Hasil untuk "Banking"

X. Luo, Han Li, Jie Zhang et al.

Chian-Son Yu

Hsiu-Fen Lin

B. Chong, Ming-Hua Liu

P. Andrés, Eleuterio Vallelado

We use a sample of large international commercial banks to test hypotheses on the dual role of boards of directors. We use a suitable econometric model (two step system estimator) to solve the well-known endogeneity problem in corporate governance literature, and demonstrate the empirical and theoretical superiority of system estimator over OLS and within estimators. We find an inverted U-shaped relation between bank performance and board size, and between the proportion of non-executive directors and performance. Our results show that bank board composition and size are related to directors' ability to monitor and advise management, and that larger and not excessively independent boards might prove more efficient in monitoring and advising functions, and create more value. All of these relations hold after we control for the measure of performance, the weight of the banking industry in each country, bank ownership, and regulatory and institutional differences.

Allen N. Berger, Emilia Bonaccorsi di Patti

K. Stiroh

P. Hanafizadeh, M. Behboudi, Amir Abedini Koshksaray et al.

Haibo Wang, Jun Huang, Lutfu S Sua et al.

The 2023 U.S. banking crisis propagated not through direct financial linkages but through a high-frequency, information-based contagion channel. This paper moves beyond exploration analysis to test the "too-similar-to-fail" hypothesis, arguing that risk spillovers were driven by perceived similarities in bank business models under acute interest rate pressure. Employing a Time-Varying Parameter Vector Autoregression (TVP-VAR) model with 30-day rolling windows, a method uniquely suited for capturing the rapid network shifts inherent in a panic, we analyze daily stock returns for the four failed institutions and a systematically selected peer group of surviving banks vulnerable to the same risks from March 18, 2022, to March 15, 2023. Our results provide strong evidence for this contagion channel: total system connectedness surged dramatically during the crisis peak, and we identify SIVB, FRC, and WAL as primary net transmitters of risk while their perceived peers became significant net receivers, a key dynamic indicator of systemic vulnerability that cannot be captured by asset-by-asset analysis. We further demonstrate that these spillovers were significantly amplified by market sentiment (as measured by the VIX) and economic policy uncertainty (EPU). By providing a clear conceptual framework and robust empirical validation, our findings confirm the persistence of systemic risks within the banking network and highlight the importance of real-time monitoring in strengthening financial stability.

Wei Minn, Phong Phan, Vikas K. Malviya et al.

Android banking applications have revolutionized financial management by allowing users to perform various financial activities through mobile devices. However, this convenience has attracted cybercriminals who exploit security vulnerabilities to access sensitive financial data. FjordPhantom, a malware identified by our industry collaborator, uses virtualization and hooking to bypass the detection of malicious accessibility services, allowing it to conduct keylogging, screen scraping, and unauthorized data access. This malware primarily affects banking and finance apps across East and Southeast Asia region where our industry partner's clients are primarily based in. It requires users to be deceived into installing a secondary malicious component and activating a malicious accessibility service. In our study, we conducted an empirical study on the susceptibility of banking apps in the region to FjordPhantom, analyzed the effectiveness of protective measures currently implemented in those apps, and discussed ways to detect and prevent such attacks by identifying and mitigating the vulnerabilities exploited by this malware.

Abdou Karim Kandji, Frédéric Precioso, Cheikh Ba et al.

Intent classification models have made a significant progress in recent years. However, previous studies primarily focus on high-resource language datasets, which results in a gap for low-resource languages and for regions with high rates of illiteracy, where languages are more spoken than read or written. This is the case in Senegal, for example, where Wolof is spoken by around 90\% of the population, while the national illiteracy rate remains at of 42\%. Wolof is actually spoken by more than 10 million people in West African region. To address these limitations, we introduce the Wolof Banking Speech Intent Classification Dataset (WolBanking77), for academic research in intent classification. WolBanking77 currently contains 9,791 text sentences in the banking domain and more than 4 hours of spoken sentences. Experiments on various baselines are conducted in this work, including text and voice state-of-the-art models. The results are very promising on this current dataset. In addition, this paper presents an in-depth examination of the dataset's contents. We report baseline F1-scores and word error rates metrics respectively on NLP and ASR models trained on WolBanking77 dataset and also comparisons between models. Dataset and code available at: https://github.com/abdoukarim/wolbanking77.

Victor Cardenas

The document discusses the financial climate risk in the context of the banking industry, emphasizing the need for a comprehensive understanding of climate change across different spatial and temporal scales. It highlights the challenges in estimating physical and transition risks, specifically extreme events and limitations of current climate models. The document also reviews current gaps in assessing physical and transition risks, including the development, improvement of modeling frameworks, highlighting the need for detailed databases of exposed physical assets and climatic hazard modeling. It also emphasizes the importance of integrating financial climate risks into financial risk management practices, particularly in smaller banks and lending organizations.

Alioune Diallo, Aicha War, Moustapha Awwalou Diouf et al.

