Journal of International Financial Trends
Journal of International Financial Trends (JIFT) is a peer-reviewed, open-access journal dedicated to advancing research on global financial systems, emerging market trends, and the intersection of economics, policy, and technology in international finance. JIFT welcomes submissions from researchers who seek to contribute to the ongoing discourse on international financial trends. The journal encourages original research, empirical studies, and theoretical analyses that offer fresh perspectives on the evolving global financial landscape.
Testing the Flow-Oriented vs. Portfolio Balance Hypotheses: Beyond Trade Channel Evidence of Balance Sheet Effects Dominance in Asia-Pacific OECD Equity and FX Markets
This study explores the dynamic, multi-horizon linkages between stock market prices and exchange rates across Asia-Pacific OECD countries to test the competing validity of the Flow-Oriented and Portfolio Balance hypotheses. Employing a comprehensive econometric framework that integrates the Panel CS-ARDL model with Breitung-Candelon frequency-domain causality, Time-Varying Parameter Panel VAR (TVP-P-VAR), and asymmetric panel ARDL techniques, the research captures long-run equilibria, regime-dependent shifts, and structural asymmetries. The empirical findings decisively invalidate the traditional flow-oriented trade competitiveness perspective, revealing instead that currency depreciation and interest rate hikes inflict severe contractionary damage on equity valuations through a dominant Balance Sheet effect. Consequently, asset market channels and sentiment-driven capital flows in Asia-Pacific countries with financial integration operate as stronger forces than traditional goods market mechanisms. Conversely, the results strongly validate the stock-oriented Portfolio Balance model, demonstrating that bullish market sentiment and equity market expansions drive rapid, high-frequency currency appreciation via portfolio capital inflows. Asymmetric and time-varying estimations reveal that regional financial markets react significantly more aggressively to negative macro-financial shocks during stress regimes than to positive stimuli during economic expansions. Another unresolved issue concerns whether the stock–FX relationship is homogeneous across Asia-Pacific OECD economies. These economies differ significantly in exchange rate arrangements, export dependence, capital market sophistication, institutional quality, and openness to international investment. The study contributed to the financial market literature by establishing that asset market channels and sentiment-driven capital flows have superseded traditional goods market mechanisms in financially integrated economies. Central banks should be positioned to understand both the limits of interest rate defenses and the need to handle asymmetric volatility. The findings of the research have broad implications that require policymakers, central banks and financial regulators to develop and execute policies which will achieve sustainable financial market growth. These insights urge policymakers to abandon static linear assumptions and adopt dynamic, macroprudential risk management frameworks to safeguard regional financial stability.
Copyright (c) 2026 David Umoru, Beauty Igbinovia, Emoabino Muhammed, Imran Enike Abu (Author) https://creativecommons.org/licenses/by/4.0
Volatility Persistence, Dynamic Co-Movements, and Portfolio Optimization in Digital Asset Markets: Disentangling Cross-Market Contagion from Internal Hedging Strategies
This study investigates the time-varying volatility dynamics, persistence, and optimal portfolio allocation architecture within the digital asset ecosystem, explicitly separating external cross-market contagion from internal digital asset hedging strategies. Utilizing real-world daily historical return data from 2020 to 2025 for a diverse basket of major (Bitcoin, Ethereum, Litecoin) and niche (Stellar, Internet Node Token, Exclusive Coin) digital assets, we employ a multivariate DCC-GARCH framework evaluated under two distinct currency configurations (Euro and US Dollar numeraires) alongside native on-chain alternative hedges. The empirical findings reveal high volatility persistence and long-memory characteristics across all assets, with volatility half-lives extending up to 22.8 days for specialized tokens, confirming that shocks to the system decay slowly. The regime-dependent dynamic conditional correlations demonstrate that cross-asset integration is highly pro-cyclical, intensifying severely during systemic market drawdowns and rapidly eroding traditional diversification benefits among core protocols. To