Jewel Kumar Roy
57211688989
Publications - 15
Assessing the influence of financial repression on Bangladesh's financial development
Publication Name: Multidisciplinary Science Journal
Publication Date: 2026-07-08
Volume: 8
Issue: 3
Page Range: Unknown
Description:
We investigate how financial repression affects financial development of Bangladesh over the period 1980-2022. Employing VECM, we find that repression policies negatively affect financial development, meaning that controlling the financial sector counteracts financial progress. Following the results, we recommend some policies. To accelerate financial progress, policymakers need to rethink on these restrictive policy instruments. For emerging nations like Bangladesh, this paper offers the first empirical data on the connection between financial repression and financial development.
Open Access: Yes
Financial technology and environmental, social and governance in sustainable finance: a bibliometric and thematic content analysis
Publication Name: Discover Sustainability
Publication Date: 2025-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
The integration of Environmental, Social, and Governance (ESG) principles with Financial Technology (Fintech) has emerged as a pivotal mechanism for advancing sustainable finance. This study investigates the interplay between ESG and Fintech through bibliometric and thematic content analysis to uncover key research trends, thematic clusters, and existing knowledge gaps in this dynamic field. The research problem focuses on how FinTech innovations can support ESG-driven initiatives such as corporate social responsibility (CSR), financial inclusion, and sustainable development while addressing challenges like performance metrics and governance issues. By mapping the research landscape, the study identifies significant contributions from scholars, notably in China and the USA, and explores prominent themes, including the role of Fintech in ESG disclosures, corporate governance, and sustainability. Emerging technologies like AI and blockchain are also highlighted for their impact on ESG reporting. The findings reveal exponential academic interest in this domain but underscore critical industrial challenges, such as the absence of standardized ESG metrics and the limited application of Fintech in addressing sustainability issues. The study concludes by offering future research directions aimed at bridging these gaps and emphasizing the transformative potential of Fintech in driving sustainability across the financial sector.
Open Access: Yes
Digital divide and digitalization in Europe: A bibliometric analysis
Publication Name: Equilibrium Quarterly Journal of Economics and Economic Policy
Publication Date: 2024-06-30
Volume: 19
Issue: 2
Page Range: 463-520
Description:
Research background:Digitalization and the associated digital divide are crucial issues impacting socio-economic development globally. Extensive research has examined digitalization and the digital divide in EU countries, but there is a lack of understanding regarding comparisons with studies conducted in Western Balkan countries. This study investigates digitalization trends in research from the past five years in both regions, focusing on efforts and factors contributing to the digital gap. Purpose of the article: The study analyzes research on digitalization from 2018 to 2023 in the EU and Western Balkans. It explores factors causing the digital divide and efforts in digitalization, aiming to guide future research and policy for digital inclusion and sustainable development. Methods: The study employs a meticulous data selection process, choosing Scopus as the database for its extensive coverage of diverse journals. A total of 1119 articles from EU countries and 277 from Western Balkan countries are selected for bibliometric analysis, adhering to PRISMA guidelines. Findings & value added: The research reveals a growing interest in digitalization-related issues, demonstrating the multidisciplinary nature of ongoing research. It points out the distribution of publications on digitalization in the EU and Western Balkans countries. The EU focuses on digital technologies, economic growth, and sustainability, while Western Balkan countries focus on COVID-19 impact and digitalization in education and business. The research compares digitalization efforts in the EU and Western Balkan countries presented in the literature, pointing to new dimensions of the digital divide studies. It discusses how socio-economic contexts affect digital transformation and stresses the need for tailored policy approaches for digital inclusivity. These insights are of great importance for policymakers, researchers, and practitioners working towards global digital development and bridging the digital divide. The study lays the groundwork for future research and policy considerations, considering limitations like potential bias in databases and search criteria.
