) including exogenous covariates). Findings indicate that net cash flows vary considerably, especially in the years 2008 global financial crisis, the 2014 macroeconomic turmoil, the 2017-2019 banking sector clean-up, and the COVID 19 pandemic. GARCH (1,1) findings are consistent with volatility clustering and regime changes, structural-break-dummy variables in the years 2008, 2015, 2017-2018 and 2020 exhibit a significant positive influence on the conditional variance equation, revealing that the risk is higher in those years. The results imply that banks of Ghana have increased the level of liquidity risks and that more advanced forecasting instruments, contingency liquidity planning, and proactive, based on volatility, macroprudential supervision are needed.
Cash Flow Volatility; Ghanaian Banks, A CV, Moving Average, GARCH-Based Analysis
Economic Viability of Organic Farming Practices in Developing Countries
Organic farming has become a better option compared to conventional farming methods, offering sustainability, health benefits, and improved rural livelihoods. Organic farming practices face unestablished economic feasibility challenges in developing countries because these areas often deal with resource limitations along with market system faults. This paper analyzes available studies to understand how costs and benefits interact with market structures and policy structures to determine organic agricultural sustainability in these regions. Various studies show organic farming provides lasting economic advantages, together with ecological advantages, though transition difficulties and short-term disadvantages prevent many farms from adopting this system. Organic systems gain viability when policies support them and when knowledge and market integration develop effectively. Future empirical work should focus on developing solutions to barriers that limit organic agriculture expansion in developing economies, according to the recommendations for policymakers and development practitioners.
Economic Viability; Organic Farming Practices; Developing Countries
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Structural Changes in Indian Economy since the 1950s: A Markov Chain Analysis
Understanding the evolution of the production structure of an economy has been one of the core macroeconomic issues for academicians, professional economists, policymakers and governments for decades. Within this context, this study investigated the structural changes in the Indian economy since the 1950s using a time-varying Markov chain (TVMC) model—a data-oriented non-parametric methodology—within a panel data framework. It aimed to understand whether India’s production structure followed the Classical–Chenery path of transformation or deviated towards a service-led trajectory. The production sector of the Indian economy was classified into three sectors–agriculture, industry and service. The data on the sectoral shares in the real gross value added (GVA) in the economy was taken from the National Statistical Accounts and deflated using the Wholesale Price Index (2011–12 as base year). The period under investigation was divided into three distinct phases: the dirigiste pre-liberalisation period (1951–1991), the neoliberal post-liberalisation period (1991–2011), and the digital integration period associated with the digital era (2011–2021). The findings showed that the Indian economy took a different growth path, experiencing direct service-led structural transformation of its production system. Its growth path showed (i) persistent decline in the GVA share of the agricultural sector throughout the study period, which was consistent with the agricultural transformation observed in developed economies; (ii) a stagnant GVA share of the manufacturing sector, indicating limited industrial deepening; and (iii) a sharp, sustained and premature expansion in the GVA share of the service sector. While projections about the production structure suggest that the service sector will sustain and improve its GVA share in the long-run, it would pave the path of jobless growth in the Indian economy. The novelty of this study lies in integrating historical data with a probabilistic model to analyse sectoral transformation, providing insights for future industrial and employment policies. However, its contribution to the literature is methodological, offering insight on how long-run historical data can enhance our understanding of structural change in an economy, which remains crucial for long-run policy framing.
Economic development, Indian economy, Projection, Markov chain, Structural change
Determinants of small farmers’ integration and agricultural yield in inclusive business models: Insights from the cocoa sector in Côte d'Ivoire
This paper examines the factors influencing the integration of smallholder farmers and their agricultural yields within inclusive business models (IBs). The Heckman two-stage selection method was used for the analysis. The first-stage results showed that the farmer's age, household size, possession of a Tax Declaration of Existence, and association membership were determinants of smallholders’ probability of being integrated into IBs. Concerning the second stage, the number of working days, gender (male), and the farmer's experience had a positive and significant influence on agricultural yield.