counteract this systematic risk, the study introduces native digital hedges, demonstrating that the stablecoin Tether (USDT) offers superior downside protection and variance reduction (up to 74.15%) over traditional fiat exchange rates, while tokenized gold (PAXG) provides a robust, uncorrelated safe-haven overlay against macro-structural shocks. Significantly, out-of-sample optimization metrics confirm that the dynamic Global Minimum Variance Portfolio (GMVP) heavily outperforms static benchmarks, achieving a 66.19% risk reduction by rebalancing asset exposures daily in response to shifting conditional variances. The study suggests that the Global Minimum Variance Portfolio is not composed solely of the largest caps (BTC/ETH). Instead, significant weight must be shifted toward assets with lower unconditional variance (like STR) or distinct covariance structures (like INT) to achieve the true efficient frontier. The optimal hedge ratios (OHR) and optimal portfolio weights (OPW) demonstrates that while the crypto market is highly integrated, strategic allocation to uncorrelated assets like INT and stable performers like STR can significantly improve risk-adjusted returns. The study highlights the role of cryptocurrencies as not isolated digital assets but as systemic components with contagion effects that are increasingly significant for regulators, monetary authorities, and investors. By analyzing these cross-market dynamics during a period marked by economic volatility, including the COVID-19 pandemic and global inflationary pressures, this research provides valuable insights into the evolving relationship between cryptocurrencies and traditional financial assets. These findings provide critical insights for investors seeking to minimize portfolio variance and hedge against downside risk in the volatile cryptocurrency market. Furthermore, the allocation mechanism exposes a fundamental theoretical boundary in asset insulation, proving that portfolio optimization models will aggressively restrict high-correlation diversifiers if the underlying instrument carries unacceptable standalone baseline noise. This research provides a self-contained, digital-first risk mitigation toolkit for international asset allocators while offering critical insights for macroprudential watchdogs tasked with stablecoin regulation and systemic risk oversight.
Copyright (c) 2026 Beauty Igbinovia, Shiloh Akpan, Imran Enike Abu, Emoabino Muhammed, David Umoru (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Transformation of Environmental, Social, and Governance (ESG): A Systematic Review, Gap Analysis, and Future Research Agenda
Environmental, Social, and Governance (ESG) considerations have become a central framework for corporate accountability, regulatory compliance, and sustainable value creation amid growing environmental challenges, stakeholder expectations, and institutional pressures. At the same time, advances in Artificial Intelligence (AI) are transforming ESG systems by enabling large-scale data integration, predictive sustainability analytics, automated reporting, and real-time governance intelligence. These developments are accelerating the digitalization of sustainability management and reshaping how organizations measure, disclose, and govern ESG performance. This study conducts a systematic literature review to synthesize and critically evaluate the emerging body of research on AI-driven ESG transformation. Adopting a socio-technical governance perspective, the review conceptualizes AI-enabled ESG integration as a transformative process in which algorithmic systems increasingly influence sustainability assessment, disclosure, monitoring, and decision-making across organizational and institutional contexts. The findings reveal that despite significant technological progress, several barriers continue to constrain the effective application of AI in ESG. These include fragmented and inconsistent ESG data infrastructures, algorithmic bias and opacity, regulatory fragmentation, weak explainability and auditability mechanisms, and unresolved ethical, social, and environmental risks. The review also identifies important gaps related to cross-country regulatory evidence, lifecycle assessments of AI technologies, stakeholder trust, and mechanisms for enhancing ESG disclosure credibility while mitigating greenwashing and machine-washing risks. Based on these findings, the study proposes a future research agenda emphasizing ESG data standardization, trustworthy and explainable AI, regulatory harmonization, governance integration, and interdisciplinary approaches to understanding the broader sustainability implications of AI-enabled ESG systems.
