Search Results (133)

The exponential growth of e-commerce and advancements in financial technology have escalated credit card fraud into a major threat, resulting in billions of dollars in global losses annually. This necessitates the development of sophisticated fraud detection systems capable of real-time anomaly interception to safeguard financial activities. While federated learning frameworks have been employed to address data privacy concerns in financial applications, existing approaches often fail to account for the heterogeneity in data distributions across different institutions, such as banks, which hinders collaborative model training. In response, this paper introduces the FED-SPFD model, an innovative federated learning framework designed to detect credit card fraud amidst multi-party heterogeneous data. The model employs a share–private segmentation approach to distinguish shared from private data attributes, facilitating unified feature representation learning. It aligns disparate shared features through local sufficient statistics, thus preventing privacy breaches without directly sharing sample data. Additionally, the integration of a “private autoencoder + standard Gaussian alignment” mechanism stabilizes the training process by ensuring consistent private feature distributions. The efficacy of the FED-SPFD model is demonstrated using a real-world dataset from Kaggle, showcasing significant improvements in recall rate compared to state-of-the-art methodologies. Comprehensive evaluation through ablation studies further validates the framework’s robust contributions to accurate and privacy-preserving fraud detection. Practically, this work offers policymakers a compliant cross-institutional risk collaboration paradigm and provides financial institutions with a privacy-protective solution to enhance fraud detection without data sharing violations. Full article

In the context of global sustainable development strategies and the rise of intelligent construction, it has become increasingly urgent for universities to adapt construction management curricula to meet the demands of this new era. However, prior education-reform-based studies rarely offer a systematic, educator-centered prioritization of knowledge areas, limiting actionable guidance for course sequencing and credit-hour allocation. To address this gap, this study identifies eight essential knowledge categories for construction management education through a comprehensive literature review and a survey of faculty members with strong theoretical and practical experience. An improved Analytic Hierarchy Process (AHP) model, weighted by the Consistency Ratio (CR), is applied to prioritize these areas. Results show that Fundamentals of Construction (18.50%), BIM (18.08%), and AI and Big Data (17.07%) received the highest importance values. These findings emphasize the need for curriculum reorientation to align with intelligent construction. This study contributes to modernizing construction management education and offers practical insights for curriculum development, ensuring alignment with industry trends and technological advancements. Full article

We present a novel deep generative semi-supervised framework for credit card fraud detection, formulated as a time series classification task. As financial transaction data streams grow in scale and complexity, traditional methods often require large labeled datasets and struggle with time series of irregular sampling frequencies and varying sequence lengths. To address these challenges, we extend conditional Generative Adversarial Networks (GANs) for targeted data augmentation, integrate Bayesian inference to obtain predictive distributions and quantify uncertainty, and leverage log-signatures for robust feature encoding of transaction histories. We propose a composite Wasserstein distance-based loss to align generated and real unlabeled samples while simultaneously maximizing classification accuracy on labeled data. Our approach is evaluated on the BankSim dataset, a widely used simulator for credit card transaction data, under varying proportions of labeled samples, demonstrating consistent improvements over benchmarks in both global statistical and domain-specific metrics. These findings highlight the effectiveness of GAN-driven semi-supervised learning with log-signatures for irregularly sampled time series and emphasize the importance of uncertainty-aware predictions. Full article

This study investigates the multifractal characteristics and nonlinear cross-correlations between two major carbon credit indices—S&P Global Carbon Index and EEX Global Carbon Index—and key global financial assets: the Euro/US Dollar exchange rate, Dow Jones Industrial Average, gold, Western Texas Intermediate, and Bitcoin. Using daily data from August 2020 to June 2025, we apply the Asymmetric Multifractal Detrended Cross-Correlation Analysis framework to examine the strength, asymmetry, and persistence of interdependencies across varying fluctuation magnitudes. Our findings reveal consistent multifractality in all asset pairs, with stronger multifractal spectra observed in those linked to Bitcoin and Western Texas Intermediate Crude Oil price. The analysis of generalized Hurst exponents indicates higher persistence for small fluctuations and antipersistent behavior for large fluctuations, particularly in pairs involving the S&P Global Carbon Index. We also detect significant asymmetry in the cross-correlations, especially under bearish trends in Bitcoin and Western Texas Intermediate. Surrogate data tests confirm that multifractality largely stems from fat-tailed distributions and temporal correlations, with genuine multifractality identified in the S&P Global Carbon Index–Dow Jones Industrial average pair. These results highlight the complex and nonlinear dynamics governing carbon markets, offering critical insights for investors, policymakers, and regulators navigating the intersection of environmental and financial systems. Full article