Open Access: Yes
DOI: 10.24136/eq.2899
FinTech credit using CBDC: Transformation of lending market
Publication Name: Exploring Central Bank Digital Currencies Concepts Frameworks Models and Challenges
Publication Date: 2024-03-07
Volume: Unknown
Issue: Unknown
Page Range: 158-168
Description:
The advancement of financial technology and the development of central bank digital currency (CBDC) are revolutionizing the lending market. FinTech credit and CBDC have disrupted traditional lending practices, providing new channels of financing for borrowers and increasing access to credit. This chapter explores the transformation in the lending market due to the advent of FinTech credit and CBDC. It analyses the impact of these developments on the lending market, borrowers, and financial institutions. The chapter also evaluates the challenges and opportunities presented by these innovations and the implications for regulators. Financial technology (FinTech) credit has rapidly emerged as a disruptive force in the lending market, and the use of central bank digital currency (CBDC) has the potential to further transform the lending market. This chapter aims to examine the potential impact of FinTech credit using CBDC on the lending market, analyse the benefits and challenges of this model, and provide recommendations for policymakers and market participants to foster its adoption.
Open Access: Yes
Machine Learning and Artificial Intelligence Method for FinTech Credit Scoring and Risk Management: A Systematic Literature Review
Publication Name: International Journal of Business Analytics
Publication Date: 2024-01-01
Volume: 11
Issue: 1
Page Range: Unknown
Description:
The ever-changing landscape of financial technology has undergone significant changes owing to advancements in machine learning, artificial intelligence, blockchains, and digitalization. These changes have had a profound impact on the provision of financial services, specifically, credit scoring and lending. This study examines the intersection of financial technology, artificial intelligence, machine learning, blockchain, and digitalization in the context of credit services with a focus on credit scoring and lending. This study addressed three main research questions: The research followed a comprehensive methodology, considering factors such as population, intervention, comparison, outcomes, and setting to ensure that collected data aligns with research objectives. The research questions were structured using the PICOS framework, and the PRISMA model was used for the systematic review and study selection. The publications analysed covered a wide range of datasets and methodologies.
Open Access: Yes
DOI: 10.4018/IJBAN.347504
The impact of unpredictable resource prices and equity volatility in advanced and emerging economies: An econometric and machine learning approach
Publication Name: Resources Policy
Publication Date: 2023-01-01
Volume: 80
Issue: Unknown
Page Range: Unknown
Description:
Global stock markets are incredibly unpredictable. Resource prices have a significant market impact on varying securities. With the use of cutting-edge technology like artificial intelligence, analysts and researchers are employing various machine learning techniques and econometrics methodologies to anticipate stock price trends in order to better comprehend stock market volatility. Volatility is the degree of variation in a time sequence of market rates. Stock market equity returns depend on the business output where the investor has trust in high and low equity. This research explores the interaction between industrialized and developing economies' market volatility relationships between 2000 and 2020 as well as the aforementioned impacts taking place on developing financial prudence worldwide. The aim of the study is to integrate an appropriate GARCH framework to estimate the uncertainty dependent on market conditions in the European Union, the Pacific, South America, Latin America, East Asia, West Asia and South Asia stock return indices. The Generalized Auto-Regressive Conditional Heteroscedasticity method is used for analyzing the effect of updates from the USA that influences the returns of S&P 500 globally as well as European Union, Pacific, South American, Latin American, East Asian, West Asian and South Asian indices returns. For capital markets of the world, there is a significant gap in equity return uncertainty. Such results have major effects on investors looking to diversify their portfolios. For international and domestic institutional shareholders, this paper is significant. The impact of international institutional investors' investments and effects of the growth of the equity market return may be omitted as the analysis is restricted exclusively to the European Union, the Pacific, South America, Latin America, East Asia, West Asia, and South Asia.
Open Access: Yes
Evaluating the Return Volatility of Cryptocurrency Market: An Econometrics Modelling Method
Publication Name: Acta Polytechnica Hungarica
Publication Date: 2022-01-01
Volume: 19
Issue: 5
Page Range: 107-126
Description:
Cryptocurrency is the blockchain financial technology used for transactions in financial institutions and exchanges. Bitcoin has attracted much coverage from investors and commentators as it represents the maximum market capitalization on a crypto-currency exchange. The study aims to determine the correlation between the daily log–returns and to understand the tendencies in the cryptocurrency market instability of Bitcoin, Litecoin, XRP, Nxt, Dogecoin, Vertcoin, DigiByte, DASH, Counterparty, and MonaCoin. The correlation among the selected cryptocurrencies exists in the study. The analysis is focused primarily upon reference information from the preserved servers of cryptocurrency websites and finance.yahoo.com. This research assesses regular details on the Logarithmic return of Bitcoin, Litecoin, XRP, Nxt, Dogecoin, Vertcoin, DigiByte, DASH, Counterparty, and MonaCoin for a timeframe spanning from October 01st, 2014, to April 30th, 2020. From 131 cryptocurrencies, we considered only 10 Cryptocurrencies due to the availability of data after October 2014. Where there was insufficient information, there were average results determined from preceding and succeeding data. Findings demonstrate that there is GARCH modelling of cryptocurrencies against Bitcoin. Litecoin, XRP, Nxt, Dogecoin, Vertcoin, DigiByte, DASH, Counterparty, and MonaCoin; variability values throughout the duration had a significant effect on the updates from Bitcoin returns. We believe that it helps create information and resources that are valuable to practitioners and scholars who research and form cryptocurrency markets in the future.