Copyright (c) 2026 Attia Gomaa (Author) https://creativecommons.org/licenses/by/4.0
Microstructural Impact of Algorithmic Trading on African Equity Markets: A Causal Evaluation of Efficiency, Liquidity, and Stability
The study provides a detailed empirical evaluation of the effect of the automated trading otherwise known as algorithmic trading (AT) on market efficiency, liquidity, and steadiness in ten main African equity markets. The study offers an in-depth empirical assessment of the impact of AT on market efficiency, liquidity, and stability in ten major African equity markets. To effectively close a critical gap in the literature on research on frontier-markets, the study applies an integrative approach to methodology, which combines along with intraday market microstructure evidence, causal inference approaches and machine learning practices. The intensity of algorithmic trading was measured using microstructure tools, and the study measures the consequential variables of the which includes return autocorrelation, bid ask spread, market depth and volatility persistence. We adopt quasi-experimental designs that exploit market-structure reforms for causal identification on a methodological level. By using a Finite Mixture Model, the analysis first classifies continent exchanges into Class 1 Deep Markets (42% probability, average capitalization $245.6B) and Class 2 Thin Markets (58% probability, average capitalization $18.7B). It shows that AT intensity is highly concentrated in deep capitalized venues. Using a robust double machine learning framework with 5-fold cross-fitting, we estimate the causal effects of quoting automatically. The estimates indicate that an increase in Message-to-Trade Ratio (MTR) and Cancellation-to-Trade Ratio (CTR) compresses the transaction cost significantly. In particular, the effective spread decreases by 0.156 (p < 0.01) and 0.167 (p < 0.01), respectively, while increasing log market depth by 0.098 and 0.087. AT robustly improves informational efficiency, reducing Kyle’s by up to 0.021 units. We quantify a distinct structural trade-off: higher AT activity also leads to a statistically significant rise in realized volatility (MTR effect: +0.027; CTR effect: +0.031). Predictive modeling based on Long Short-Term Memory (LSTM) neural networks demonstrates that high AT environments considerably enhance predictability, despite an increase in baseline volatility; improving out-of-sample RMSE forecasting performance by 11.9% and boosting R2 by 18.5%. To sum up, our high-frequency anomaly detection reveals that AT does not change the market shock frequency baseline of 1.92%. However, it provides a significant shock absorber that compresses the anomaly duration by 3.6 minutes, helps the market recover post-shock in 16.6 fewer minutes, and reduces the extreme volatility multipliers from 4.2x to 3.8x. Most prominently, findings indicate that the African Trade (AT) Exchange is essential for market liquidity and resilience, although the share is not equal. The study finds that a common feature of algorithm trading is its capacity to improve informational efficiency, contributing positively to the information quality of prices. It does so by reducing the return autocorrelation and order flow predictability. Having state-dependent dynamics implies that the sensitivity to shocks runs through the rich posterior distribution of shocks for various parameters. To the point, the effects are quite heterogeneous since they are distinctly moderated by the original depth of the market and the structure of the exchange. A customized policy prescription study for African regulators and market actors to illustrate the need to measure visibility, establish robust market structures, and execute conditional reforms in line with the specifics of the market situation and liquidity needed.
Copyright (c) 2026 David Umoru, Imran Enike Abu, Beauty Igbinovia, Emoabino Muhammed (Author) https://creativecommons.org/licenses/by/4.0
Risks, Sustainability, and Interconnection between FinTech Adoption and Traditional Investments
FinTech has rapidly transformed the global banking sector by redefining financial intermediation, investment systems and risk management practices. This paper explores the association between FinTech adoption, banking risks, sustainability performance and conventional investments in an increasingly interconnected financial environment. The study investigates the effect of digital financial innovations, including artificial intelligence, blockchain technology, cloud computing, digital payment systems, and big data analytics, on banking sustainability, financial stability, and investment interconnectedness. The methodology is qualitative and conceptual, and it is based on a systematic review of recent academic studies published between 2020 and early 2026. The study synthesizes the existing theoretical and empirical literature on FinTech adoption, operational and cybersecurity risks, sustainable banking practices, ESG-oriented finance, and financial interconnectedness. In this paper, rather than empirically estimating these relationships, an integrated conceptual framework is developed to explain the dynamic linkages between FinTech adoption, banking sustainability, risk exposure and traditional investment systems. The literature reviewed in this study indicates that the adoption of FinTech can have a positive impact on banking efficiency, operational flexibility, financial inclusion, customer experience, and sustainability performance through digital transformation and technological innovation. FinTech further enables ESG-oriented financial practices and the modernization of traditional investment systems. But growing reliance on digital financial infrastructures may also give rise to increasing operational, cybersecurity, regulatory and systemic risks at the same time. Moreover, stronger links between digital financial assets and traditional investment markets could increase financial contagion and market volatility in times of economic uncertainty. This study contributes to the literature by offering a comprehensive conceptual framework that encompasses FinTech adoption, banking sustainability, financial risks, and investment interconnectedness in the contemporary financial systems. It also offers potential directions for empirical research and measurable indicators for future quantitative research. The findings are of significant interest to policymakers, financial institutions, regulators and investors who want to strike a balance between technological innovation, financial resilience and sustainable economic growth.
