Hasil untuk "Banking"

I. Jacobson, G. Booch, J. Rumbaugh

T. Beck, R. Levine, Norman V. Loayza et al.

Allen N. Berger, Gregory F. Udell

D. Chaum

V. Chari, R. Jagannathan

Jiazhao Wang, Wenchao Jiang

Spectrum multiplexer enables simultaneous transmission of multiple narrow-band IoT signals through gateway devices, thereby enhancing overall spectrum utilization. We propose a novel solution based on filter banks that offer increased efficiency and minimal distortion compared with conventional methods. We follow a model-driven approach to integrate the neural networks into the filter bank design by interpreting the neural network models as filter banks. The proposed NN-based filter banks can leverage advanced learning capabilities to achieve distortionless multiplexing and harness hardware acceleration for high efficiency. Then, we evaluate the performance of the spectrum multiplexer implemented by NN-based filter banks for various types of signals and environmental conditions. The results show that it can achieve a low distortion level down to $-39$dB normalized mean squared error. Furthermore, it achieves up to $35$ times execution efficiency gain and $10$dB SNR gain compared with the conventional methods. The field applications show that it can handle both the heterogeneous and homogeneous IoT networks, resulting in high packet reception ratio at the standard receivers up to $98\%$.

Zhengxin Pan, Haishuai Wang, Fangyu Wu et al.

The past decade has witnessed rapid advancements in cross-modal retrieval, with significant progress made in accurately measuring the similarity between cross-modal pairs. However, the persistent hubness problem, a phenomenon where a small number of targets frequently appear as nearest neighbors to numerous queries, continues to hinder the precision of similarity measurements. Despite several proposed methods to reduce hubness, their underlying mechanisms remain poorly understood. To bridge this gap, we analyze the widely-adopted Inverted Softmax approach and demonstrate its effectiveness in balancing target probabilities during retrieval. Building on these insights, we propose a probability-balancing framework for more effective hubness reduction. We contend that balancing target probabilities alone is inadequate and, therefore, extend the framework to balance both query and target probabilities by introducing Sinkhorn Normalization (SN). Notably, we extend SN to scenarios where the true query distribution is unknown, showing that current methods, which rely solely on a query bank to estimate target hubness, produce suboptimal results due to a significant distributional gap between the query bank and targets. To mitigate this issue, we introduce Dual Bank Sinkhorn Normalization (DBSN), incorporating a corresponding target bank alongside the query bank to narrow this distributional gap. Our comprehensive evaluation across various cross-modal retrieval tasks, including image-text retrieval, video-text retrieval, and audio-text retrieval, demonstrates consistent performance improvements, validating the effectiveness of both SN and DBSN. All codes are publicly available at https://github.com/ppanzx/DBSN.

Guillermo Bermejo, Ángel Macías, Juan Luis Herrera et al.

In security-sensitive or regulated environments — such as banking, healthcare, or industrial control systems — strict network segmentation policies prevent direct communication between development and production infrastructure. As a result, software delivery processes in these contexts often rely on manual workflows, including detecting new Docker images, transferring them across isolated domains, and manually applying deployment updates. This paper presents a self-managed, lightweight CI/CD framework specifically designed for such disconnected environments. Rather than managing containers directly, the system automates a critical subset of the DevOps workflow: the detection, transfer, and deployment of updated Docker images across network-isolated zones. It operates from a bastion host with access to both segments, utilizing open-source tools: Diun for monitoring external Docker registries, Skopeo for transferring images securely between registries, and ‘kubectl‘ for updating the corresponding Kubernetes deployments. Notifications are sent via Postfix to maintain traceability at every stage of the process. The main contribution of this work lies in adapting DevOps automation principles to segmented infrastructures without relying on cloud services or central control, a scenario largely unsupported by existing tools. The proposed solution requires no internet access, cloud platforms, or third-party services, making it suitable for environments with strict connectivity restrictions. It is modular, reproducible, and vendor-neutral. Validation in a simulated enterprise scenario confirms the system’s reliability across both successful and failure cases. By targeting the image propagation stage of the deployment pipeline, this work contributes a practical, focused automation tool for CI/CD under constrained network conditions. Source code and deployment artifacts are publicly available to facilitate reuse in similarly restricted environments.

Hasan Ayaydın, Tolga Ergün, Abdulkadir Barut et al.