Accurate forecasting of carbon credit prices is increasingly vital for the effective functioning of forest carbon markets, which play a growing role in global climate mitigation strategies. Against this backdrop, the present study conducts a systematic literature review to evaluate the state of carbon price forecasting methodologies, with particular emphasis on their applicability to forest-based carbon credits. The review highlights the predominance of machine learning (ML) and hybrid modeling approaches, which demonstrate enhanced predictive capabilities relative to conventional econometric techniques, particularly in capturing nonlinear dynamics and integrating heterogeneous data sources. However, their predictive power is limited by data scarcity, market opacity, and regulatory volatility. These issues are particularly severe in voluntary forest credit markets. The review identifies a critical research gap. Few studies explicitly model the behavior of forest credit prices. The findings suggest that future research should prioritize the development of policy-sensitive, scenario-based models that incorporate ecological, economic, and regulatory dimensions. While the majority of studies concentrate on compliance carbon markets, the methodological insights and forecasting approaches reviewed are highly relevant for the evolving forest carbon sector, nature-based mitigation strategies, and climate solutions. It also offers guidance for creating more transparent and robust forecasting tools in the forest carbon sector. Full article

The 2015 Paris Agreement paved the way for the carbon trade economy, which has since evolved but has not attained a substantial magnitude. While carbon credit exchange is a critical mechanism for achieving global climate targets, it faces persistent challenges related to transparency, double-counting, and verification. This paper examines how Distributed Ledger Technology (DLT) can address these limitations by providing immutable transaction records, automated verification through digitally encoded smart contracts, and increased market efficiency. To assess DLT’s strategic potential for leveraging the carbon markets and, more explicitly, whether its implementation can reduce transaction costs and enhance market integrity, three alternative approaches that apply DLT for carbon trading were taken as case studies. By comparing key elements in these DLT-based carbon credit platforms, it is elucidated that these proposed frameworks may be developed for a scalable global platform. The integration of existing compliance markets in the EU (case study 1), Australia (case study 2), and China (case study 3) can act as a standard for a global carbon trade establishment. The findings from these case studies suggest that while DLT offers a promising path toward more sustainable carbon markets, regulatory harmonization, standardization, and data transfer across platforms remain significant challenges. Full article

This study investigates how bank mergers and acquisitions (M&As) reshape the monitoring architecture of syndicated loans and, by extension, borrowers’ financing conditions. Using a global panel of 20,299 syndicated loan contracts, originating in 43 countries between 1982 and 2020, we link LPC DealScan data to Securities Data Company M&A records to trace each loan’s lead arrangers before and after consolidation events. Fixed-effects regressions, enriched with borrower- and loan-level controls, reveal three key patterns. First, post-merger loans exhibit significantly more concentrated syndicates: the Herfindahl–Hirschman Index rises by roughly 130 points and lead arrangers retain an additional 0.8–1.1 percentage points of the loan, consistent with heightened monitoring incentives. Second, these effects are amplified when information asymmetry is acute, i.e., for opaque or unrated firms, supporting moral hazard theory predictions that lenders internalize greater risk by holding larger stakes. Third, relational capital tempers the impact of consolidation: borrowers with repeated pre-merger relationships face smaller increases in syndicate concentration, while switchers experience the most significant jumps. Robustness checks using lead arranger market share, alternative spread measures, and lag structures confirm the findings. Overall, the results suggest that bank consolidation strengthens lead arrangers’ incentives to monitor but simultaneously reduces risk-sharing among participant lenders. For borrowers, the net effect is a trade-off between potentially tighter oversight and reduced syndicate diversification, with the balance hinging on transparency and prior ties to the lender. These insights refine our understanding of how structural shifts in the banking sector cascade into corporate credit markets and should inform both antitrust assessments and borrower funding strategies. Full article

Banks in the United States face persistent challenges from non-performing loans (NPLs), despite conducting thorough client evaluations before issuing loans. To mitigate the impact of NPLs and support both local and global growth, banks must adopt effective risk management strategies. This study investigates the effect of NPLs on bank growth and the moderating of bank size and Capital Adequacy Ratio (CAR) through the lens of the Resource-Based View (RBV) theory. A sample of 253 banks listed on the New York Stock Exchange from 2006 to 2023 was selected using specific inclusion criteria from the Thomson Reuters Eikon DataStream. To address cross-sectional dependence and endogeneity, advanced estimation techniques—Feasible Generalized Least Squares (FGLS), Driscoll and Kraay standard errors, and the Generalized Method of Moments (GMM)—were employed. The results show that NPLs have a significant negative impact on banks’ asset and income growth. Furthermore, bank size and capital adequacy ratio (CAR) negatively and significantly moderate this relationship. These findings underscore the need for banks to enhance credit risk management by strengthening loan approval processes and leveraging advanced analytics to assess borrower risk more accurately. Full article

