Search Results (958)

The increasing adoption of artificial intelligence (AI) in public-sector financial management has raised significant concerns regarding interpretability, accountability, governance alignment, and institutional transparency. Existing AI-based fiscal analytical approaches frequently emphasize predictive capability while providing limited integration with formal governance structures and public-sector oversight requirements. This study proposes a governance-centered AI consultancy framework that embeds AI-assisted fiscal analysis directly within institutional budgeting, accountability, and governance-oriented decision-support workflows. Rather than treating AI as an isolated predictive or automation technology, the proposed framework operationalizes analytical intelligence within governance-aware consultancy structures emphasizing interpretability, auditability, traceability, and institutional usability. The framework was evaluated using authentic longitudinal public-sector fiscal records obtained from the official Ministry of Finance budget performance reports of Saudi Arabia for fiscal year 2023. The experimental evaluation incorporated temporal fiscal monitoring, robustness analysis under heterogeneous budgetary conditions, and comparative assessment against conventional descriptive budgetary analysis and standalone AI-based fiscal analytical procedures. The experiments utilized quarterly governmental fiscal indicators including revenues, expenditures, deficit progression, debt accumulation, expenditure volatility, and oil and non-oil revenue behavior across multiple reporting intervals. The findings demonstrate that the proposed governance-centered framework preserves strong temporal analytical consistency (81.9%) while achieving an algorithmically computed interpretability-support score of 4.6/5, a Governance Alignment Index of 0.94, and an operational Decision Usability Index of 4.7/5 relative to the conventional statistical baseline (Logistic Regression) and standalone AI-based analytical approaches. Improvements over conventional descriptive budgetary analysis are reported separately through the governance-oriented institutional comparison. Additional validation studies showed that the Decision Usability Index and Governance Alignment Index provided the strongest predictive contributions, while Traceability Index and Temporal Support Index exhibited the strongest construct-level statistical validity evidence. Robustness analysis further showed stable governance-aware analytical behavior across heterogeneous fiscal conditions involving expenditure volatility, debt progression, and changing revenue structures. Additional statistical validation demonstrated empirical support for the Traceability Index and Temporal Support Index, while other governance metrics exhibited weaker evidence and should be interpreted primarily as governance-support indicators rather than primary predictive drivers. The findings suggest that governance-aware analytical operationalization can provide measurable value beyond standalone AI models for formula-based operational fiscal-risk categorization when supported by reproducible governance-oriented analytical procedures. Because the supervised labels represent deterministic operational fiscal-risk categories rather than independently verified fiscal anomalies, the reported results should not be interpreted as direct validation of real-world fiscal anomaly detection or financial misconduct identification. Full article

Amid the massive digitization of financial flows, forensic accounting is emerging as a strategic lever to strengthen the prevention, detection, and control of financial irregularities. This study aims to examine the associations between forensic accounting practices and perceived financial fraud control, and to analyze the mediating roles of corporate governance quality, transparency, and disclosure. A quantitative survey was conducted with 325 audit, control, and accounting professionals. The data were analyzed using structural equation modeling to assess the direct and indirect relationships. Fraud prevention mechanisms have the strongest correlation with financial fraud control (β = 0.310; p < 0.001), followed by forensic data analysis (β = 0.270; p < 0.001), litigation support (β = 0.084), and fraud detection techniques. Forensic data analysis is associated with the quality of corporate governance (β = 0.480; p < 0.001), while transparency and disclosure practices are the most important determinants of perceived financial fraud control (β = 0.463; p < 0.001), followed by governance quality (β = 0.165). Mediation analyses show that corporate governance and transparency primarily amplify the relationship between prevention mechanisms and forensic data analysis, whereas the mediating effects of detection techniques are not significant. Financial fraud control is strongly associated with an integrated approach that combines prevention, analytical capabilities, quality governance, and transparency, rather than with detection activities alone. The findings provide an explanatory model that highlights the organizational mechanisms by which forensic accounting practices strengthen governance and control over financial fraud. Full article

