Hasil untuk "Banking"

D. Baron

Allen N. Berger, Timothy H. Hannan

Vichuda Nui Polatoglu, S. Ekin

H. Riquelme, Rosa E. Rios

Avinandan Mukherjee, P. Nath

Giovanni Dell'Ariccia, E. Detragiache, Raghu Rajan et al.

C. Borio, Mathias Drehmann

O. D. Jonghe

E. Nier, Ursel Baumann

W. Kerr, Ramana Nanda

Tao Zhou

Gustavo Polleti, Marlesson Santana, Eduardo Fontes

We introduced a multimodal foundational model for financial transactions that integrates both structured attributes and unstructured textual descriptions into a unified representation. By adapting masked language modeling to transaction sequences, we demonstrated that our approach not only outperforms classical feature engineering and discrete event sequence methods but is also particularly effective in data-scarce Open Banking scenarios. To our knowledge, this is the first large-scale study across thousands of financial institutions in North America, providing evidence that multimodal representations can generalize across geographies and institutions. These results highlight the potential of self-supervised models to advance financial applications ranging from fraud prevention and credit risk to customer insights

Haibo Wang

The growing economic influence of the BRICS nations requires risk models that capture complex, long-term dynamics. This paper introduces the Bank Risk Interlinkage with Dynamic Graph and Event Simulations (BRIDGES) framework, which analyzes systemic risk based on the level of information complexity (zero-order, first-order, and second-order). BRIDGES utilizes the Dynamic Time Warping (DTW) distance to construct a dynamic network for 551 BRICS banks based on their strategic similarity, using zero-order information such as annual balance sheet data from 2008 to 2024. It then employs first-order information, including trends in risk ratios, to detect shifts in banks' behavior. A Temporal Graph Neural Network (TGNN), as the core of BRIDGES, is deployed to learn network evolutions and detect second-order information, such as anomalous changes in the structural relationships of the bank network. To measure the impact of anomalous changes on network stability, BRIDGES performs Agent-Based Model (ABM) simulations to assess the banking system's resilience to internal financial failure and external geopolitical shocks at the individual country level and across BRICS nations. Simulation results show that the failure of the largest institutions causes more systemic damage than the failure of the financially vulnerable or dynamically anomalous ones, driven by powerful panic effects. Compared to this "too big to fail" scenario, a geopolitical shock with correlated country-wide propagation causes more destructive systemic damage, leading to a near-total systemic collapse. It suggests that the primary threats to BRICS financial stability are second-order panic and large-scale geopolitical shocks, which traditional risk analysis models might not detect.

Cristian F. Jiménez-Varón, Marina I. Knight

Financial spillovers in interconnected systems, such as global banking networks, require tools that capture temporal and frequency dynamics, while incorporating the underlying network topology. While current network time series models are developed in the time-domain, frequency-domain approaches, which reveal how cross-nodal dependencies vary across different cycles, remain under-explored. This paper develops a spectral analysis framework that accommodates flexible forms of network dependence, including interactions mediated through intermediate nodes. This ensures that inter-nodal relationships are not restricted to direct connections, a feature crucial for capturing indirect financial spillovers. We define the network time series spectral density, alongside coherence and partial coherence, and propose both parametric and network-constrained nonparametric methods for their estimation. Simulations and theoretical results demonstrate the strong performance of the parametric approach when the data-generating process aligns with the model structure, whereas the nonparametric alternative provides robustness against model misspecification. An application to global bank connectedness shows that the proposed spectral measures capture inter-bank frequency-specific spillover effects, yielding results consistent with existing measures while additionally uncovering richer patterns of volatility transmission that are intimately connected to the network topology.

Nabila Syafira, Achmad Soediro, Media Kusumawardani et al.