Inclusive business models, smallholder farmers, agricultural performance, cocoa sector
Deep Learning Architectures for High-Frequency Stock Price Prediction: An Empirical Evaluation
High-frequency trading (HFT) generates vast amounts of financial data at millisecond intervals, presenting both opportunities and challenges for accurate stock price prediction. Traditional econometric and statistical models, while useful for low-frequency data, often fail to capture the nonlinear dependencies, temporal correlations, and microstructural patterns inherent in high-frequency financial markets. In this study, we present an empirical evaluation of multiple deep learning architectures including Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Temporal Convolutional Networks (TCNs), and Transformer-based models for high-frequency stock price forecasting. Using a dataset of tick-level order book information and intraday price movements from major U.S. exchanges, we assess each model’s predictive power, robustness, and computational efficiency. Evaluation metrics include root mean squared error (RMSE), mean absolute percentage error (MAPE), and directional accuracy, alongside financial performance measures such as cumulative returns and Sharpe ratio in a simulated trading environment. Results reveal that attention-based architectures, particularly Transformers, consistently outperform recurrent and convolutional counterparts in capturing complex temporal dependencies, while TCNs demonstrate superior efficiency in low-latency scenarios. The findings highlight the trade-offs between accuracy, interpretability, and latency, providing actionable insights for practitioners in algorithmic trading, risk management, and market microstructure research.
High-frequency trading; Stock price prediction; Deep learning architectures; LSTM; GRU; Temporal Convolutional Networks (TCN); Transformers; Financial time series forecasting; Market microstructure; Algorithmic trading
Explainable Artificial Intelligence for Credit Risk Assessment: Balancing Transparency and Predictive Performance
Credit risk assessment is a cornerstone of financial decision-making, guiding loan approvals, interest rate determination, and capital allocation strategies. While machine learning and deep learning models have demonstrated superior predictive accuracy compared to traditional statistical techniques, their black-box nature often undermines trust, interpretability, and regulatory compliance. This study explores the integration of Explainable Artificial Intelligence (XAI) into credit risk modeling, with the dual goal of enhancing transparency while maintaining strong predictive performance. We propose a hybrid framework that combines gradient boosting and neural network models with post-hoc interpretability tools such as SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations), alongside inherently interpretable models like decision trees and logistic regression. By evaluating the trade-offs between accuracy, fairness, and explainability on benchmark credit datasets, we demonstrate that XAI methods can provide actionable insights into borrower default risk without substantially compromising predictive power. Furthermore, we discuss the role of explainability in ensuring regulatory compliance, promoting fairness in lending decisions, and fostering trust among stakeholders. The findings suggest that transparent, high-performing models can strengthen risk management practices and support responsible innovation in the financial sector.
Credit risk assessment; Explainable Artificial Intelligence (XAI); Transparency; Predictive performance; Machine learning; SHAP; LIME; Fairness in lending; Regulatory compliance; Financial decision-making
Detecting Financial Fraud in Real-Time Transactions Using Graph Neural Networks and Anomaly Detection Techniques
Real-time fraud detection must balance accuracy with millisecond-level latency as adversaries evolve tactics across accounts, devices, merchants, and networks. This paper presents a streaming framework that models payment ecosystems as dynamic, heterogeneous graphs and detects anomalies by fusing Graph Neural Networks (GNNs) with online anomaly detectors. Incoming transactions update a temporal multi-relational graph (card–device–merchant–IP), from which a lightweight GNN (GraphSAGE/GAT variants with edge features and time encoding) produces embeddings on the fly. These embeddings feed (a) a cost-sensitive classifier for known fraud and (b) unsupervised detectors (e.g., Isolation Forest/Deep SVDD) to surface novel, label-sparse attacks. To cope with class imbalance and concept drift, we employ streaming reweighting, adaptive thresholds tuned on precision@k, and continual learning via replay and drift triggers. The system exposes local explanations (subgraph rationales via GNNExplainer/motif scores) to support analyst review and regulatory needs, while a deployment blueprint (feature cache, micro-batching, and asynchronous inference) meets <50–100 ms decision budgets. We evaluate on mixed synthetic/industry datasets with evolving fraud scenarios, reporting ROC-AUC/PR-AUC, detection delay, alert volume, and business impact under cost constraints. Results show consistent gains over rule-based, tabular ML, and static graph baselines, particularly for low-footprint fraud and fast-moving attack campaigns. The proposed design offers a practical path to accurate, auditable, and scalable fraud screening in production payment streams.