Open Access: Yes
Blockchain and sustainable finance as enablers of regenerative finance: a bibliometric and thematic review
Publication Name: Discover Sustainability
Publication Date: 2025-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
Blockchain technology, sustainable finance, and regenerative finance are increasingly intersecting as vital research frontiers in addressing global environmental and social challenges. This study offers a comprehensive bibliometric and thematic analysis of the scholarly landscape at this intersection, aiming to illuminate how these domains are evolving and converging. Drawing on a curated dataset of 78 peer-reviewed articles sourced from Scopus and Web of Science, the research integrates quantitative bibliometric mapping with qualitative thematic insights to capture both the structural and conceptual dimensions of the field. The analysis employs co-word mapping to explore patterns of keyword co-occurrence and identify key intellectual linkages, thematic evolution analysis to track the development of major research themes over time, and factorial correspondence analysis to visualize the relationships among concepts within a multidimensional space. Findings reveal a rapidly growing and increasingly interdisciplinary body of work, with blockchain and sustainable finance serving as central pillars, while regenerative finance, though still emerging, shows strong conceptual potential. Notably, the appearance of AI-related themes suggests a nascent yet promising integration of intelligent systems with sustainability-focused finance. Together, these insights offer a nuanced understanding of the field’s current dynamics and lay the groundwork for future research on regenerative, technology-enabled financial systems.
Open Access: Yes
Sentiment Analysis of Marketplace Lending Platforms: A Study Based on Natural Language Processing
Publication Name: International Journal of Business Analytics
Publication Date: 2025-01-01
Volume: 12
Issue: 1
Page Range: Unknown
Description:
This study explores the link between user sentiment and credit risk on FinTech lending platforms using sentiment analysis techniques like Latent Dirichlet Allocation (LDA) and the Liu Hu method. Analyzing data from 2020 to 2023, findings reveal Kiva leads with 91.16% positive feedback and a 4.7-star rating but fewer reviews (617). LendingClub, with 1.58K reviews, has mixed sentiment (56.08% positive, 39.99% negative) and a lower rating (3.3 stars). Plenti achieves 58.33% positive sentiment but lower coherence, while Mintos balances sentiment (66.69% positive) with the largest review base (100K+). Results show platforms with higher positive sentiment and topic coherence mitigate credit risk more effectively, underscoring the value of user feedback in optimizing marketplace lending. The study offers actionable insights for FinTech stakeholders to improve app performance and user-centric financial solutions through effective sentiment analysis.
Open Access: Yes
DOI: 10.4018/IJBAN.393942
Decentralized finance and sustainability analysis of global research patterns and emerging themes
Publication Name: Discover Sustainability
Publication Date: 2026-12-01
Volume: 7
Issue: 1
Page Range: Unknown
Description:
Decentralized finance (DeFi) is rapidly transforming financial systems, yet its environmental, social, and economic sustainability implications remain underexplored. To address this gap, we conducted a structured review of peer-reviewed literature published between 2022 and 2025, drawing on 239 records retrieved from Scopus and Web of Science and screened through the PRISMA 2020 protocol in Covidence. The review combined bibliometric analysis, thematic mapping, and a systematic review to synthesize patterns, clusters, and critical insights. Bibliometric results show a sharp post-2023 rise in outputs, with China leading in publication volume and Switzerland achieving the highest citation impact, although collaboration networks remain fragmented and weakly connected. Thematic analysis reveals three dominant clusters: blockchain-driven financial innovation, AI and fintech applications for sustainability, and green economy transitions, highlighting DeFi’s dual role as a driver of transparency and inclusion but also a source of energy inefficiency and systemic risk. The systematic review further identifies regulatory gaps, particularly around Maximal Extractable Value (MEV), and emphasizes the need for energy-efficient consensus mechanisms, standardized ESG metrics for tokenized assets, and inclusive platform designs to bridge digital divides. By aligning DeFi’s disruptive potential with sustainability objectives, the study proposes hybrid governance models and interdisciplinary collaboration to foster a resilient, equitable, and low-carbon financial ecosystem, underscoring the urgency of balancing technological innovation with planetary boundaries to realize DeFi’s promise as a catalyst for sustainable development.