After the global financial crisis, central banks used unconventional monetary policies, including quantitative easing (QE), to restore financial stability. Although several studies have analyzed the effects of these measures on financial markets, limited attention has been given to how central bank transparency influences the stability of commercial banks operating under these conditions. This study examines the impact of central bank transparency on national banking system vulnerability during periods of QE across eight economies from 2013 to 2019. Using a dynamic two-step generalized method of moments model based on bank-level data, the analysis includes bank-specific variables, monetary policy indicators, macroeconomic determinants, central bank characteristics, and structural factors of the banking sector and applies a fixed-effects panel regression model. The findings show that transparency moderates the effect of QE on bank vulnerability and strengthens banking system resilience. By emphasizing the role of central bank transparency as a key element of monetary policy, this research provides useful evidence for policymakers seeking to improve the effectiveness and credibility of unconventional monetary measures in different banking environments. Full article

Financial reporting oversight evolves through repeated feedback among managerial incentives, audit detection, board oversight, regulatory intervention, and market trust. I develop a transparent recursive simulation model to examine whether specified mechanism combinations can generate distinguishable governance trajectories within its rules. Monte Carlo simulations compare institutional scenarios, supported by analyses of uncertainty, alternative model designs, heterogeneous conditions, and a learning-agent extension. Within the simulations, high-transparency coordination generally produces the strongest governance outcomes, whereas weak governance remains consistently least favorable across the uncertainty analyses. In the model, governance improvement depends on interactions among disclosure, audit, regulation, and market feedback. In the learning-agent extension, learned policies yield less favorable governance and reporting outcomes than the fixed-policy benchmark. Chinese A-share evidence is broadly consistent with the main simulated patterns in direction, risk location, and broad ordering. These findings provide mechanism-sufficiency evidence within the declared rule family and explain how oversight signals become institutional outcomes and subsequent feedback. Full article

Digitization is reshaping how banks operate, yet whether it improves corporate financial performance remains unsettled. This study examines how the digital transformation that banks disclose relates to the structure of their key financial performance indicators. Using a balanced panel of 73 listed Gulf Cooperation Council (GCC) banks over 2020–2025 (438 bank–year observations), digital transformation is measured by a text-mined digital disclosure index (DDI, 0–100) constructed from annual reports and decomposed into nine themes. Bank fixed-effects regressions with Driscoll–Kraay standard errors, one-year-lagged specifications, and two-step system GMM are estimated across profitability, net interest margin, cost efficiency, credit risk and capital adequacy. Disclosed digitization more than doubled over the window, but its associations with performance are conditional rather than uniformly positive. Within banks, a higher DDI value is associated with wider net interest margins, yet also with lower profitability, higher cost-to-income ratios, modestly higher credit risk, and thinner capital buffers. This pattern is consistent with an investment or build-out phase in which the costs of digital transformation are visible before any efficiency or stability dividend and in which margins are the single offsetting benefit. The six-year window observes only this cost-bearing segment and not any later recovery, so the study documents the investment-phase drag rather than a completed cycle. The theme decomposition indicates that the margin association is closest to regulatory technology, cybersecurity, broad transformation and payments. Because the design is observational, the results are interpreted as within-bank associations rather than causal effects, and, although precisely estimated, these associations are economically modest. The study contributes a transparent theme-decomposed measure of bank digitization and evidence on its limits for corporate financial performance in an emerging-market banking region. Full article

As the retail banking sector shifts toward automated lending, the black-box nature of high-performing machine learning models remains a significant barrier to regulatory transparency and institutional trust. A critical gap in existing literature is the lack of deployed frameworks that simultaneously optimize predictive accuracy, manage asymmetric financial risks, and provide actionable interpretability. To bridge this gap, this study aims to develop and evaluate a highly interpretable, ethically accountable ensemble machine learning framework for credit risk assessment. Utilizing a cross-sectional public dataset of over 45,000 generalized retail banking records, this research conducts a comprehensive comparative analysis of four diverse ensemble architectures: Bagging, Boosting, Stacking, and Voting. To address inherent class imbalance and evaluate risk tolerance, the models were integrated with Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) resampling techniques. While all architectures demonstrated high discriminative power, the SMOTE-balanced Bagging model emerged as the superior performer, achieving a peak Area Under the Curve (AUC) of 0.972 by establishing a safe operational threshold that strictly minimizes costly false approvals. Crucially, a SHapley Additive exPlanations (SHAP) framework was applied across all four models to decode their internal logic. The SHAP analysis successfully validated that the ensembles prioritize core financial behavior, such as default history and loan-to-income ratios, while correctly assigning near-zero predictive weight to demographic traits like gender and education. By empirically proving that high-performance algorithms can be mathematically blind to demographic biases, this framework directly advances SDG 10 (Reduced Inequalities). Furthermore, by resolving the performance-transparency trade-off, this study provides the accountable, feature-level justifications required for secure and sustainable financial inclusion (SDG 8). Full article