Financial fraud detection; Graph neural networks; Dynamic/temporal graphs; Real-time streaming analytics; Anomaly detection; Concept drift; Class imbalance; Explainable AI; Cost-sensitive learning; FinTech deployment architecture
AI-Powered Financial Analytics and Visualization Tools: The Evolving Landscape of Strategic Finance
The incorporation of artificial intelligence (AI) in financial analytics and visualization has become a strategic necessity of contemporary finance in the age of inspired technology change. The topic covered in this research paper is the changing landscape of the use of AI to power financial applications and the nature and benefits of such applications in transforming the traditional financial management systems into systems of data-driven decision making. Through the utilization of machine learning, natural language processing, and predictive analytics ARTS insight and forecasting are no longer just the preserve of data scientists and machine learning specialists, rather with the new tools companies can now provide real-time insights and add to their forecasting models with a much higher degree of accuracy and capital allocation strategies can be optimized. The research study uses a large data set which contains a wide range of the indicators of global economies such as GDP, inflation, interest rates, and the gross national income which will be analyzed using AI enabled visualization tools like Tableau and Python. The discussed analysis shows that AI models are capable of unearthing hidden trends, sources of anomalies, and modeling hypothetical circumstances that enhance financial robustness. Some of the most interesting findings are of how AI is being deployed in terms of strategic finance teams, which focus on reducing the amount of financial data that is being input into a highly visual relative form of data to improve budgeting and planning decisions involving investment and risk assessment. The suggested use of AI algorithms can help companies to be proactive in case of macroeconomic changes and political instabilities by creating automated insights in time. The paper has come to the conclusion that the analytical technology AI is not only redefining the support to frontline finance functions, but is also redefining strategic planning at the organizational level. As they keep developing, these technologies have the potential to transform the finance role into an agile, intelligent, predictive and adaptive system. The paper adds to the body of knowledge about financial digitalization because it highlights the role of AI in enhancing efficiency, transparency, and long-term competitiveness in long-term competitiveness in strategic finance.
Artificial Intelligence, Financial Analytics, Data Visualization, Strategic Finance, Predictive Modeling and Economic Forecasting
Predicting and Preventing Drug Shortages: A Big-Data Digital-Twin Framework for Pharmaceutical Supply-Chain Optimization
Drug shortages put patients at risk and make the health system less efficient. This research suggests a comprehensive big-data simulation framework to forecast and preempt shortages within multi-echelon pharmaceutical supply chains, encompassing APIs, manufacturers, wholesalers, and both hospital and community pharmacies.We consolidate demand signals (claims, EHR orders, syndromic trends), production and quality data (recalls, batch yields, inspections), logistics (lead times, port congestion), and market structure (single-source risk, inventory) within a data lake. A hybrid pipeline merges gradient-boosted risk scoring, graph/network analytics, and a discrete-event/agent-based digital twin fine-tuned over five years of operations. Scenario experiments test policies for reducing risk, such as dynamic allocation, therapeutic substitution, prioritised rationing, selective dual sourcing, and targeted safety stocks. The framework cuts expected shortage days by 30–44% and backorders by 32–51% compared to current policies, while keeping logistics and holding costs within a 5–9% range. The early-warning risk scores (AUC≈0.85) give you 2–6 weeks to start taking steps to reduce the risk. A Pareto frontier appears: small, well-timed reallocations and selective dual sourcing provide the most benefits; blanket inventory inflation is the main issue. Health systems, wholesalers, manufacturers, and regulators can prioritise scarce vials, negotiate contingency capacity, and test emergency-use or import flexibilities in silico. Explainable models show which upstream nodes (API sites, fill-finish lines) cause risk, which helps make decisions that can be checked. We combine policy search with predictive learning in a digital twin that is aware of networks for supply chains that are important for humanitarian purposes.Originality/value: This playbook takes data from the beginning to the end and turns early warning signs of risk into cost awareness. These grounded actions stop drug shortages while keeping service levels high at scale.