This study explores the impact of credit spreads—defined as the difference between corporate bond yields and matched government bond yields—and macro-financial conditions on Japanese firms’ decision-making regarding whether to issue corporate bonds in domestic or international markets. Using firm-level panel data from 2010 to 2019, we employ fixed-effects regressions to identify the determinants of credit spreads and assess their influence on issuance location. The results suggest that firms strategically opt for foreign markets when anticipating narrower spreads, despite the typically higher borrowing costs associated with overseas issuance. Sensitivity to credit spreads systematically varies with issuer characteristics—such as leverage and credit ratings—and market elements—including the United States volatility and stock performance. Interaction models further demonstrate that market selection dynamically responds to pricing signals and uncertainty. By connecting credit spread formation to venue choice, this study provides a new perspective on cross-border financing in segmented capital markets. These findings offer theoretical insights and practical implications for understanding how firms adapt their debt strategies in response to global financial conditions. Full article

Although Ethiopia has an enormous agroecological potential for beekeeping, only 10% of it is realized. As its conventional smallholder production calls for improvement in market relationships, this paper aims at an in-depth analysis of the honey value chain, value share distribution, and leverages for improvement. Questionnaires, focus group discussions, and key informant interviews were used to collect data. Descriptive statistics, value chain mapping, and margin analysis were used for analysis. The main honey value chain actors were input suppliers, producers (beekeepers), collectors, wholesalers, processors, cooperatives, unions, retailers, and consumers. Agricultural offices, research centers, trade and market development offices, financial institutions, and NGOs are major supporters. The value share of beekeepers using traditional hives is still low, while the largest share goes to improved hive users and wholesalers, respectively. Weak market linkages, high costs and shortage of modern equipment, limited access to credit, lack of legal frameworks and standardized laboratories, absconding, pest infestation, and unsafe use of agrochemicals were the major challenges. Nevertheless, attractive investment policy, global market demand, low capital requirements, and support from NGOs were key opportunities. Improving access to better market, finance and modern inputs, capacity building, legal reform, and a standardized laboratory would help to support the honey value chain and its contribution. Full article

In the Eastern Cape Province of South Africa, rural women agripreneurs encounter ongoing structural challenges in accessing formal finance, securing land rights, and gaining leadership roles, despite their vital contribution to agriculture and food security. This research combines a thematic review of secondary sources from 2018 to 2024 with an embedded case study based on primary qualitative data with women involved in the Citrus Growers Association–Grower Development Company (CGA–GDC) public–private partnership. This dual approach connects local, real-world entrepreneurial experiences with global financial inclusion initiatives, especially the G20 Women’s Empowerment Principles and the G20 Development Agenda. The findings highlight a consistent gap between policy and practice: while frameworks at both national and international levels advocate for women’s financial inclusion, actual implementation in rural agribusiness often neglects gender differences. Women’s engagement is limited by insecure land rights, restricted access to formal credit, male-controlled cooperative management, and insufficient gender-specific data monitoring. Drawing comparative insights from Kenya, India, and West Africa, the study proposes seven interconnected policy suggestions, such as establishing gender-disaggregated data systems, expanding women-led cooperatives, reforming land tenure laws, including entrepreneurial financial literacy in capacity-building programmes, and utilising gender-sensitive digital finance solutions. By connecting grassroots empirical evidence with global policy discussions, this study aims to contribute to academic debates and practical efforts to develop gender-responsive financial ecosystems, thereby boosting women’s economic independence, entrepreneurial activity, and rural progress in South Africa and similar contexts in the Global South. Full article

Farmers’ land transfer practices optimize the allocation of agricultural resources by transferring them to more efficient operators. This enhances agricultural productivity and advances rural revitalization. However, due to the lack of financial institution outlets in rural areas, the availability of financial services in rural areas is limited, which in turn hinders the transfer of rural land. This study examines the impact of digital financial inclusion, characterized by the deep integration of internet technology and financial services, on farmers’ land transfer behavior in China. The study uses data from the China Family Panel Studies (2012–2022) and provincial digital financial inclusion data. The results show that digital financial inclusion significantly promotes rural land transfer-out. The mechanisms reveal two pathways: (1) digital financial inclusion expands non-agricultural entrepreneurship by easing credit constraints and reducing reliance on land livelihoods; (2) it increases participation in commercial insurance, mitigating risks of land abandonment. Heterogeneity analysis reveals stronger effects in eastern China and among educated households. Theoretically, the study identifies the dual role of financial technology in reshaping rural land markets through credit access and risk management. Practically, it reveals how DFI influences land transfer behavior, providing a basis for the government to formulate policies that combine the two, ultimately enhancing the production capacity, operational efficiency, and market competitiveness of smallholder farmers. The findings offer global insights for developing countries that are leveraging digital finance to activate rural land markets and achieve digital financial inclusion. Full article