Abstract The increasing emphasis on climate change and environmental sustainability worldwide has brought about the convergence and increased focus on financial technologies (FinTech) and green energy initiatives. In light of this, the study’s objective is to investigate how FinTech moderates the relationship between the ecological footprint (EF) and green energy transition (GTE) in the BRICS-T nations between 1990 and 2021. This study examines how fintech moderated the GTE and EF in the BRICS-T from 1990 to 2021. We applied the Fully Modified Ordinary Least Squares (FMOLS) and used Dynamic Ordinary Least Squares (DOLS) as the main estimators for longitudinal analysis. In contrast, Driscoll-Kraay was used to verify the robustness of the results under cross-sectional dependence and heteroskedasticity. The results reveal that FinTech indirectly hinders EF by facilitating GTE. The outcomes also show that FinTech significantly constrains EF, while GDP and industrialization worsen EF. The results also confirm the important role of GTE and foreign direct investment (FDI) in reducing CO2 emissions in BRICS-T countries. Lastly, the paper offers policymakers useful recommendations for lowering EF in light of these outcomes. The study suggests establishing appropriate policies and strategies that encourage FinTech platforms to invest in green energy projects, including financial technology, promoting energy-efficient and low-carbon foreign direct investment, and encouraging GTE.

Dũng Trí Thái

Sự gia tăng nhanh chóng của lực lượng lao động thế hệ Gen Z tại Việt Nam, đặc biệt ở các đô thị lớn, đặt ra thách thức cho doanh nghiệp không chỉ trong việc thu hút mà còn duy trì gắn kết và ổn định nhân sự. Các khảo sát gần đây cho thấy mức độ gắn kết của Gen Z còn hạn chế, khiến việc xây dựng văn hóa tổ chức phù hợp với giá trị thế hệ trẻ trở nên cấp thiết. Văn hóa doanh nghiệp - được hiểu như hệ thống giá trị, chuẩn mực và niềm tin chung - là nhân tố nền tảng có khả năng tác động mạnh đến sự gắn kết. Dựa trên khung lý thuyết của Denison (2000) và nghiên cứu của Saks (2006), bài viết đánh giá tác động của từng thành phần văn hóa doanh nghiệp đến gắn kết nhân viên Gen Z. Nghiên cứu định lượng được thực hiện với 315 nhân viên Gen Z thuộc nhiều lĩnh vực và được phân tích bằng mô hình cấu trúc tuyến tính. Kết quả cho thấy bốn thành phần văn hóa doanh nghiệp gồm giá trị cốt lõi, truyền thông nội bộ, tinh thần học hỏi - đổi mới và môi trường hỗ trợ - tôn trọng đều ảnh hưởng tích cực đến gắn kết, trong đó môi trường hỗ trợ - tôn trọng có tác động mạnh nhất. Từ đó, nghiên cứu đề xuất một số hàm ý giúp doanh nghiệp định hình và phát triển văn hóa tổ chức phù hợp với kỳ vọng của nhân viên Gen Z.

Kunpeng Wang, Zhengzheng Tu, Chenglong Li et al.

Multi-modal salient object detection (MSOD) aims to boost saliency detection performance by integrating visible sources with depth or thermal infrared ones. Existing methods generally design different fusion schemes to handle certain issues or challenges. Although these fusion schemes are effective at addressing specific issues or challenges, they may struggle to handle multiple complex challenges simultaneously. To solve this problem, we propose a novel adaptive fusion bank that makes full use of the complementary benefits from a set of basic fusion schemes to handle different challenges simultaneously for robust MSOD. We focus on handling five major challenges in MSOD, namely center bias, scale variation, image clutter, low illumination, and thermal crossover or depth ambiguity. The fusion bank proposed consists of five representative fusion schemes, which are specifically designed based on the characteristics of each challenge, respectively. The bank is scalable, and more fusion schemes could be incorporated into the bank for more challenges. To adaptively select the appropriate fusion scheme for multi-modal input, we introduce an adaptive ensemble module that forms the adaptive fusion bank, which is embedded into hierarchical layers for sufficient fusion of different source data. Moreover, we design an indirect interactive guidance module to accurately detect salient hollow objects via the skip integration of high-level semantic information and low-level spatial details. Extensive experiments on three RGBT datasets and seven RGBD datasets demonstrate that the proposed method achieves the outstanding performance compared to the state-of-the-art methods. The code and results are available at https://github.com/Angknpng/LAFB.

Yu Cheng, Qin Yang, Liyang Wang et al.