This study investigates the impact of real effective exchange rate (REER) volatility on foreign direct investment (FDI) inflows in three major Central and Eastern European (CEE) economies—Hungary, Poland, and Romania—using quarterly data spanning from 2007-Q1 to 2024-Q4. The exchange rate volatility is modeled using a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework, and country-specific relationships are estimated through Autoregressive Distributed Lag (ARDL) bounds testing and Toda–Yamamoto causality analysis. Our research indicates that a uniform relationship does not exist across the region. In Hungary, the utilization of directional FDI data excluding Special Purpose Entities (SPEs), in conjunction with structural breaks and quarterly seasonal controls, reveals a statistically significant nonlinear (inverted U-shaped) relationship between long-run exchange rate volatility and FDI inflows. In addition, domestic financial development exerts a substantial buffering effect on the transmission of volatility in Hungary by bypassing SPE flows that previously obscured this effect. In Poland and Romania, a stronger currency consistently discourages investment by reducing cost competitiveness. Romania shows a distinct pattern: volatility initially attracts FDI, and while deeper financial markets meaningfully dampen this effect, the net relationship remains positive, unlike Hungary, where sufficiently deep credit markets fully reverse it. These results suggest that policymakers should look beyond short-term exchange rate stabilization and instead prioritize structural reforms, competitive exchange rate levels, transparent FDI reporting standards, and deeper domestic financial markets to sustain FDI inflows. Full article

In the modern understanding of public administration, public authorities establish companies governed by private law to ensure flexibility and efficiency in service delivery. However, the fact that these companies utilize public funds necessitates effective oversight in accordance with the principles of accountability and transparency. Since the capital of these companies is derived from public resources, their financial auditing by public authorities becomes necessary. Under Turkish law, the financial audit of these companies—established with public capital and whose managers are appointed by public authorities—is carried out by the institution known as the Turkish Court of Accounts (TCA). This study aims to analyze the structure and functioning of the TCA, the institution responsible for the financial audit of public companies, within the framework of the Turkish Court of Accounts Act No. 6085, and to provide an overview of this subject. Based on the TCA Act, the body responsible for assessing the financial discipline and legal compliance of public companies is introduced. This study also provides information on the public officials serving within the TCA and the units in which they perform their duties. Furthermore, it addresses the disciplinary and criminal liability of those serving within the TCA. Full article

Artificial intelligence (AI) is reshaping the financial sector by redefining how customers access, interpret, and experience banking services. However, financial inclusion is still frequently analyzed in terms of access, use, or availability of digital channels, with less attention paid to the relational quality of interactions between customers and institutions. This study introduces Relational Inclusion in Banking (RIB) as a specific dimension of financial inclusion in AI-mediated contexts, referring to the degree to which customers continue to feel understood, treated fairly, heard, and connected to their institution. Drawing on Social Exchange Theory and Sociotechnical Systems Theory, a PLS-SEM model was tested using data from 770 banking customers in Extremadura, Spain. The results show that institutional trust was the strongest antecedent of RIB, followed by relational mediation capacity, perceived fairness of AI, and AI transparency. In addition, the rural-urban context influenced digital literacy level, although it did not directly explain institutional trust. At the theoretical level, this study contributes to existing knowledge by shifting the analysis of financial inclusion from digital access toward the relational position customers retain within automated banking services. It also identifies human mediation as a sociotechnical mechanism that connects AI-assisted outcomes with customers’ specific circumstances. At the practical level, the managerial implications direct financial institutions toward clear AI governance, better-designed AI-assisted interactions, and effective procedures for explanation and human review. The policy implications highlight the need to strengthen the traceability of automated decisions, ensure effective human review mechanisms, and monitor potential inequalities associated with territory or digital vulnerability. They also point to the value of maintaining hybrid support mechanisms that enable customers with greater digital difficulties to understand and challenge AI-assisted decisions. Full article

