Search Results (69)

Ensuring the quality, reliability, and efficiency of cold chain logistics for thermolabile pharmaceutical products, particularly vaccines, remains a critical challenge in global health supply chains. These biologics require stringent temperature control throughout storage, transport, and distribution to preserve their efficacy. Persistent issues such as maintaining product integrity, accurately forecasting vaccine demand, and fostering trust among stakeholders often result in inefficiencies, waste, and public mistrust. This study proposes an intelligent digital management framework specifically designed for vaccine cold chains, integrating blockchain, the Internet of Things (IoT), and machine learning (ML) to address these challenges in a holistic and sustainable manner. The main innovation of the study lies in combining secure traceability, real-time cold chain monitoring, and predictive decision support within a unified vaccine cold chain management framework rather than treating these functions as isolated technological solutions. Using WHO immunization coverage data and vaccine-related review data, the framework supports vaccine demand forecasting through the Informer model and stakeholder trust assessment through BERT-based sentiment analysis. In the sentiment analysis task, the BERT model achieved ~80% accuracy on dominant sentiment classes, with a weighted F1-score of 0.6974, demonstrating strong performance on imbalanced datasets. By minimizing vaccine spoilage and enabling more accurate demand planning, the system reduces excess production and distribution, thus lowering resource consumption, carbon emissions, and financial waste. Moreover, trust-informed analytics support better alignment of supply with actual community needs, fostering equity and resilience in vaccine distribution. While this framework has been validated through simulations and experimental evaluation, further real-world testing is needed to assess long-term stability and stakeholder adoption. Nonetheless, it provides a scalable and adaptive foundation for advancing sustainability and transparency in pharmaceutical cold chains. Full article

Financial fraud and money laundering continue to challenge financial stability and regulatory oversight, motivating the widespread adoption of machine learning models for transaction monitoring. Although ensemble models such as Random Forest and XGBoost achieve strong predictive performance, their deployment in high-stakes financial environments is constrained by limited interpretability, overconfident predictions, and the absence of principled mechanisms for expressing decision uncertainty. Emerging regulatory expectations increasingly emphasise transparency, accountability, and operational reliability, underscoring the need for evaluation frameworks that extend beyond predictive accuracy. This study proposes the Integrated Transparency and Confidence Framework (ITCF), a deployment-oriented approach that unifies model explainability, statistically valid uncertainty quantification, and operational decision support for fraud detection. ITCF combines instance-level explanations generated via Local Interpretable Model-Agnostic Explanations (LIME) with distribution-free uncertainty estimation using split conformal prediction. The framework incorporates selective explainability, abstention-based routing, and uncertainty-driven triage to support human-in-the-loop review. Using the PaySim dataset of 6,362,620 mobile-money transactions, Random Forest and XGBoost models are evaluated under extreme class imbalance using F1-score, AUC–ROC, and Matthews Correlation Coefficient (MCC). At a target coverage level of 90% ($\alpha =0.1$), both models achieve empirical coverage close to the target level, with XGBoost producing smaller prediction sets and superior recall, MCC, and latency. ITCF provides transaction-level explanations for uncertain cases and specifies an auditable workflow that is intended to support transparency, traceability, and risk-aware human review, thereby enabling defensible human decision-making in regulated environments. Overall, this study illustrates how explainability and uncertainty quantification can be combined in a deployment-oriented evaluation workflow while noting that real-world validation remains a future endeavour. Full article

Through assessing the roles of artificial intelligence (AI), Internet of Things (IoT), and blockchain in augmented finance, a critical synthesis of the literature for addressing the complex financial challenges that accompany climate change is provided. This systematic review synthesizes the existing literature to identify how these technologies may help in the context of sustainable finance. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we reviewed and analyzed 42 peer-reviewed studies published between 2018 and 2025. Our results are applicable in three general areas: (1) increased measurement, reporting, and verification (MRV) of environmental impacts through employing IoT and blockchain to ensure transparency and traceability; (2) better physical and transition risk control using predictive AI modeling; and (3) better environmental, social, and governance (ESG) analysis and detection of greenwashing and risk reduction via alternative data. We highlight the power of these technologies to address problems such as information asymmetry and transparency gaps in impact chains. However, significant challenges such as algorithmic bias, difficulties associated with data governance, and regulatory delays persist. This study addresses this critical gap by synthesizing the evidence into a cohesive overview of the augmented finance landscape, identifying key challenges and priorities for future research. It also proposes a future research agenda with emphasis on impact assessment, algorithmic transparency, and impact on financial stability. Full article