The West African Economic and Monetary Union (WAEMU) states, characterized by widespread smartphone usage, have witnessed banks and financial institutions introducing mobile banking applications (MBAs). These apps empower users to perform transactions such as money transfers, bill payments, and account inquiries anytime, anywhere. However, this proliferation of MBAs also raises significant security concerns. Poorly implemented security measures during app development can expose users and financial institutions to substantial financial risks through increased vulnerability to cyberattacks. Our study evaluated fifty-nine WAEMU MBAs using static analysis techniques. These MBAs were collected from the 160 banks and financial institutions of the eight WAEMU countries listed on the Central Bank of West African States (BCEAO) website. We identified security-related code issues that could be exploited by malicious actors. We investigated the issues found in the older versions to track their evolution across updates. Additionally, we identified some banks from regions such as Europe, the United States, and other developing countries and analyzed their mobile apps for a security comparison with WAEMU MBAs. Key findings include: (1) WAEMU apps exhibit security issues introduced during development, posing significant risks of exploitation; (2) Despite frequent updates, underlying security issues often persist; (3) Compared to MBAs from developed and developing countries, WAEMU apps exhibit fewer critical security issues; and (4) Apps from banks that are branches of other non-WAEMU banks often inherit security concerns from their parent apps while also introducing additional issues unique to their context. Our research underscores the need for robust security practices in WAEMU MBAs development to enhance user safety and trust in financial services.

Dzhambulat Mollaev, Alexander Kostin, Maria Postnova et al.

Financial organizations collect a huge amount of temporal (sequential) data about clients, which is typically collected from multiple sources (modalities). Despite the urgent practical need, developing deep learning techniques suitable to handle such data is limited by the absence of large open-source multi-source real-world datasets of event sequences. To fill this gap, which is mainly caused by security reasons, we present the first industrial-scale publicly available multimodal banking dataset, MBD, that contains information on more than 2M corporate clients of a large bank. Clients are represented by several data sources: 950M bank transactions, 1B geo position events, 5M embeddings of dialogues with technical support, and monthly aggregated purchases of four bank products. All entries are properly anonymized from real proprietary bank data, and the experiments confirm that our anonymization still saves all significant information for introduced downstream tasks. Moreover, we introduce a novel multimodal benchmark suggesting several important practical tasks, such as future purchase prediction and modality matching. The benchmark incorporates our MBD and two public financial datasets. We provide numerical results for the state-of-the-art event sequence modeling techniques including large language models and demonstrate the superiority of fusion baselines over single-modal techniques for each task. Thus, MBD provides a valuable resource for future research in financial applications of multimodal event sequence analysis. HuggingFace Link: https://huggingface.co/datasets/ai-lab/MBD Github Link: https://github.com/Dzhambo/MBD

Tingmin Wu, Shuiqiao Yang, Shigang Liu et al.

Threat modeling is a crucial component of cybersecurity, particularly for industries such as banking, where the security of financial data is paramount. Traditional threat modeling approaches require expert intervention and manual effort, often leading to inefficiencies and human error. The advent of Large Language Models (LLMs) offers a promising avenue for automating these processes, enhancing both efficiency and efficacy. However, this transition is not straightforward due to three main challenges: (1) the lack of publicly available, domain-specific datasets, (2) the need for tailored models to handle complex banking system architectures, and (3) the requirement for real-time, adaptive mitigation strategies that align with compliance standards like NIST 800-53. In this paper, we introduce ThreatModeling-LLM, a novel and adaptable framework that automates threat modeling for banking systems using LLMs. ThreatModeling-LLM operates in three stages: 1) dataset creation, 2) prompt engineering and 3) model fine-tuning. We first generate a benchmark dataset using Microsoft Threat Modeling Tool (TMT). Then, we apply Chain of Thought (CoT) and Optimization by PROmpting (OPRO) on the pre-trained LLMs to optimize the initial prompt. Lastly, we fine-tune the LLM using Low-Rank Adaptation (LoRA) based on the benchmark dataset and the optimized prompt to improve the threat identification and mitigation generation capabilities of pre-trained LLMs.

Rania Ben Belgacem, Anis Ben Amar , Valerio Brescia

This study conducts a comparative analysis of the relationship between Corporate Social Responsibility (CSR) and financial performance in Tunisian banks. The research focuses on an extensive sample of Tunisian banks operating between 2018 and 2022. Two models are employed: one based on Return on Equity (ROE) and the other on Return on Assets (ROA). The findings reveal that Islamic banks benefit from robust CSR practices, leading to enhanced ROA and aligning with ethical principles inherent in Islamic finance. In contrast, conventional banks demonstrate no significant correlation between CSR and ROE and exhibit a negative impact of CSR on ROA. These results underscore the sector-specific nuances of CSR and its influence on financial performance, highlighting the necessity for customized CSR strategies. The study offers valuable insights for banking professionals, policymakers, and stakeholders, aiding their comprehension of the role of CSR in shaping financial outcomes in distinct banking sectors.