Purpose – This study investigates the influence of Islamic corporate governance (ICG) and the Islamicity performance index (IPI), as proxied by the profit-sharing ratio (PSR), zakat performance ratio (ZPR), and equitable distribution ratio (EDR), on the financial performance of Islamic banks in Indonesia. Furthermore, this study examines the moderating role of Islamic social reporting (ISR) in these relationships. Method – Employing a quantitative approach, this research utilizes secondary data obtained from the Financial Services Authority (FSA) of Indonesia and the official websites of Islamic banks. The sample comprises 10 Islamic commercial banks operating in Indonesia from 2019–2023. The study applies moderated regression analysis (MRA) to assess the proposed relationships. Findings – The findings reveal that, individually, both ICG and EDR positively and significantly influence financial performance, whereas PSR does not exhibit a significant effect. In contrast, ZPR demonstrates a negative and significant impact on financial performance. Moreover, ISR moderates the relationships between ICG, ZPR, and EDR with financial performance, while its moderating effect is not observed in the relationship between PSR and financial performance. ISR strengthens the impact of ICG and the IPI on Islamic banks' financial performance, enhancing the understanding of governance and performance in Islamic finance. Implications – The theoretical implication highlights ISR's role in enhancing ICG and IPI's impact, and the practical implication emphasizes its importance in boosting transparency and trust in Islamic banking.

Subrata Haldar, Somnath Mandal, Subhasis Bhattacharya et al.

Abstract The peri-urban region of Durgapur Municipal Corporation (DMC) area has experienced substantial socioeconomic changes throughout the last decade (2011–2023). Most of the literature focused on urban expansion, landuse changes and industrial expansion with little attention to complex interaction between urbanization, industrialization and their effects on livelihoods and quality of life (QoL). This study examines the socio-economic transformations in the peri-urban zone of the Durgapur Municipal Corporation (DMC) from 2011–2023, emphasizing how urbanization and industrialization shape livelihoods and quality of life (QoL). The study collected primary data and used satellite-driven data for constructing several indices like the Peri-Urban Development Index (PUDI), Peri-Urban Development Transition Index (PUDTI), Livelihood Diversity Index (LDI), and Quality of life (QoL). By the systematic sampling method, the study considered 830 households with 10% marginal error and 20% non-sampling for the primary survey. Furthermore, statistical analyses like multiple linear regression and ANOVA have been applied to identify the variation in QoL across the study units. The study reveals a positive association between livelihood diversification and PUDTI, underscoring how economic diversification supports socio-economic advancement in peri-urban areas. Multilinear regression analysis highlights that demographic and economic factors especially sex ratio, household mobility, and educational opportunities are stronger predictors of QoL than land use and infrastructure improvements. Additionally, ANOVA results show that inner peri-urban areas experience more substantial QoL improvements than outer areas, likely due to better access to educational institutions, healthcare, transportation, and banking facilities, which have all seen significant upgrades. Despite these advancements, the study also identifies challenges, including displacement from traditional occupations and rising income inequality. These findings underscore the need for integrated development policies to address the diverse and complex factors influencing urbanization and the well-being of peri-urban residents, offering valuable insights for policymakers aiming to foster balanced growth in peri-urban zones.

Dao Ha, Yen Nguyen

Abstract This study aims to examine the impact of financial inclusion on environmental pollution in developing countries, thereby addressing whether financial inclusion contributes to environmental improvement or exacerbates pollution. Based on a panel dataset of 62 developing countries from 2005 to 2022, the study employs the system Generalized Method of Moments (SGMM) estimator to address endogeneity issues and ensure the robustness of the results. The empirical findings reveal a nonlinear relationship between financial inclusion and environmental pollution, with a threshold identified at 0.331. Specifically, expansion tends to increase pollution at lower levels of financial inclusion; however, once financial inclusion surpasses the threshold, its impact becomes positive, helping to improve environmental quality. Furthermore, the study highlights the moderating role of national characteristics, such as income level and participation in the Paris Agreement. In upper-middle-income countries and those that have signed the Paris Agreement, financial inclusion is found to have a positive influence on environmental outcomes. In contrast, financial inclusion tends to aggravate pollution in lower-income countries or those not party to the Paris Agreement. These findings enhance the academic understanding of the finance-environment nexus and provide valuable empirical evidence to support the design of sustainable and environmentally friendly financial inclusion policies.