Copyright (c) 2026 Oumaima Abouzaid (Author) https://creativecommons.org/licenses/by/4.0
Do Portfolio Construction Strategies Matter in Mitigating Macroeconomic Risks?
This study examines how macroeconomic variables—the market risk premium, inflation, the exchange rate, and the interest rate—affect the performance of portfolios constructed using six different strategies applied to the Egyptian Exchange (EGX). Using monthly data for 31 non-financial stocks over the period of 2020 to 2024, we construct and compare Equal Weighting (EW), Minimum Variance (MinVar), Equal Risk Contribution (ERC), Mean-Variance Optimization (MVO), Value, and Growth portfolios, and subject all six to a multivariate OLS regression framework with stationarity controls. The market risk premium dominates all other factors across every strategy. Minimum Variance portfolios have been most affected by the exchange rate depreciation, while the Growth portfolios have been insulated to a great degree. The interest rate positively impacted five portfolios, which is a paradoxical effect. The interest rate normally negatively impacts portfolios, but in the case of the Egyptian economy, the interest rate increased during the crisis, which caused the risk premium to widen. The inflation rate positively impacted the Value portfolio. The results showed an interesting anomaly whereby the heuristic Equal Risk Contribution portfolio outperformed the theoretical Minimum Variance portfolio. Policy implications include transparent exchange-rate adjustment, credible inflation targeting, and improvements in corporate governance standards.
Copyright (c) 2026 Shahd Elboghdady (Author) https://creativecommons.org/licenses/by/4.0
Factors Influencing Consumer Acceptance of AI-powered Shopping Assistants: Evidence from Jumia Sierra Leone
The National Innovation and Digital Strategy (2019–2029) of Sierra Leone emphasizes the application of technology to foster economic growth, improve service provision, and invigorate trade. This strategy emphasizes including digital tools in corporate framework and adherence to the United Nations E-Commerce and Digital Economy Principles. Despite adopting this strategy, the country still faced significant issues regarding implementing and adopting AI-powered tools to assist customers in shopping. This study explored factors influencing consumer acceptance of AI-powered shopping assistants in Sierra, with a significant focus on customers of Jumia Sierra Leone. Through the snowball sampling technique, the study collected data using Google Forms from 384 customers who have been using Jumia. The collected data was analyzed using the structural equation model (SEM) and bootstrapping method of AMOS software. The study found that the perceived usefulness of AI-powered shopping assistants had a positive and significant impact on behavior intention toward AI-powered shopping assistants. It was discovered that perceived ease of use of AI-powered shopping assistants had a negative and significant impact on behavior intention toward AI-powered shopping assistants. The results reveal that attitudes towards AI partially mediate the relationship between perceived usefulness, ease of use, and behavior intention toward AI-powered shopping assistants. Jumia should highlight the benefits of its AI-driven shopping assistants, enhancing the shopping experience and time efficiency and providing tailored recommendations.