Credit risk assessment forms a cornerstone of banking risk management and the stability of the wider financial system. Over the past decade, the rapid development of machine learning (ML) techniques has substantially enhanced traditional credit risk assessment methodologies. ML has now emerged as a core technological pillar for the banking sector, strengthening risk identification capabilities, optimising credit decision-making, and advancing financial inclusion. Conventional credit scoring models, dominated by logistic regression (LR) and scorecard approaches, offer inherent strengths in interpretability and regulatory compliance. However, constrained by their linear assumptions, these methods struggle to capture complex non-linear relationships within credit data and deliver insufficient predictive accuracy for the “credit-invisible” population lacking formal credit histories. This paper presents a systematic literature review (SLR) of ML applications in credit risk assessment (CRA), covering publications from January 2016 to May 2026. A total of 894 papers were retrieved from five digital libraries, and following a rigorous multi-stage screening process, 129 studies were selected for final inclusion. Our analysis reveals that tree-based ensemble models and deep learning (DL) architectures predominate in contemporary research in this field. Meanwhile, post hoc explanation methods and machine learning operations (MLOps) are gaining significant traction as solutions to address fairness, transparency, and system maintenance challenges in real-world production environments. We synthesise prevailing methodologies into a unified end-to-end credit risk modelling framework spanning data preprocessing, feature engineering, model training, evaluation, and operational deployment. Through a critical assessment of the advantages, limitations, and inherent trade-offs of existing approaches, this SLR not only identifies current research gaps and future directions for the academic community, but also provides practical guidance for the banking sector to build compliant, fair, and efficient intelligent risk assessment systems. Full article

Social media platforms have become major vectors for financial and cryptocurrency fraud, resulting in substantial economic losses and eroding user trust. This research presents a dual model system integrating machine learning-based user verification with deep learning-based content analysis to detect fraudulent activity more effectively than traditional single dimensional approaches. The core innovation lies in fusing these two modalities using a logical OR strategy. This design facilitates the detection of hybrid fraud schemes such as compromised legitimate accounts posting deceptive content or fake accounts spreading benign-looking messages that typically evade isolated detection systems. For user verification, ensemble methods were evaluated on a large, balanced dataset of social media profiles. Among the individual classifiers evaluated, the random forest classifier achieved the strongest performance and was selected for the final architecture due to its optimal balance of accuracy, interpretability, and computational efficiency. For content analysis, a convolutional neural network (CNN) trained on a substantial corpus of crypto-related posts demonstrated high accuracy, outperforming traditional keyword-based and recurrent neural network baselines. Ultimately, combining these models yields a system that flags significantly more fraudulent posts than either component alone. The user verification component retains interpretability through feature importance measures, while the CNN-based content analysis component operates as a less transparent classifier; the combined system can be adapted across diverse social media platforms. Full article

Urban regeneration has become a primary strategy for addressing the spatial, social, environmental, and economic challenges associated with rapid urbanization. By using the PRISMA framework, this paper provides a systematic review of urban regeneration, focusing on its objectives, indicator systems, and estimation approaches. Journal articles published between 2008 and 2025 were retrieved from Web of Science, and 50 studies were retained after screening. The findings indicate that regeneration has increasingly highlighted long-term economic efficiency and environmental performance, along with growing attention to social equity, urban safety, and resilience. Life cycle costs are recognized as a core framework for capturing long-term financial and environmental performance. In terms of methodological evolution, a progression from conventional estimation techniques toward machine learning and hybrid approaches is observed. Several challenges remain, including the absence of standardized data frameworks and limited cross-regional transferability of existing models. To advance the field, future research could prioritize the establishment of common data standards, the adoption of explainable machine learning for improved model transparency, and the validation of models across diverse geographical and project settings. Full article