The Amazon rainforest, a vital global carbon sink, is experiencing extensive forest loss due to environmental pressures, particularly from livestock production. While research on this topic has grown, a comprehensive synthesis is needed to map the intellectual landscape of this critical field and inform actionable policies. Unlike a systematic review, which synthesizes findings qualitatively, this analysis focuses on a quantitative overview of research trends, key authors, and collaborative networks regarding greenhouse gas emissions from livestock-driven deforestation in the Amazon from 2004 to 2024. Additionally, the study makes a thematic synthesis of reviewed literature providing overview on emissions, mitigation, and biodiversity impacts. The review, based on data from Scopus and Web of Science processed through Bibliometrix and VOSviewer software, reveals a growing and increasingly collaborative field, with research output showing significant growth post-2010, dominated by institutions in Brazil and the United States, with a conceptual focus that has shifted from basic deforestation metrics to sophisticated analyses of mitigation strategies and policy impacts. The findings highlight recurrent deforestation drivers, including export-oriented agriculture and weak land tenure, and demonstrate the effectiveness of specific mitigation options. Key mitigation strategies identified include silvopastoral systems with more than 30% tree cover, rotational grazing, and targeted pasture restoration, which can halve emissions within 5–7 years when combined with credit incentives and secure land tenure. The review underscores the evolution of research toward more policy-relevant and interdisciplinary approaches, but also highlights the need for more empirical validation and collaborative efforts to translate these findings into scalable climate solutions. Full article

This paper examines the impact of agent banking activities, a recent FinTech development, influencing the profitability and financial outcomes of commercial banks operating in Bangladesh, as agent banking has been receiving significant global attention due to its technology-driven approach, cost-effectiveness and easy accessibility, and broader coverage of the unbanked population. Through the application of penal data regression methods, the study estimates a random-effect model using panel data comprising quarterly observations from nine Bangladeshi commercial banks that maintained uninterrupted agent banking activities, covering both deposit mobilization and lending during the period from 2018Q1 to 2024Q4. The empirical findings indicate that credit disbursement by agent banks has a positive and statistically significant impact on bank profitability measures, return on assets (ROA), and return on equity (ROE). Similarly, the expansion of agent banking outlets positively and significantly influences ROA. Therefore, an appropriate agent banking policy aimed at increasing agent banking outlets using digital platforms based on FinTech is vital for ensuring positive growth in credit disbursement to achieve improved financial outcomes for the banking sector in a developing country like Bangladesh. Full article

Light Detection and Ranging (LiDAR) provides three-dimensional information that can be used to extract tree parameter measurements such as height (H), canopy volume (CV), canopy diameter (CD), canopy area (CA), and tree stand density. LiDAR data does not directly give diameter at breast height (DBH), an important input into allometric equations to estimate biomass. The main objective of this study is to estimate tree DBH using existing allometric models. Specifically, it compares three global DBH pantropical models to calculate DBH and to estimate the aboveground biomass (AGB) of the Lake Broadwater Forest located in Southeast (SE) Queensland, Australia. LiDAR data collected in mid-2022 was used to test these models, with field validation data collected at the beginning of 2024. The three DBH estimation models—the Jucker model, Gonzalez-Benecke model 1, and Gonzalez-Benecke model 2—all used tree H, and the Jucker and Gonzalez-Benecke model 2 additionally used CD and CA, respectively. Model performance was assessed using five statistical metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), percentage bias (MBias), and the coefficient of determination (R²). The Jucker model was the best-performing model, followed by Gonzalez-Benecke model 2 and Gonzalez-Benecke model 1. The Jucker model had an RMSE of 8.7 cm, an MAE of −13.54 cm, an MAPE of 7%, an MBias of 13.73 cm, and an R² of 0.9005. The Chave AGB model was used to estimate the AGB at the tree, plot, and per hectare levels using the Jucker model-calculated DBH and the field-measured DBH. AGB was used to estimate total biomass, dry weight, carbon (C), and carbon dioxide (CO₂) sequestered per hectare. The Lake Broadwater Forest was estimated to have an AGB of 161.5 Mg/ha in 2022, a Total C of 65.6 Mg/ha, and a CO₂ sequestered of 240.7 Mg/ha in 2022. These findings highlight the substantial carbon storage potential of the Lake Broadwater Forest, reinforcing the opportunity for landholders to participate in the carbon credit systems, which offer financial benefits and enable contributions to carbon mitigation programs, thereby helping to meet national and global carbon reduction targets. Full article