Open Access: Yes
Evaluating AI-driven credit scoring models versus traditional statistical techniques
Publication Name: Discover Artificial Intelligence
Publication Date: 2026-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
This study evaluates AI credit-scoring models against standard statistical models using a real-life data set with 1000 loan applications. The main research question is about whether machine-learning methods are more valuable in terms of predictive accuracy, interpretability and stability to changes in the process of macroeconomic deterioration as compared to traditional methods. The four model variants were developed: the logistic regression and decision tree as examples of the classical ones, and XGBoost gradient-boosting ensemble and multilayer perceptron neural networks as examples of AI-based alternatives. The methodological engine was R (v4.3.1), which established a 70:30 train-test strata union, inner cross-validation and harmony spatial search to tune the hyperparameter. The findings show that XGBoost produces an optimal balanced accuracy cumulative gain curve (cumulative gain, CGC: 0.89; area under the receiver operating characteristic, AUC: 0.89) that is trumped by the neural network (CGC: 0.87; AUC: 0.87) and succeeded by the logistic regression (CGC: 0.76; AUC: 0.76). The SHAP analysis shows that the amount of credit, duration of loan, and age are central predictors. During stress-test simulation, XGBoost is stable in making predictions (AUC = 0.83) compared to a severe decline in logistic-regression results (AUC = 0.68). The results therefore justify the claim that the state-of-the-art AI tools have better predictive potential and robust interpretability, thus rising as viable alternatives to the systems in vogue in modern finance organisations.
Open Access: Yes
TRANSFORMATIONAL LEADERSHIP STRATEGY AS A DRIVING FORCE TO ENGAGEMENT OF WORKERS: EMPIRICAL STUDY IN THE BANKING SYSTEM
Publication Name: Corporate and Business Strategy Review
Publication Date: 2026-01-01
Volume: 7
Issue: 1
Page Range: 250-257
Description:
This paper explores the hypothesis that transformational leadership strategy (TLS) is related to employee engagement in the branch-banking setting of a developing economy. Based on a quantitative survey of bank managers and employees (matched pairs; n = 61) and the available measures, reliability and validity have been measured, and the hypothesized TLS-engagement path has been tested through regression. Although there are recent studies and reviews that usually indicate positive links between TLS and engagement (e.g., meta-and narrative syntheses) (Bakker et al., 2023; Grah et al., 2024), our findings indicate a weak, statistically insignificant effect. The result indicates that leadership can be less motivated in banking due to contextual contingencies, including reward systems, legacy processes, or culture. We present hypotheses to apply to the job demands-resources (JD-R) theory, and in this case, leadership as a job resource might not be effective without other resources. We provide some steps that banks should take to balance leadership development with job redesign and incentives. We end with restrictions (convenience sampling, cross-sectional design) and future research (longitudinal and multi-level design and studies in other industries). Such insights provide a valuable boundary condition to other existing studies of TLS-engagement in other industries and different regions (Decuypere & Schaufeli, 2021; Bakker et al., 2023; Grah et al., 2024).
Open Access: Yes
Examining the Role of Accountant’s Knowledge of Forensic Accounting, Corporate Governance Policies and Fraud Awareness Training in Preventing Fraud: A Survey of Indian Corporates
Publication Name: Journal of Risk and Financial Management
Publication Date: 2026-02-01
Volume: 19
Issue: 2
Page Range: Unknown
Description:
Corporate fraud remains a persistent problem that highlights the need for improved internal control and governance. Research on corporate governance (CG) and forensic accounting (FA) has been largely performed as separate studies. Little has been done to look at how accountants’ knowledge and the specific training of accountants in fraud awareness for their company’s leaders affect preventing fraud (FP). The study surveyed 150 accountants in India from April 2023 to May 2024. The results are based on Chi-Square testing and binary logistic regression. The study investigated how companies in India incorporate CG policy understanding and FG use for KMP and boards and how these factors affect FP. The findings indicate that understanding CG, using FA, and having specific training on fraud awareness for KMPs and boards of directors are all significant factors in reducing the occurrence of fraud. In addition, general employee training has no impact on FP. The theories of agency, stakeholder, and fraud triangle were combined to create one model to provide guidance to both organizations and regulators on how to institutionalize FG and to improve transparency in governance.