Copyright (c) 2026 Sulaiman Kamara, Saidu Koroma, Abdul Caesar Fofanah (Author) https://creativecommons.org/licenses/by/4.0
How Does Diversification Affect Financial Performance? Evidence from a Case Study of Meituan Group
In the current era, characterized by constant changes in the market landscape, enterprises seeking development face numerous risks and challenges. Adopting a diversified operations strategy has emerged as a critical direction for companies aiming to break through competition in homogeneous product markets. As an early adopter of diversification among Chinese internet platform enterprises, Meituan has achieved significant results through its diversified operations, ultimately realizing synergies among its various business units. Therefore, studying Meituan’s diversification strategy and its outcomes holds considerable significance. This paper elaborates on the research background, purpose, and significance of diversification studies and systematically outlines the concepts and importance of diversification research. It examines Meituan’s diversification strategy from three perspectives: the implementation context, development history, and specific manifestations of the strategy. Furthermore, it analyzes the pathways through which diversification impacts financial performance and conducts a comprehensive evaluation using four financial indicators: solvency, operational capability, profitability, and growth capability.This paper takes Meituan as the research object and employs case study method and financial analysis method to conduct a correlational analysis on the impact of diversified operations on Meituan’s financial performance. It is found that there is a significant correlation between diversified operations and the improvement of enterprise financial performance. It is concluded that Meituan’s diversified operation strategy has a positive impact on the enterprise, but it also brings some problems and challenges to Meituan. This research focuses on exploratory case analysis, aiming to reveal the correlation path between diversified strategy and financial performance, and providing reference for similar platform enterprises.
Copyright (c) 2026 Leming Zhang, Aicong Liu, Shi Yin (Author) https://creativecommons.org/licenses/by/4.0
Economic Implications of Digital Banking on Financial Inclusion in the EMEA Regions
The economic impact of digital banking were explored in this study in a manner to expand financial inclusion across the EMEA (Europe, Middle East, and Africa) region. The study incorporated Technology Acceptance Model (TAM) in its central discussion while concentrating on two strategic components of economic interest how useful customers comprehend digital banking and how straightforward it is to adapt. The study discussed how consumers and small medium enterprises (SMEs) particularly in underserved communities are implementing these systematic inventions. By adopting Chi-square statistical analysis test, the study established a compelling correlation between digital banking adoption and improved financial inclusion, with a Chi-square value of 11.136 at the 0.05 significance level. The study findings underscore the access to internet connectivity, automated teller machines (ATMs), mobile phones, and point of sale (POS) terminals, performing the main functions in enabling the utilization of digital financial services. This study observed that the regions with greater digital infrastructure along supportive policies were inclined to high ranking adoption levels. On the other hand, obstacles persist specifically among the low-income citizens and rural areas where inequalities in digital literacy, inconsistent regulations, and limited infrastructure endure to limit economic progression. Integrating TAM across-regional economic environments, this study appends additional assessment to the discussion surrounding financial inclusion. The study points to the behavioral and technological factors that guide the implementation and demands inclusive schemes, focused financial education, and greater investment in digital infrastructure. These identifiable economic and technology components advanced practical speculations for policymakers, financial institutions, and tech developers thriving to creäte a more rightful digital economy throughout the EMEA economic regions.
Copyright (c) 2026 Inimfon Bassey Bassey, Okechukwu Matthew Ani , Frederick Oscar, Godwin Nse Ebong, Okwudili Matthew Ugochukwu (Author) https://creativecommons.org/licenses/by/4.0
Data Science vs Econometrics: Testing Different Forecasting Approaches
Forecasting is a critical activity for economists, financial analysts, and businesses engaged in budgeting and planning. Recent advances in Data Science promise greater forecasting sophistication and accessibility, often through automated or semi‑automated tools. However, these techniques also carry risks, particularly when users are unaware of the underlying data‑generating processes. This paper compares popular Data Science forecasting approaches with long‑standing Econometric methods using simulated data under varying assumptions about noise and structural complexity. The results show that while Data Science methods can perform competitively in highly noisy environments, structural econometric models consistently outperform them when data noise is low or when underlying relationships are complex. This reinforces a long‑standing insight often missed by automatic approaches: the source and structure of shocks matter for accurate forecasting.