Bayode Ogunleye, Tonderai Maswera, Laurence Hirsch et al.

Topic modelling is a prominent task for automatic topic extraction in many applications such as sentiment analysis and recommendation systems. The approach is vital for service industries to monitor their customer discussions. The use of traditional approaches such as Latent Dirichlet Allocation (LDA) for topic discovery has shown great performances, however, they are not consistent in their results as these approaches suffer from data sparseness and inability to model the word order in a document. Thus, this study presents the use of Kernel Principal Component Analysis (KernelPCA) and K-means Clustering in the BERTopic architecture. We have prepared a new dataset using tweets from customers of Nigerian banks and we use this to compare the topic modelling approaches. Our findings showed KernelPCA and K-means in the BERTopic architecture-produced coherent topics with a coherence score of 0.8463.

Saeed Siyal, Riaz Ahmad, Shamrez Ali

In the modern age, the card payment business has flourished in Pakistan. Many multinational and national commercial banks have not only introduced debit cards but also many types of credit cards with distinct payment plans, and they are broadly used as financial instruments in consumer financing. The purpose of this research is to identify the distinctive components that cause credit card debt and how Pakistanis are snared into a debt trap by using credit cards. For this purpose, the study examines the effects of credit card usage (CCU), materialism (M), installment plan (IP), convenience (CONV), religiosity (R), and financial literacy (FL) on a dependent variable, debt trap (DT). Furthermore, the moderation impacts of R, FL, and CONV are tested too. The quantitative research approach and cross-sectional research design were applied. The data was collected from 297 credit card users by using a convenience sampling technique that shows a 79.2 % response rate. The Partial Least Square (PLS)-Structural Equation Modeling (SEM) technique was employed for data analysis. The Partial Least Square (PLS)-Structural Equation Modeling (SEM) direct effect has shown that CCU, M, IP, CONV, and R have a significant relationship with DT while FL has an insignificant relationship with the DP. In other words, the indirect effect has shown that FL is not significantly moderate in the relationship between R and DT. Likely, CONV does not significantly moderate the relationship between CCU and DP, however; other indirect effects show a significant moderated relationship of all other independent and dependent variables. Most of the moderating effects are supported, therefore the moderating effects are significant contributions. The study findings could help banks and regulatory bodies in launching these types of credit cards which are under shariah complaints about captivating the users to use the credit card without any fear. This research is a pioneer study in the context of the Pakistan banking sector along with three moderators, religiosity, financial literacy, and convenience. Based on the findings of this research, policymakers and financial institutions in Pakistan should consider introducing Shariah-compliant credit cards that align with users' religious beliefs, while promoting financial literacy programs to better educate consumers on responsible credit card usage and debt management, thus preventing them from falling into debt traps.

S. A. Kabirzad, B. M. Rehan, Z. Zulkafli et al.

Investment to reduce flood risk for social and economic wellbeing requires quantitative evidence to guide decisions. Direct and indirect flood damages at individual household and business building levels were assessed in this study using multivariate analysis with three groups of flood damage attributes, i.e., flood characteristics, socioeconomic conditions, and building types. A total of 172 and 45 respondents from residential and commercial buildings were gathered through door-to-door interviews at areas in Peninsular Malaysia that were pre-identified to have frequently flooded. Two main findings can be drawn from this study. First, flood damage is greatly contributed by high-income households and businesses, despite them being less exposed to floods than low-income earners. This supports the current use of mean economic damage in engineering-based flood intervention analysis. Second, indirect damages increase with the increase in family size, indicating the importance of strengthening preparedness and social support to those with great social responsibility. Overall, the study highlights the importance of holistic flood management accounting for both direct and indirect losses. HIGHLIGHTS National flood damage is greatly contributed by high-income households and businesses, despite them being less exposed to floods than low-income earners, suggesting that the least mean economic damage is used in engineering-based flood intervention analysis.; Indirect damages increase with the increase in family size, thus requiring a greater investment for the socially vulnerable group.;