Kamal Sulaiman Ibrahim, Shkur Majeed Hameed, Kosret Muhemed Ebabekir

      The Banks have an energetic role in the field of business and financing, human resource in this field is an important factor for success. So, it is necessary to know the market in which the supplier or banking labor can be obtained and qualified to work and perform banking activities with the standards and characteristics required in the banking field. This research is trying to detarmine the role of academic Institutions in meeting the needs of the banking market. To reach the aims of the study, a questionnaire form distributed on academic leaders in the Universities of Erbil from the specializations of the departments of Business Administration, Accounting, Financial and Banking Sciences. They are returned (60) leaders, (46) questionnaire returned, that is (76%) of them. Used statistical software (SPSS) to analyze the practical part of the study.The study got number of results, appeared that academic media play a key role in qualifying human cadres for the banking market, as well as Universities and Educational Institutes can provide students necessity knowledge and developIng technical skills to work in banking sector and there is impact of Academic Institutions and its dimension on Banks Labour Market. The Study suggests that Universities need to focus on the dimension of education and academic leaders should adapting renewable environment and banking needs and using modern technologies in the teaching process suitable for banking.

Howard Chitimira, Elfas Torerai, Lisa Jana

Abstract Money laundering and financial crimes pose a significant threat to the integrity and stability of South Africa’s financial system. This paper explores the application of artificial intelligence (AI) to detect and prevent money laundering in South African banking institutions. Through the implementation of big data technologies and data processing analytics, AI can enhance the detection and prevention of money laundering activities in South Africa’s banking sector. AI can be harnessed to improve the detection of suspicious activities, enhance accuracy of financial intelligence and adapt to evolving money laundering techniques. The paper also examines the benefits and challenges of implementing AI as an anti-money laundering (AML) measure in the South African banking sector. These include the need for quality data, integration with existing systems, regulatory compliance and ethical considerations. The paper further highlights the potential of AI in transaction monitoring, customer due diligence, outcomes-based risk assessment, and improved detection of suspicious transactions by analysing how AI can enhance the effectiveness and efficiency of AML measures. The importance of coordination between banking institutions, regulatory authorities and law enforcement bodies is also highlighted as an important component of leveraging AI to combat money laundering and related financial crimes in South Africa’s banking sector.

Halyna Kryshtal , Viktoriia Tomakh , Tetiana Ivanova et al.

The study is aimed at summarizing the processes of eco-innovative (green) transformation of urban infrastructure and researching possible prospects for the development of Ukraine in this context. In the course of the research, the possibilities of "green" transformation of urban infrastructure were considered and it was noted that the use of the principles of eco-innovative transformation in the post-war period can only take place under the condition of proper planning, state support and the creation of favourable market conditions. The authors noted that the success of such a transformation requires the establishment of green goals in all aspects of the development of Ukrainian cities. Auto-frame considered the financial possibilities of the development of urban infrastructure and proposed the location of support offices for the eco-innovative transformation of urban infrastructure at the regional level. The principles of achieving eco-innovative transformation of urban infrastructure are revealed, namely maximum energy efficiency, energy transition, "zero waste", environmental sustainability of buildings, adaptation to climate change, popularization of a green lifestyle, resource conservation, citizen involvement and circular economy. It is proposed to create a platform that would unite architects, builders, urban planners, citizens, artists and other interested persons. This platform should contribute to the search for answers to the question of how to ensure a quick, ecological, attractive and safe "green" transformation of urban infrastructure. Ukraine should cooperate with the European Union within various green platforms and networks that help cities in green transformation. All the above-mentioned tools and solutions should contribute to the creation of green, sustainable and people-oriented cities in Ukraine. The authors have considered the possibilities of financing the restoration of Ukrainian cities after the destruction in terms of the necessary financial resources, donor countries, and reconstruction expenditures.

Yusuf Olamilekan Quadri, Ayorinde Olutimi Akinwumi, Kudirat Mopelola Malik-Abdulmajeed et al.

Background: The inability to achieve optimum financial inclusion in Nigeria has necessitated the review of various policies and instruments meant to reduce the level of financial exclusion. Objective: Hence this study investigates the impact of agent banking and mobile money operation on financial inclusion in Nigeria, focusing on the supply side. Method: Descriptive research design was adopted and secondary data ranging from 2013 to 2021 were obtained from the World Bank Global Financial database and e-payment statistics of the Central Bank of Nigeria. Ordinary least squares repression was used to analyse the data. Results: Findings revealed that at 5% significance level, point-of-sale and mobile money operations have a positive impact on financial inclusion while web/internet banking plays a limited role in achieving financial inclusion. Conclusion: The study concluded that both agent banking and mobile money operations impact on financial inclusion in Nigeria; the study, therefore, recommends that more off-site automated teller machine and licensed agents should be encouraged to cater for the rural residents and ultimately improve financial inclusion. Keywords: Agent Banking, Financial inclusion, Mobile Money Operation