Strategic Mine Planning (SMP) creates the long-term economic baseline for mining operations, yet economic variability necessitates Dynamic Mine Planning (DMP) to rapidly stress-test those financial assumptions. Currently, this capability is hindered by fragmented software ecosystems that require manual data handoffs, slowing iteration and breaking the audit trail between market data and valuation models. While Generative AI affords an opportunity to automate these workflows, its adoption in the mining industry is stalled by concerns over data quality and the risk of uncritical acceptance of automated outputs. Addressing these challenges, this paper describes the Mine Intelligence and Decision Support (MINDS) framework. We present MINDS as a modular reference architecture that uses Large Language Model (LLM) agents to orchestrate the economic evaluation process while maintaining strict engineering oversight. The system integrates a conversational interface with a multi-agent assessment layer that acts as an adversarial review, assessing price assumptions against market intelligence before generating economic valuation scenarios. A proof-of-concept using the Marvin copper benchmark evaluates the framework, demonstrating automated request-to-report orchestration, execution stability with an average debate latency of 10.69 s and a transparent decision audit trail. These findings show that MINDS can systematize economic scenario analysis without sacrificing the governance and verification required for definitive feasibility studies. Full article

This research examines market reactions to financial distress announcements by small privately held Canadian oil firms operating in the upstream sector between 2015 and 2021, employing an event study methodology, with daily spot prices for Brent and WTI crude oil serving as market benchmarks. The sample includes 11 firms that filed for insolvency, giving 99 observations for analysis. Data were collected from the publicly available Haynes Boone repository, ensuring transparency and verifiability. Abnormal returns were computed using market-adjusted returns to control for general market movements, isolating event-specific effects. The findings reveal statistically significant yet modest abnormal returns around the announcement day, indicating a measured market reaction. These results indicate that investors may partially anticipate such events and interpret them as potential restructuring opportunities rather than indicators of sector-wide collapse. The study underscores the importance of transparent disclosure and structured legal frameworks in moderating market volatility during financial distress. While the analysis is confined to short-term effects and small firms, it provides valuable insights into how financial distress in small upstream oil firms influences commodity markets, contributing new evidence to the literature on event studies and financial distress in energy markets, and offers implications for policymakers aiming to enhance market stability. Full article

The correct valuation of collateral supporting real estate loans has always been a key issue for the stability of the credit system. Substandard lending practices and the absence of uniform valuation approaches have historically contributed to the accumulation of non-performing loans. In recent years, several regulatory measures operating at both the European and national level have introduced principles, rules and procedures aimed at standardizing the valuation of properties pledged as collateral for credit exposures. These interventions seek to promote greater transparency, consistency, and prudence in property appraisals, thereby enhancing the soundness and resilience of the financial system. In January 2025, the updated Regulation (EU) 575/2013 came into force, incorporating the Basel III reform (also referred to as Basel 3+ or Basel IV). Among the innovations introduced, the concept of property value (PV) is particularly relevant, a prudential value that excludes expectations of price growth and considers the sustainability of the value over time in relation to the duration of the loan. PV is defined as a derived value with respect to market value (MV), determined by considering the main current and forward-looking risk factors that may arise during the life of the loan, including environmental, social and governance (ESG) risks, the intrinsic characteristics of the property and expectations regarding the economic cycle. This paper proposes a quantitative model for the determination of PV, applied to a practical case involving a residential property located in a medium-sized city in Italy’s Veneto region. The model adopts a deterministic and a probabilistic approach, the latter implemented through Monte Carlo simulation, which is indeed a generalization of the deterministic one. The model links the assessment of PV to the possible evolution of the property’s key parameters and the real estate cycle over the duration of the loan. It was tested under the assumption of a twenty-year mortgage originated in 2025 for the purchase of a residential property in Italy, considering two alternative locations: a suburban area and a city-centre area. The analysis conducted showed a substantially higher MV haircut for the suburban property compared with the central location. This difference reflects the fact that PV is less sensitive to real estate cycle fluctuations in more premium, central locations. Furthermore, the use of Monte Carlo simulation in the probabilistic approach enabled the calibration of the haircut according to a predefined confidence level, confirming the pattern observed in the deterministic framework. The combined evidence strengthens the empirical robustness of the model and highlights the importance of locational and cyclical dynamics in collateral valuation. Full article