Copyright (c) 2024 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence in Corporate Financial Strategy: Transforming Long-Term Investment and Capital Budgeting Decisions
Artificial Intelligence (AI) is increasingly transforming the landscape of corporate financial strategy, particularly in the domain of long-term investment and capital budgeting. Traditional evaluation methods—such as Net Present Value (NPV), Internal Rate of Return (IRR), scenario analysis, and real options valuation—are essential tools for strategic decision-making but are constrained by rigid assumptions, historical data limitations, and the inability to adapt quickly to uncertainty. AI-powered models, by contrast, offer dynamic, data-driven approaches that enhance forecasting accuracy, risk assessment, and strategic flexibility. Through machine learning, natural language processing, and predictive analytics, corporations can analyze vast amounts of financial and non-financial data, identify hidden patterns, and generate real-time insights to guide investment decisions. This paper examines how AI augments the capital budgeting process by refining cash flow estimation, enabling advanced scenario simulations, and integrating external factors such as macroeconomic volatility, market sentiment, and ESG considerations into corporate financial planning. The study further investigates the role of AI in real options valuation, where adaptive models provide managers with enhanced flexibility in responding to evolving market conditions, technological disruptions, and competitive pressures. In addition, ethical and governance implications are discussed, particularly regarding the transparency and interpretability of AI-driven financial decisions. By bridging traditional corporate finance theories with advanced AI applications, this research highlights the potential for firms to optimize capital allocation, reduce uncertainty in investment planning, and maximize long-term shareholder value. Ultimately, the study argues that AI is not merely a supportive tool but a transformative force capable of redefining corporate financial strategy and positioning organizations for sustainable growth in increasingly complex global markets.
Corporate Finance, Capital Budgeting, Net Present Value (NPV), Internal Rate of Return (IRR), Scenario Analysis, Predictive Analytics, Investment Decision-Making, Risk Management, Financial Forecasting, ESG Integration
Managing Purchasing Power in Today’s Global Economy: Challenges and Strategic Approaches
This paper explores the evolving nature of purchasing power in the context of current global economic dynamics. Drawing insights from the book 'Purchasing Power Management,' the study examines the underlying causes of declining real purchasing capacity among middle and lower-income populations, especially in post-colonial and developing economies. The paper emphasizes the role of inflation, unstable income distribution, import dependency, and monetary policy in shaping modern consumer power. It proposes a conceptual model integrating real income, price levels, and employment factors to better understand this phenomenon. Through a review of global policy practices and theoretical insights, the study provides strategic recommendations for governments to stabilize and strengthen household purchasing power. This includes approaches such as wage indexation, targeted subsidies, price regulation, and local production support. The study aims to provide a structured academic framework to inform policymaking in emerging and vulnerable economies.
Purchasing Power, Inflation, Income Inequality, Consumer Economics, Post-Colonial Development, Economic Policy, Global Markets
The Frozen Frontier: United States Arctic Strategy in a Shifting Economic Landscape
The Arctic region has rapidly evolved into a focal point of 21st-century geopolitical competition, emerging as a critical arena for economic opportunity and global power projection due to its vast resource reserves, emerging trade routes, and increasing strategic relevance. This paper examines the dynamics of modern Arctic geopolitics, analyzing the growing involvement of major powers—namely, the United States, Russia, and China—with a particular focus on the United States' policy and expansion in the region. In contrast to Russia's militarized approach and China's economic engagement, the United States is shown to possess relatively limited capabilities in the region, hindered by an underdeveloped icebreaker fleet, infrastructure gaps, and diplomatic challenges arising from its increasingly isolationist posture. This analysis calls for a measured but assertive approach to Arctic expansion, grounded in enhanced polar capabilities, infrastructure investment, and multilateral cooperation to balance geopolitical risks. The paper concludes that such expansion is not only necessary to safeguard American strategic interests but is also key to counterbalancing adversarial advances and ensuring long-term energy security and access to trade routes, driving both economic growth and regional stability. The paper contends that America's long-term economic and geopolitical success in the Arctic rests upon decisive action, resilient infrastructure development, and the careful management of alliance dynamics and collective defense in the region.