Copyright (c) 2026 Colin Ellis (Author) https://creativecommons.org/licenses/by/4.0
Asylum as Geopolitical Currency: Rents and Incentives in Contemporary Migration Governance
This article examines the political economy of asylum as an emergent logic reshaping the contemporary international refugee regime. Building on the post-War II foundations of the 1951 Refugee Convention, the analysis traces how escalating displacement, geopolitical asymmetries and governance externalization have progressively eroded the universalist, rights-based architecture of asylum. Through a reconstruction of the concept’s theoretical lineage – introducing refugee rentierism and situating it within debates on rentier-state theory, migration diplomacy and global governance – the article argues that states increasingly convert the presence, containment or potential mobility of refugees into strategic rents. Drawing on a transdisciplinary framework that incorporates international political economy, global governance, international law and critical migration studies, the analysis maps a series of key cases – including the EU-Turkey Statement, EU partnerships with North African states, the UK-Rwanda plan, the Italy-Albania protocol, the Australia-Nauru offshoring model and recent U.S. initiatives in the Darién region. These practices reveal the instrumentalisation of refugees as geopolitical assets, enabling states to secure financial transfers, diplomatic concessions or domestic political capital while outsourcing protection obligations to peripheral regions. Normatively, the rise of refugee rentierism undermines the authority of international law, entrenches global inequalities and accelerates the delegalization and fragmentation of the asylum regime. The article concludes that refugee rentierism is not a marginal trend but a structural transformation that shifts asylum from an inalienable right to a transactional instrument, challenging the future viability of a universal protection system and calling for renewed commitments to global justice, solidarity and responsibility-sharing.
Copyright (c) 2025 Ricci Antonio (Author) https://creativecommons.org/licenses/by/4.0
Exploring the Impact of Marketing Strategies and Promotional Tactics on Consumer Behaviour in the Healthcare Sector, South Africa
Marketing strategies have been recognised as pivotal in shaping consumer behaviour and facilitating organisational success, particularly within the healthcare sector, where decision-making processes are inherently complex. This study examined the interplay between marketing strategies, focusing on promotional tactics, and consumer behaviour in healthcare. Guided by the conceptual framework of Ġostar and Ristanović, it investigated the influence of the 4 Ps (product, price, place, promotion) on healthcare consumer decisions. Employing a qualitative methodology, the study interrogated the efficacy of promotional tools such as discounts, coupons, and exclusive offers. Results demonstrated that while promotional strategies can effectively enhance brand engagement and stimulate short-term sales, overemphasising these tactics erodes long-term brand equity and financial viability. Furthermore, the study highlighted significant challenges intrinsic to healthcare marketing, including multifaceted stakeholder dynamics, restricted information access, and systemic complexity, which collectively constrain consumer agency. The findings underscore healthcare organisations’ need to balance immediate promotional gains against sustained innovation-driven growth. Consequently, the study advocates integrating service innovation, refining outsourcing models, and strategically aligning promotional activities with broader organisational objectives to reconcile profitability with sustainable innovation.