This article offers a structural reappraisal of central bank Monetary Policy Transparency (MPT) by explicitly incorporating two dimensions that have long remained peripheral in the literature: Accounting Information Transparency (AIT) and Financial Stability Transparency (FST). Building on a rigorous theoretical foundation, we develop two original transparency dimensions centered on AIT and FST, designed to extend a widely recognized monetary policy transparency index developed in the existing literature. This extension aims to capture, in an integrated manner, the institutional and macroprudential foundations that underpin the credibility, coherence, and effectiveness of modern monetary policy. The empirical analysis relies on a balanced panel of 25 countries over the period 2000–2019 and employs both Ordinary Least Squares (OLS) and the Generalized Method of Moments (GMM) to address potential endogeneity concerns and ensure the structural robustness of the estimations. The results provide strong evidence that both AIT and FST exert a positive, statistically significant, and economically meaningful effect on MPT. These findings substantially enrich the analytical framework of central bank transparency by demonstrating that high-quality financial reporting and transparent macroprudential communication constitute fundamental pillars of central banks’ credibility capital in an increasingly complex and globalized financial environment. Full article

The Earnings Response Coefficient (ERC) has emerged as a pivotal topic in academic literature and financial practice, elucidating the critical relationship between corporate earnings information and market response, which directly impacts corporate performance evaluation and investment decision-making. This study aims to identify the most frequently researched topics in the Earnings Response Coefficient domain, explore the basic concepts and theoretical frameworks underlying ERC research, and propose potential future research directions in the field, all within finance and investment management. This research employs bibliometric analysis to use data from Google Scholar and Scopus, accessed through Publish or Perish (PoP), to evaluate the literature’s performance, explore related topics, and identify research trends, thereby deepening the understanding of ERC studies. The findings reveal that income smoothing and intellectual capital disclosure have a significant impact but low connectedness, indicating a need for deeper exploration to heighten their relevance in ERC studies. Research on corporate social responsibility exhibits a high degree of interconnectedness and substantial impact. Underexplored topics such as economic uncertainty and analysts’ influence require greater attention to understand their contributions fully. This study identifies publication trends and citation networks related to ERC, provides insights into researcher collaborations, and offers guidance for academics, practitioners, and policymakers to enrich their understanding, develop more effective earnings management strategies, and design regulations that bolster market transparency and efficiency in the realm of finance and investment management. This research is particularly beneficial for practitioners, as it helps evaluate more effective earnings management strategies and understand the market’s response to earnings information, ultimately enhancing firm value. For policymakers, this study provides a framework for designing regulations and policies that support financial information transparency and market efficiency to enhance economic stability and investor confidence. Full article

This paper examines the influence of Monetary Policy Transparency on Foreign Equity Portfolio Allocation by addressing the informational frictions that shape cross-border investment in Financial Markets. Building on recent developments in central bank communication, we construct a multidimensional measure of Monetary Policy Transparency that extends traditional frameworks by incorporating Accounting Information Transparency and Financial Stability Transparency. This enhanced index provides a more comprehensive representation of the informational environment faced by foreign investors. Using a panel of developed and emerging economies over a twenty-year period, the empirical analysis combines OLS and system GMM estimations to account for endogeneity, dynamic effects, and unobserved heterogeneity. The results indicate that higher levels of Monetary Policy Transparency significantly increase the attractiveness of domestic equity markets to foreign investors, with heterogeneous effects across country groups linked to differences in institutional credibility and financial integration. Overall, the findings highlight multidimensional transparency as a key determinant of Foreign Equity Portfolio Allocation, underscoring the strategic importance of Accounting Information Transparency and Financial Stability Transparency in shaping foreign equity portfolio allocation. Full article