In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropagation (BP) neural network, to pioneer a novel model for preempting credit risk in commercial banks. The discourse initially scrutinizes conventional financial risk preemptive models, such as ARMA, ARCH, and Logistic regression models, critically analyzing their real-world applications. Subsequently, the exposition elaborates on the construction process of the BP neural network model, encompassing network architecture design, activation function selection, parameter initialization, and objective function construction. Through comparative analysis, the superiority of neural network models in preempting credit risk in commercial banks is elucidated. The experimental segment selects specific bank data, validating the model's predictive accuracy and practicality. Research findings evince that this model efficaciously enhances the foresight and precision of credit risk management.

Victor Aguirregabiria, Robert Clark, Hui Wang

Geographic dispersion of depositors, borrowers, and banks may prevent funding from flowing to high loan demand areas, limiting credit access. Using bank-county-year level data, we provide evidence of the geographic imbalance of deposits and loans and develop a methodology for investigating the contribution to this imbalance of branch networks, market power, and scope economies. Results are based on a novel measure of imbalance and estimation of a structural model of bank competition that admits interconnections across locations and between deposit and loan markets. Counterfactual experiments show branch networks and competition contribute importantly to credit flow but benefit more affluent markets.

Dini Maulana Lestari, Hadri Kusuma, Sunaryati

This study aims to compare the level of maṣlaḥah regarding the existence of collateral in Profit and Loss Sharing (PLS) financing from Islamic banking customers and employees standpoints. This is crucial as PLS financing becomes the main characteristic product of Islamic banks, but it emerges as an unfavorable product due to the high financial risk. The existence of collateral plays a pivotal role in this situation to realize maṣlaḥah. This is an explanatory study with a quantitative approach and survey method, considering 400 respondents, particularly 200 Islamic banking customers and 200 Islamic banking employees, who were conveniently obtained at certain time intervals for a one-shot. An Independent sample t-test is used to calculate the comparison level of maṣlaḥah among these two groups. At the same time, Rasch Model analysis is also used to measure the data based on the demographic characteristics of respondents. The result discovers that although there is a slightly different level of maṣlaḥah between Islamic banking customers and employees in PLS finance practice regarding collateral, it still significantly brings benefit, as it aligns with the concept of ta’widh to prevent financial risk. This finding contributes to Islamic banks as a guideline to understand how to operate and enhance the market share of this financing product based on the Rasch Model Analysis.

Jihan Mafaza, Maharani Dheva Dwi Safitri, Eiyagina Tenika et al.

This study aims to examine the effect of capital adequacy ratio, non-performing loan, loan loss provisions ratio, loan-to-deposit ratio, loan-to-asset ratio, bank size, and bank age on financial performance as measured by return on assets (ROA) in 35 banking companies listed on the Indonesia Stock Exchange during the period 2018-2022. The study results indicate that capital adequacy ratio, non-performing loan, loan loss provisions ratio, and bank size do not significantly affect bank financial performance. On the other hand, loan-to-deposit ratio, loan-to-asset ratio, and bank age were found to significantly impact ROA, indicating that liquidity and company age factors are important determinants in improving banking financial performance.

Elvira Nica, Gheorghe H. Popescu, Milos Poliak et al.

Relevant research has investigated how predictive modeling algorithms, deep-learning-based sensing technologies, and big urban data configure immersive hyperconnected virtual spaces in digital twin cities: digital twin modeling tools, monitoring and sensing technologies, and Internet-of-Things-based decision support systems articulate big-data-driven urban geopolitics. This systematic review aims to inspect the recently published literature on digital twin simulation tools, spatial cognition algorithms, and multi-sensor fusion technology in sustainable urban governance networks. We integrate research developing on how blockchain-based digital twins, smart infrastructure sensors, and real-time Internet of Things data assist urban computing technologies. The research problems are whether: data-driven smart sustainable urbanism requires visual recognition tools, monitoring and sensing technologies, and simulation-based digital twins; deep-learning-based sensing technologies, spatial cognition algorithms, and environment perception mechanisms configure digital twin cities; and digital twin simulation modeling, deep-learning-based sensing technologies, and urban data fusion optimize Internet-of-Things-based smart city environments. Our analyses particularly prove that virtual navigation tools, geospatial mapping technologies, and Internet of Things connected sensors enable smart urban governance. Digital twin simulation, data visualization tools, and ambient sound recognition software configure sustainable urban governance networks. Virtual simulation algorithms, deep learning neural network architectures, and cyber-physical cognitive systems articulate networked smart cities. Throughout January and March 2023, a quantitative literature review was carried out across the ProQuest, Scopus, and Web of Science databases, with search terms comprising “sustainable urban governance networks” + “digital twin simulation tools”, “spatial cognition algorithms”, and “multi-sensor fusion technology”. A Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow diagram was generated using a Shiny App. AXIS (Appraisal tool for Cross-Sectional Studies), Dedoose, MMAT (Mixed Methods Appraisal Tool), and the Systematic Review Data Repository (SRDR) were used to assess the quality of the identified scholarly sources. Dimensions and VOSviewer were employed for bibliometric mapping through spatial and data layout algorithms. The findings gathered from our analyses clarify that Internet-of-Things-based smart city environments integrate 3D virtual simulation technology, intelligent sensing devices, and digital twin modeling.