Copyright (c) 2025 Michael Willie (Author) https://creativecommons.org/licenses/by/4.0
The Mediation Effect of Service Innovation on the Relationship Between Promotional Strategies and Market Share in Medical Schemes, South Africa
Orientation: The dynamic global healthcare landscape requires ongoing adaptation and innovation. Service innovation (SI) is pivotal for healthcare funders and providers, particularly in medical schemes, where it significantly enhances the effectiveness of promotional strategies (PS) and improves market share (MS). Research Purpose: This study investigated the mediating effect of service innovation on the relationship between promotional strategies and market share in the private healthcare sector, with a focus on medical scheme beneficiaries in Gauteng, South Africa. It aimed to assess how service innovation amplified the impact of promotional strategies on market share and examined the implications of outsourcing marketing services. Motivation for the Study: Rising complexity in the healthcare market and the need for medical schemes to attract and retain members amid growing competition drive this study. Understanding how service innovation mediates the relationship between promotional strategies and market share offers insights into optimising marketing efforts for better market penetration. Research Approach/Design and Method: A quantitative research approach utilising a mediation analysis design was adopted. Data were collected from 260 beneficiaries of medical schemes in Gauteng, South Africa, through snowball sampling. The analysis followed the mediation framework proposed by Baron and Kenny, with additional validation conducted using STATA software, incorporating the approach developed by Zhao, Lynch, and Chen. Main Findings: Service innovation partially mediates the relationship between promotional strategies and market share, accounting for approximately 12% of the total effect. This finding underscores service innovation's role in enhancing promotional strategies' effectiveness. It indicates that service innovation is a key explanatory mechanism through which promotional efforts contribute to the growth or retention of market share within the healthcare insurance sector. Practical/Managerial Implications: Integrating service innovation into promotional strategies is crucial for healthcare funders and providers to improve market share. The study also underscores the importance of aligning outsourced marketing services with organisational goals to maximise effectiveness. Contribution/Value-Add: This research provides empirical evidence on the partial mediation effect of service innovation, offering valuable insights for optimising marketing strategies and addressing challenges in outsourcing marketing services. Thus, it enhances competitive positioning in the healthcare sector.
Copyright (c) 2025 Michael Willie, Abigail Chivandi (Author) https://creativecommons.org/licenses/by/4.0
A Boosted Deep ConVNet embedding Long Short Term Memory with Synthetic Minority Oversampling Techniques as Foiling Model for Payment Card Fraud
Payment card fraud with contemporaries have persisted as strain challenges bedeviling the financial institutions. With hi-tech thieves exploiting flaws in the digital fraud prevention and detection system to prompts derogatory effects of unquantifiable financial losses, cash back, and customer frictions. An ideal Fraud Detection System (FDS) and countermeasure is require for mitigating these concerns. As a result, several scholars anticipated statistical methods, rule-based approaches with many others for detection. But, majority of these approaches suffers from imbalance data distribution, high dimensionality with sparsity challenges, and real-time detection. This study recommended enhanced Deep ConVNet embedding Long Short-Term Memory and resampling method of SMOTE (DCNN-LSTM+SMOTE) as potential solution. The model is design and implemented on Google Colab platform with GPU; where Tensorflow is used for the DL and Scikit learn for ML models respectively, and Python as the modelled language. Firstly, baseline experiment is steered on two orthodox ML models of Random Forest (RF), Logistics Regression (LR) for feature selection and engineering. While probing kaggle dataset obtained; comprising 284,807 records with 31 field’s features. This dataset is very imbalance with data distribution sort of 0.17% deceitful and 99.8% non-deceitful. Second trialing is conduct; using SMOTE techniques to balance the dataset sort distribution and improved on used LR, RF, with other models such as Isolation forest, Artificial Neural Network (ANN), Multiple Layer Perceptron’s (MLP), Light Gradient Boosting Machine (LGBM), Deep ConVNet and Long Short-Time Memory. In testing efficacy of these models, confusion matrix performance evaluation metrics is delved. This revealed the outcome of the balancing model trial; that described the proposed DCNN-LSTM+SMOTE superclass performance against other models. Where, its accuracy score and prevalence result is 99.8% distinctly. The model offered the second least Error Rate of 0.2%. With 99.9% of recall, True Negative Rate (TNR), Precision and F1-Scores outcomes respectively. Cohen Kappa result is 99.6% and the false positive rate of 0.00%. This result validates the developed model as remarkable in performance, when compared with benchmark studies; and it is promising in the classification of fraudulent credit card transaction in financial institution.