Open Access: Yes
DOI: 10.3390/jrfm19020118
Entrepreneurial intentions among Generation Z university students: a theory of planned behavior perspective
Publication Name: Cogent Business and Management
Publication Date: 2026-01-01
Volume: 13
Issue: 1
Page Range: Unknown
Description:
Entrepreneurial intentions among Gen Z university students in Bangladesh remain underexplored, particularly regarding the influence of education, access to finance and socioeconomic status. This study aims to investigate how these factors shape entrepreneurial intentions within a developing economy context. A quantitative research design was employed, using stratified random sampling of university students in Dhaka, and data were collected through structured questionnaires. Analysis was conducted using covariance-based structural equation modeling (CB-SEM) with AMOS, which showed that all structural paths were statistically significant at p < 0.05. The study found a significant positive association between education and entrepreneurial intentions among Gen Z students in Bangladesh. Access to resources, particularly digital tools and crowdfunding platforms, was also significantly and positively associated with entrepreneurial intentions. Socioeconomic status demonstrated a further significant positive relationship with entrepreneurial intentions. Access to digital resources emerged as a strong direct predictor of entrepreneurial intention. These findings extend the Theory of Planned Behavior (TPB) within a developing economy context. In conclusion, education, resource access and socioeconomic status are key positive determinants of entrepreneurial intentions. Strengthening digital infrastructure and entrepreneurship education may enhance youth entrepreneurial outcomes. Broader studies beyond Dhaka are recommended to improve generalizability.
Open Access: Yes
Barriers and Socio-Economic Drivers of Renewable Energy Adoption Among Manufacturing SMEs: A Structural Equation Modeling Approach
Publication Name: Sustainability Switzerland
Publication Date: 2026-04-01
Volume: 18
Issue: 8
Page Range: Unknown
Description:
Background: Small- and medium-sized enterprises (SMEs) constitute a large portion of the industrial energy demand in the emerging economies, but their shift to renewable energy is not well comprehended at the firm level. Bangladesh is a special case, since the country has adopted national commitments to Sustainable Development Goal 7 on clean energy, but the uptake of renewable energy by SMEs remains minimal due to complex socio-economic factors. Most of the literature has concentrated on household access to energy or national policy models, leaving a gap in empirically validated models of firm-level adoption in the manufacturing sector. Method: Based on the diffusion of innovation theory, institutional theory, and the resource-based view, this research paper formulates and empirically verifies a combined socio-economic model of renewable energy adoption. Partial least squares structural equation modeling (PLS-SEM) was used to analyze a cross-sectional survey of 426 owners and managers of manufacturing SMEs in Bangladesh’s textile and food processing sub-sectors. Findings: Four out of five hypothesized direct relationships were supported. The most important drivers were environmental orientation (β = 0.467, p < 0.001, f2 = 0.413), market competitiveness (β = 0.287, p < 0.001, f2 = 0.413), policy and institutional factors (β = 0.211, p < 0.001, f2 = 0.413), and access to finance (β = 0.096, p = 0.004). Perceptions of cost did not become significant (β= −0.036, p = 0.279). Top management support significantly and negatively moderated the relationship between environmental orientation and adoption (β = −0.093, p = 0.003), possibly because it moderates the substitution mechanism in SME decision-making, which is highly centralized. The model accounted for 64.5% of the variation in renewable energy adoption (R2 = 0.645). Conclusion: The results show that attitudinal and institutional factors tend to be more important than financial barriers in determining SMEs’ energy transitions. Environmental consciousness, market incentives, and streamlined institutional access should be the focus of policy interventions to hasten inclusive low-carbon transitions in emerging manufacturing economies.
Open Access: Yes
DOI: 10.3390/su18083809