Open-source software is transforming visualization-oriented digital documentation and conceptual BIM by lowering financial and technical barriers, enabling broader participation in the digitalization of the AEC sector. This study develops and validates a cost-effective Scan-to-BIM workflow that combines low-cost hardware with freely available software for 3D data acquisition, processing, and modeling. Photogrammetry and SLAM-based techniques generate accurate point clouds, which, once verified against terrestrial laser scanning data, can be integrated into open-source BIM environments. The workflow leverages COLMAP for 3D reconstruction and BlenderBIM for parametric modeling, combining geometric and semantic information to produce fully interoperable models. While open-source tools offer accessibility and transparency, they require supplementary validation in precision-critical applications and may involve trade-offs in accuracy, stability, and automation compared to commercial solutions. Application to a case study shows how efficient and rapid the process is, representing the trend for the scientific community. Full article

Accurate early-warning forecasting of corporate financial distress remains a critical challenge due to nonlinear financial relationships, severe data imbalance, and the high operational costs of false alarms in risk-monitoring systems. This study proposes an explainable voting ensemble framework for early-warning forecasting of corporate financial distress using lagged accounting-based financial information. The proposed framework integrates heterogeneous base learners, including Decision Tree, Neural Network, and k-Nearest Neighbors models, and is evaluated using financial statement data from 752 publicly listed firms in Thailand, comprising sixteen financial ratios across six dimensions: liquidity, operating efficiency, debt management, profitability, earnings quality, and solvency. To ensure robustness under imbalanced and rare-event conditions, the study employs feature selection, data normalization, stratified cross-validation, resampling techniques, and repeated validation procedures. Empirical results demonstrate that the proposed Voting Ensemble delivers a precision-oriented and decision-relevant forecasting profile, outperforming classical classifiers and maintaining greater early-warning reliability when benchmarked against advanced tree-based ensemble models. Probability-based evaluation further confirms the robustness and calibration stability of the proposed framework under repeated cross-validation. By adopting a forward-looking, early-warning perspective and integrating ensemble learning with explainable machine learning principles, this study offers a transparent and scalable approach to financial distress forecasting. The findings offer practical implications for auditors, investors, and regulators seeking reliable early-warning tools for corporate risk assessment, particularly in emerging market environments characterized by data imbalance and heightened uncertainty. Full article

Accurate credit risk assessment for small firms and farmers is crucial for financial stability and inclusion; however, many models still rely on binary default labels, overlooking the continuum of borrower vulnerability. To address this, we propose Transformer–LightGBM–Stacked Logistic Regression (TL-StackLR), a hybrid stacking framework for multi-class loan default prediction. The framework combines three learners: a Feature Tokenizer Transformer (FT-Transformer) for feature interactions, LightGBM for non-linear pattern recognition, and a stacked LR meta-learner for calibrated probability fusion. We transform binary labels into three risk tiers, Low, Medium, and High, based on quantile-based stratification of default probabilities, aligning the model with real-world risk management. Evaluated on datasets from 3045 firms and 2044 farmers in China, TL-StackLR achieves state-of-the-art ROC-AUC scores of 0.986 (firms) and 0.972 (farmers), with superior calibration and discrimination across all risk classes, outperforming all standalone and partial-hybrid benchmarks. The framework provides SHapley Additive exPlanations (SHAP) interpretability, showing how key risk drivers, such as income, industry experience, and mortgage score for firms and loan purpose, Engel coefficient, and income for farmers, influence risk tiers. This transparency transforms TL-StackLR into a decision-support tool, enabling targeted interventions for inclusive lending, thus offering a practical foundation for equitable credit risk management. Full article