Copyright (c) 2025 Lateef Salaudeen, Danlami Gabia, Muhammad Garbaa, Hassan Umar Surua (Author) https://creativecommons.org/licenses/by/4.0
Systemic Risks of Sophisticated Financial Products: A Critical Review of Post-2008 Innovations and the Impacts on Global Markets
This article provides a critical review of systemic risks arising from post-2008 financial innovation, with a focus on sophisticated instruments such as structured products, crypto derivatives and decentralised finance (DeFi). Drawing on recent theoretical developments and empirical insights, the study investigates how these products amplify market instability through leverage, interconnectivity and algorithmic reflexivity. The analysis criticises traditional regulatory frameworks, highlighting their limitations in addressing emerging risks and proposing a shift towards functional, risk-based governance. By consolidating fragmented academic and regulatory perspectives, the article offers a comprehensive conceptual synthesis of how modern financial engineering contributes to systemic vulnerabilities. It also identifies regulatory gaps, in areas such as programmable finance and synthetic tokenisation, that challenge current supervisory mechanisms. This analysis is intended to serve as a reference point for policymakers, researchers and financial institutions in the ongoing effort to mitigate systemic threats in increasingly digital and decentralised financial systems.
Copyright (c) 2025 Natália Teixeira (Author) https://creativecommons.org/licenses/by/4.0
The Moderating Role of Dividend Payout on the Relationship Between Working Capital Management and Profitability: Evidence from Japan's Technology Hardware and Equipment Industry
This study examines the moderating impact of dividend payout on the relationship between working capital management and profitability. The working capital management is measured by the cash conversion cycle, and return on assets and Tobin’s Q as profitability measure. The data were collected from the Thomson Reuters Eikon database spanning from 2002 to 2023. The study employed the two-step Generalized Method of Moments (GMM), and Fully Modified Ordinary Least Squares (FMOLS) methods, to estimate the moderating effect of dividend payout ratio on the relationship between CCC and profitability. The findings of this study revealed a negative and statistically significant impact of dividend payout on the profitability of Japan's manufacturing firms, particularly in the technology hardware and equipment industry. It also suggests that in industries where high capital expenditures are necessary, maintaining lower dividend payout ratio may be more conducive to sustaining profitability. These findings encourage a re-evaluation of dividend strategies, emphasizing the importance of aligning dividend payouts with the firm's long-term financial and operational goals to avoid undermining profitability. The study extends the inconclusive empirical evidence on the determinants of dividend policy, working capital management and profitability fills the lacuna in existing literature by focusing on how dividend policy influence working capital management practices and profitability of firms in Japan. The findings are also useful to the board of directors of non-financial firms in deciding an appropriate dividend policy, and to the shareholders in making investment decisions.
Japan technology hardware and equipment industry
Copyright (c) 2025 Saidu Koroma, Sulaiman Kamara (Author) https://creativecommons.org/licenses/by/4.0
Exploring User Adoption of Digital Lending Platforms in NBFCs: Insights from the Retail Loan Segment
This study aims to evaluate user perceptions of digital loan platforms, analyze the role of technology in enhancing loan accessibility, and provide strategic recommendations for Non-Banking Financial Companies (NBFCs) to optimize their digital lending infrastructure. A quantitative research design has been employed, with data collected from individual users of digital loan mobile applications to identify adoption patterns. Purposive sampling was used, targeting individuals who have processed loan applications through digital platforms at selected NBFCs in India, specifically those with a Head Office or Branch in the state of Gujarat. The findings reveal that Perceived Ease of Use (PEOU) has a greater influence than Perceived Usefulness (PU) on users’ Attitude Toward Use (ATU). This suggests that simplifying the digital loan application process has a more significant impact on shaping users' attitudes toward adoption than merely increasing the perceived usefulness of the platform. Non-banking financial companies (NBFCs) play a crucial role in addressing the credit needs of organized and unorganized sectors, particularly where traditional banks have limited reach. With a competitive advantage in offering customized products, NBFCs have a significant opportunity to expand in the micro, small, and medium enterprise (MSME) and retail sectors [1].
Copyright (c) 2025 Dr. SONAM ARORA, Arun Boliya (Author) https://creativecommons.org/licenses/by/4.0