Арьяти Арьяти , Джунаиди Джунаиди , Рэнди Ариядита Путра

During the COVID-19 pandemic, most countries suffered economically. Financial institutions play an important role in enhancing economic growth through intermediation. However, preliminary studies focused on common aspects of financial institutions rather than the banking context, and the majority of the literature was written prior to the COVID-19 pandemic. This study examines the banking sector’s role in short-run and long-run contributions to economic growth from 2009 to 2021. Indicators of the number of banking deposits, offices and public financing were used as proxies to validate the relationship between Indonesian financial development and economic growth (gross domestic product) in the vector error correction model (VECM). The Indonesian bank’s contribution to the country’s economic growth was examined. Data were collected from banks’ annual reports. This study found a strong short- and long-term correlation between financial development and Indonesia’s economic growth. There is a bidirectional relationship between Indonesia’s Islamic Bank (IIB) and GDP. The relationship between the conventional bank and Indonesia’s economic growth is unidirectional. Therefore, policymakers should enhance the intensified mobilisation of loans obtained for capital and productive projects. This study also shows that macroeconomic and microeconomic stability can be improved by enhancing capital inflows and investments in lucrative sectors, as the research goal was to examine the effect of financial development before and after the COVID-19 pandemic, which detriments most countries’ stability. However, future studies need to confirm banks’ contributions to specific sectors such as agriculture and small and medium enterprises due to their strong correlation with developing countries.

Michał Kura, Aleksandra Płonka

The current macroeconomic and political conditions, and above all technological progress, mean that in recent years the condition of the cooperative banking sector has been gradually deteriorating compared to commercial banks. The above premises give grounds to claim that the current operating model of the entire group of cooperative banks, as well as of a single cooperative bank, should be redefined. Remaining with the current models of operation of cooperative banks will maintain the trend of sector consolidation, departure from the idea of cooperatives and moving towards models attributed to commercial banks. The aim of the study was therefore an attempt to present one of the possible concepts for the development of a small cooperative bank as an alternative to the consolidation processes of the cooperative banking sector. An attempt was made to answer the question whether there is space for the functioning of a boutique cooperative bank and what a boutique cooperative bank is and what it can be. The focus was on presenting and transferring the concept of a boutique bank to cooperative banks. Examples of the functioning of boutique financial institutions were presented, which were an inspiration to build a model of a boutique cooperative bank. Solutions were analyzed and various variants of the functioning of commercial boutique banks were discussed as examples of the real success of this form of banking activity. On this basis, an attempt was made to present models of functioning of a boutique cooperative bank.

Barry Eichengreen, Ganesh Viswanath-Natraj

Stablecoins and central bank digital currencies are on the horizon in Asia, and in some cases have already arrived. This paper provides new analysis and a critique of the use case for both forms of digital currency. It provides time-varying estimates of devaluation risk for the leading stablecoin, Tether, using data from the futures market. It describes the formidable obstacles to widespread use of central bank digital currencies in cross-border transactions, the context in which their utility is arguably greatest. The bottom line is that significant uncertainties continue to dog the region's digital currency initiatives.

Román Acosta, Josué Cortés

This paper investigates the relationship between expansionary credit events and firms’ employment decisions. To overcome the endogeneity coming from the supply side of credit we exploited the legal and political framework in Mexico to examine the effects of local governments’ prepayment of loans, a situation that leads banks to channel newfound liquidity in firms. Analysis of a novel data set covering a 10-year period showed that a 1-standard-deviation increase in the issuance of new loans increases firms’ employment by 2.57 percentage points. Timing of the boost in employment varies, with smaller firms reacting immediately and larger firms reacting four months later. The effects are driven by firms in the manufacturing sector. Our results highlight the importance of the bank lending channel to stimulate employment in the short term, especially for smaller firms. Further, our estimates suggest that the effect of credit on employment could be amplified with policies that promote a more competitive corporate loan market.