A robust audit services market is essential for ensuring financial transparency, regulatory compliance, and investor confidence. As a dimension of organizational sustainability, the capacity of audit firms to remain competitive and resilient under market pressures is increasingly relevant. However, existing research has paid insufficient attention to the stability of audit firms and the survival dynamics of mid-sized players. The present study addresses this gap by examining the volatility of the U.S. audit services market and the sustained dominance of the Big Four firms over the 2019–2023 period. Based on data from Accounting Today’s annual rankings, the study employs Kaplan–Meier survival analysis to assess the probability of audit firms remaining in the Top 100 over time. Furthermore, K-means clustering is used to identify structural factors contributing to firm exit, including revenue, number of employees, branches, and partners. The results indicate that, while the Big Four retained stable leadership, 19 firms exited the rankings, with revenue and number of specialists being the most influential exit factors. These findings provide insights for enhancing risk assessment, strategic planning, and regulatory design. Moreover, the study contributes to broader discussions on organizational sustainability and long-term competitiveness within the context of the U.S. audit sector, while offering insights that may be informative for understanding similar dynamics in other markets rather than aiming for direct global generalization. Full article

This study investigates the dynamic relationship between risk-taking, capital adequacy, and operational efficiency in the MENA banking sector, with a particular emphasis on the role of ESG performance in shaping sustainable financial behavior. Using an unbalanced panel of 167 commercial banks from 2015 to 2024, we develop a three-equation framework and estimate it using the two-step System-GMM method to address endogeneity, simultaneity, and dynamic effects. The empirical results reveal significant interdependencies among risk, capital, and efficiency, confirming the existence of a sustainable risk–capital–efficiency nexus. The results reveal that bank risk is strongly persistent; however, ESG performance significantly mitigates credit risk, particularly through its social and governance dimensions, which enhance transparency, borrower discipline, and stakeholder trust. Efficiency also acts as a stabilizing force by reducing overall risk. Capital adequacy is positively influenced by ESG performance and efficiency, indicating that sustainable and well-managed banks maintain stronger capital buffers and more resilient balance sheets. Furthermore, bank efficiency improves with profitability, capitalization, favorable macroeconomic conditions, and socially oriented ESG engagement. These findings demonstrate that ESG adoption is a strategic driver of financial soundness, simultaneously lowering risk, reinforcing capital strength, and enhancing operational performance. The paper offers important implications for regulators and bank managers, highlighting the need to embed ESG metrics into supervisory frameworks, risk-management systems, and long-term strategic planning. Full article

This study examines the relationship between foreign direct investment (FDI) and economic growth in Romania during 2003–2023, by distinguishing the effects of FDI stock and FDI flow, with a focus on sustainable development. Because the variables have different integration orders, we used the ARDL model and the bounds test to check the long-run relationship between real GDP per capita and FDI stock, FDI inflows, exports, and labor productivity growth. The refined ARDL model (adjusted for multicollinearity) confirms a stable long-run equilibrium relationship among the variables, with all coefficients statistically significant at the 5% level. Long-run elasticities indicate that economic growth is primarily driven by FDI stock (0.23) and exports (0.24), validating the “export–investment nexus” hypothesis. Also, FDI inflows contribute positively (0.09), while labor productivity remains a critical internal determinant (0.03). Short-run dynamics, captured through the ARDL-ECM specification, reveal that only labor productivity exerts an immediate effect, whereas foreign capital plays a structural stabilizing role. The error correction term (–0.279) suggests an adjustment speed of approximately 27.9% annually, reflecting strong economic resilience across EU ascension (2007), financial crisis (2008–2009), and COVID-19 pandemic (2020–2021). Our study contributes to the literature regarding the effects of FDI in Romania, by simultaneously including FDI stock and flow and considering the pandemic period. Also, our study employs dynamic productivity specification and provides transparent model selection procedures within a sustainable framework. The results in this study are of interest for policymakers, emphasizing the need to focus on attracting quality FDI (green and high-tech investments, investor retention, and human capital development) which can facilitate sustainability-oriented strategies that could lead to sustainable economic growth. Full article