Explainable artificial intelligence (XAI) has become a central methodology for developing transparent, accountable, and human-centered AI systems. As data-driven models are increasingly deployed in high-stakes and socially consequential settings, explanations are expected not only to illuminate model behavior but also to support validation, error analysis, fairness auditing, regulatory compliance, and effective human–AI collaboration. This Editorial introduces the Special Issue “Explainable Artificial Intelligence Technology and Its Applications” and situates its contributions within the broader trajectory of XAI research. We briefly review major methodological families, including intrinsic interpretability, local surrogate and Shapley-value explanations, gradient- and perturbation-based visual attribution, counterfactual and causal explanations, and human-centered evaluation. We then highlight representative contributions in this Special Issue, which demonstrate how XAI is moving from generic explanation visualizations toward domain-sensitive, data-aware, and operationally reliable methods in network security, computer vision, knowledge graphs, and financial decision support. Finally, we discuss future directions, emphasizing faithful and plausible explanations, causal and multimodal reasoning, real-time and hardware-efficient deployment, trustworthy governance, and the emerging role of XAI in education. Full article

Urban competitiveness has traditionally been associated with infrastructure, economic performance, and innovation capacity, while the role of financial governance has received considerably less attention. This study examines how citizens form perceptions of urban financial integrity and whether such perceptions influence evaluations of urban attractiveness. Drawing on a governance-based framework, the study investigates the relationships among Digital Governance, Financial Transparency, Anti-Fraud Capacity, Financial Integrity, and Urban Attractiveness. Data were collected from 2764 residents of 18 digitally advanced cities across Northern and Western Europe and North America. To enhance methodological rigor, the sample was randomly divided into two equal subsamples and analyzed using Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. The results indicate that Digital Governance is positively associated with both Financial Transparency and Anti-Fraud Capacity, while Financial Transparency and Anti-Fraud Capacity are positively associated with stronger perceptions of Financial Integrity. Financial Integrity emerged as a significant predictor of Urban Attractiveness and served as the principal mechanism through which governance practices influenced broader evaluations of cities. The findings suggest that citizens do not perceive cities as attractive simply because they are digitally advanced; rather, digital governance becomes valuable when it enhances confidence that public financial resources are managed transparently, responsibly, and in the public interest. These results identify Financial Integrity as the key mechanism through which citizens translate governance quality into perceptions of urban attractiveness. Full article

Macroeconomic forecasting has become increasingly challenging in environments characterized by structural breaks, nonlinear dynamics, and elevated economic uncertainty. Traditional econometric forecasting models frequently experience substantial predictive deterioration during periods of financial crises, geopolitical instability, and rapidly evolving macroeconomic conditions due to their reliance on assumptions of parameter stability and linear economic relationships. In response to these limitations, this study proposes an integrated causal machine learning framework designed to improve macroeconomic forecasting performance under structural instability and uncertainty. The proposed framework combines structural break detection techniques, machine learning algorithms, causal inference methodologies, and Explainable Artificial Intelligence (XAI) tools within a unified empirical architecture. More specifically, the study integrates Bai–Perron structural break analysis, Markov-Switching regime identification, Double Machine Learning (DML), Causal Forest estimation procedures, and SHAP-based explainability techniques. The empirical analysis employs a U.S. macroeconomic time-series dataset covering major crisis episodes, including the 2008 Global Financial Crisis, the COVID-19 pandemic, and the 2022 inflation shock. The dataset combines inflation, monetary, financial, energy-market, and uncertainty indicators obtained from publicly available U.S. macroeconomic databases. The empirical findings demonstrate that causal machine learning models significantly outperform conventional econometric frameworks such as VAR and TVP-VAR models, as well as standard machine learning algorithms including Random Forest (RF), XGBoost, and LSTM networks. The Double Machine Learning framework generates the strongest forecasting performance across all forecasting horizons, economic regimes, and robustness specifications. The results further reveal that macroeconomic relationships are highly regime-dependent and strongly influenced by uncertainty indicators, financial volatility, oil price shocks, and monetary policy dynamics. Explainability analysis additionally shows that uncertainty measures and energy market variables become dominant drivers of inflation forecasts during crisis periods characterized by elevated instability. The study contributes to the growing literature on macroeconomic forecasting by bridging econometric forecasting theory, causal inference methodologies, machine learning techniques, and explainable artificial intelligence within a unified forecasting framework. The findings provide important implications for central banks, policymakers, and financial institutions seeking more adaptive, transparent, and robust forecasting systems under uncertain macroeconomic environments. Full article