Forecasting Currency Volatility in Global Markets Using Transformer Models: Implications for U.S. Trade and Investment Strategies
Currency volatility has become one of the most significant challenges for global financial stability and U.S. trade competitiveness. Traditional econometric approaches, such as GARCH and VAR models, often fail to capture the nonlinear dependencies and structural breaks inherent in currency markets. This paper proposes the use of transformer-based deep learning models for forecasting short- and medium-term exchange rate volatility across major and emerging market currencies. By leveraging self-attention mechanisms, transformers can model long-range dependencies in high-frequency financial data, capturing hidden structures often overlooked by conventional models. Empirical analysis demonstrates that transformer models outperform GARCH, LSTM, and GRU baselines in predictive accuracy and volatility clustering detection. Furthermore, the study evaluates the strategic implications of currency volatility forecasts for U.S. trade policy, hedging strategies, and foreign investment decisions. Results highlight the potential for AI-driven forecasting systems to provide U.S. firms, investors, and policymakers with actionable insights for risk management, portfolio allocation, and international trade strategy.
Currency volatility; exchange rate forecasting; transformer models; deep learning; financial risk management; trade strategy; U.S. foreign investment; macro-financial stability; attention mechanism; AI in finance
AI-Powered Early Warning Systems for Emerging Market Crises: Enhancing U.S. Foreign Investment Risk Strategy
Emerging markets (EMs) exhibit nonlinear dynamics and contagion pathways that can rapidly amplify local shocks into systemic crises, exposing U.S. investors to outsized downside risk. This paper proposes an AI-powered early warning system (EWS) that forecasts the probability and timing of EM crisis events currency crashes, sovereign distress, and capital-flow sudden stops over 3/6/12-month horizons. The framework integrates multi-modal data: macro–financial indicators (FX, rates, CDS, reserves, external balances), market microstructure signals (order-flow imbalance, jump intensity), and alternative data (news and social sentiment, trade/shipping activity, satellite night-lights). Methodologically, we combine temporal transformers for regime-aware sequence modeling with graph neural networks over exposure networks (trade, banking, portfolio flows) to capture spillovers, and gradient-boosting models for calibrated probabilities. A composite Crisis Risk Index (CRI) is produced via isotonic calibration, with uncertainty bands from conformal prediction. Model transparency is ensured through SHAP-based global and local explanations, counterfactual analysis for policy levers (e.g., reserve adequacy, rates), and stability checks against data revisions. Backtests benchmark against canonical EWS rules and logistic/KLR-style baselines, evaluating AUC, Brier score, precision, false-alarm costs, and average lead-time. We illustrate decision utility for U.S. foreign investment strategy through three use cases: (i) dynamic country allocation and hedging, (ii) pre-trade risk budgeting with crisis-conditioned scenarios from a generative stress engine, and (iii) portfolio-level loss mitigation under liquidity and currency constraints. The system operationalizes an end-to-end pipeline ingestion, nowcasting, horizon forecasting, and alert governance suitable for investment committees and risk offices. Results indicate materially improved early warning lead-time and fewer false positives versus traditional indicators, enabling earlier de-risking and more resilient U.S. exposure to EM cycles.
Early warning systems; emerging market crises; sovereign risk; currency crash; sudden stop; machine learning; temporal transformers; graph neural networks; explainable AI (SHAP); alternative data & news sentiment; contagion and spillovers; U.S. foreign investment risk management
The Future of Enterprise Finance: Lessons from ECC-to-S/4HANA Migrations Across Industries
Enterprise Resource Planning (ERP) systems are the foundation of modern financial and operational management. For decades, SAP ECC (ERP Central Component) has provided the backbone for industries ranging from manufacturing and healthcare to retail and the public sector. However, with SAP announcing the end of mainstream maintenance for ECC by 2027 (and only offering costly extended support until 2030), organizations face a critical inflection point. This paper examines the drivers compelling migration, the challenges of transformation, and the lessons learned from cross-industry programs moving from ECC to SAP S/4HANA, SAP’s next-generation digital core. Unlike ECC, S/4HANA leverages an in-memory database to deliver real-time analytics, cloud-native scalability, embedded compliance, and integration with emerging technologies such as AI and machine learning. Drawing on global benchmarks, industry case studies, and policy frameworks, the paper positions migration not as a technical IT upgrade but as a strategic re-architecture of enterprise finance. It further highlights the executive implications for CFOs and CIOs, the policy perspective of finance as critical infrastructure, and future research directions in AI-enabled and quantum-resilient finance systems. The findings make clear that delaying migration magnifies risk—technical debt, regulatory exposure, and obsolescence—while early adopters are seizing competitive advantage. The imperative is clear: SAP S/4HANA is the future of enterprise finance.
SAP ECC, SAP S/4HANA, ERP migration, enterprise finance, digital transformation, compliance, cloud ERP, AI in finance
Social Return on Investment of Establishing a Cancer Drug Fund in Thailand
Cancer remains the leading cause of death in Thailand, with 336 new cases and 221 deaths daily, yet access to high-cost cancer therapies under the universal health coverage system remains limited. This disparity creates significant treatment inequities and highlights the need for innovative financing mechanisms to expand access to life-saving drugs. Drawing lessons from the United Kingdom’s Cancer Drug Fund, this study evaluates the potential social return of establishing a similar fund in Thailand. A two-pronged framework was adopted to quantify social benefits: (1) improvements in quality of life, measured by changes in Disability-Adjusted Life Years using an Interrupted Time Series with Control Group model comparing the United Kingdom (treatment group) and Germany (control group), and (2) reductions in inequality of health access, assessed using the Transferability of Economic Evaluation framework by applying per-patient cost data from the UK CDF to the Thai context. Monetary values were derived using Thailand’s GDP per capita as a financial proxy. The ITSCG analysis indicated a reduction of 16.95 DALYs per 100,000 population associated with CDF implementation, equivalent to THB 3,381.76 million in social value. In addition, under a simulated annual budget of THB 15,000 million, expanding access to high-cost cancer drugs was estimated to benefit 8,367 patients, generating THB 14,808.68 million in social value. Combining these outcomes, the total social benefit was estimated at THB 18,190.45 million, yielding a Social Return on Investment ratio of 1.21. These findings suggest that establishing a CDF in Thailand could deliver a substantial positive social return by improving health outcomes, reducing access inequalities, and alleviating the long-term burden of cancer. Policy adoption of such a fund could enhance equity, ensure financial sustainability, and strengthen Thailand’s public health system
Cancer Drug Fund, Social Return on Investment, DALYs, Quality of Life, Health Access, Interrupted Time Series, Thailand
Decentralizing Real Estate Markets: Evaluating REITs and Blockchain Tokenization Approaches
Blockchain-based tokenization is transforming the real estate sector, presenting a compelling alternative to the traditional model of Real Estate Investment Trusts (REITs). As the industry shifts from financialization to decentralization, driven by technological advancements, these two models offer different approaches to democratizing real estate investment.REITs have been a foundational aspect of real estate financialization, enabling individual investors to participate in large-scale real estate ventures through fractional ownership of diversified property portfolios. This has broadened the investor base and improved market liquidity. However, the emergence of blockchain technology and decentralized finance (DeFi) introduces a new paradigm: real estate ownership can now be fractionalized into digital tokens. This enhances liquidity, transparency, and accessibility through global 24/7 trading platforms. While REITs have made significant strides in expanding access to real estate investment, blockchain-based tokenization can further enhance these achievements by lowering entry barriers, reducing transaction costs, and decentralizing market operations. Nevertheless, the adoption of blockchain technology in real estate also comes with challenges, including regulatory uncertainties, technological risks, and the need for robust governance frameworks. As the lines between finance and technology continue to blur, it is essential to adapt regulatory frameworks and investment strategies to navigate this evolving landscape. The critical review highlights the future implications of these trends, emphasizing the importance of continued research and regulatory innovation to fully realize the potential of decentralized real estate markets. This is particularly relevant in addressing issues of housing inequality and affordability, as housing serves not only as an investment vehicle but also as a fundamental shelter for people.