Hasil untuk "Banking"

Luis Rodrigo Arnabal, Santiago Camara, Cecilia Dassatti

This paper studies how shocks to global banks' net worth transmit to Emerging Market Economies. Using the identification strategy of Ottonello and Song (2022), which isolates high-frequency surprises to banks' credit supply capacity, we show that positive shocks appreciate local currencies, lower external borrowing costs, increase capital flows to domestic banking sectors, and raise investment, credit, and real activity across EMEs. These effects are highly robust across specifications and samples. Using administrative credit-registry data from Uruguay, we find that better capitalized banks transmit global credit easing more strongly. At the firm level, responses are weaker for more leveraged firms, especially those with foreign-currency debt, short maturities, or collateral not priced to market.

Alina Kudinova, Oleksandra Maslii, Valerii Smokvina et al.

The primary objective of this study is to identify the various cyber threats that impact the operations of financial institutions, particularly their information and economic security. This is crucial for the sustainable growth of the country and has a direct effect on its economic security. The financial sector globally experiences the highest losses due to cyber incidents. On average, financial organizations around the world incur losses of approximately USD 5.9 million per incident, which is higher than the average loss across all industries, estimated at USD 4.45 million. Financial institutions incur losses not only from ransom payments to prevent the disclosure of stolen data and the costs associated with restoring infrastructure after ransomware attacks, but also from direct financial losses in certain situations. This study identified the most common types of cyberattacks, examined their impact on the operations of financial institutions, and suggested ways to respond to and prevent such incidents. For the first time, an algorithm for the strategic management of digitalization in financial institutions was proposed, aimed at enhancing their economic and informational security. The algorithm can be implemented at all managerial levels to reduce the influence of subjective risk factors. Additionally, a multifactor predictive model has been developed and substantiated, which represents a further development of existing approaches to assessing information and economic security in financial institutions. This model integrates internal (organization-controlled) and external (environmental) factors and utilizes statistical methods and machine learning techniques to analyze data and forecast security levels. As digitalization continues to evolve in our country, financial institutions must adapt and embrace innovation to ensure sustainable development, even under martial law.

Kumar Rahul, Shieh Chin-Shiuh, Chakrabarti Prasun et al.

Federated Learning (FL) transformed decentralized machine learning by allowing joint model training without mutually sharing raw data, hence being especially useful in privacy-sensitive applications like healthcare, e-commerce, and finance. Even with its privacy-focused architecture, FL is vulnerable to a range of security attacks such as data poisoning, model inversion, membership inference attacks, and communication interception. These attacks compromise the confidentiality of patients in healthcare, consumer data privacy in e-commerce, and financial safety in banking, thus necessitating effective privacy-preserving mechanisms. This survey presents a classification of security threats in FL, grouping them by their source, effect, and attack mode. We review state-of-the-art countermeasures, such as differential privacy, secure multi-party computation, homomorphic encryption, and resilient aggregation methods, their effectiveness, trade-offs, and real-world applicability to FL. In medicine, FL enables joint disease diagnosis without compromising patient confidentiality; in online shopping, it provides personalized suggestions without revealing customer tastes; and in banking, it improves fraud detection without violating regulatory requirements. In addition, we discuss future horizons in privacy-preserving FL, including adversarial robustness, blockchain-protected models, and tailored FL architectures, improving security and resiliency in these domains. We also discuss the balancing problems between security, accuracy, and computational efficiency with possible trade-offs in scaling privacy-preserving FL By analyzing threats and mitigation strategies systematically, this paper will provide direction to future research on designing secure, scalable, and privacy-preserving FL frameworks for the changing healthcare, e-commerce, and finance needs.

Nataliya Tkachenko

This paper examines the integration of AI's carbon footprint into the risk management frameworks (RMFs) of the banking sector, emphasising its importance in aligning with sustainability goals and regulatory requirements. As AI becomes increasingly central to banking operations, its energy-intensive processes contribute significantly to carbon emissions, posing environmental, regulatory, and reputational risks. Regulatory frameworks such as the EU AI Act, Corporate Sustainability Reporting Directive (CSRD), Corporate Sustainability Due Diligence Directive (CSDDD), and the Prudential Regulation Authority's SS1/23 are driving banks to incorporate environmental considerations into their AI model governance. Recent advancements in AI research, like the Open Mixture-of-Experts (OLMoE) framework and the Agentic RAG framework, offer more efficient and dynamic AI models, reducing their carbon footprint without compromising performance. Using these technological examples, the paper outlines a structured approach for banks to identify, assess, and mitigate AI's carbon footprint within their RMFs, including adopting energy-efficient models, utilising green cloud computing, and implementing lifecycle management.

F. Bolivar, Miguel A. Duran, A. Lozano-Vivas

This paper analyzes the relation between bank profit performance and business models. Using a machine learning-based approach, we propose a methodological strategy in which balance sheet components' contributions to profitability are the identification instruments of business models. We apply this strategy to the European Union banking system from 1997 to 2021. Our main findings indicate that the standard retail-oriented business model is the profile that performs best in terms of profitability, whereas adopting a non-specialized business profile is a strategic decision that leads to poor profitability. Additionally, our findings suggest that the effect of high capital ratios on profitability depends on the business profile. The contributions of business models to profitability decreased during the Great Recession. Although the situation showed signs of improvement afterward, the European Union banking system's ability to yield returns is still problematic in the post-crisis period, even for the best-performing group.

Shuochen Bi, Wenqing Bao

With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. In the current wave of digitalization, AI has become a key driving force for the strategic transformation of financial institutions, especially the banking industry. For commercial banks, the stability and safety of asset quality are crucial, which directly relates to the long-term stable growth of the bank. Among them, credit risk management is particularly core because it involves the flow of a large amount of funds and the accuracy of credit decisions. Therefore, establishing a scientific and effective credit risk decision-making mechanism is of great strategic significance for commercial banks. In this context, the innovative application of AI technology has brought revolutionary changes to bank credit risk management. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve realtime monitoring and early warning, helping banks intervene before risks occur and reduce losses.

Carlos Alberto Durigan Junior, André Taue Saito, Daniel Reed Bergmann et al.

The main objective of this paper is to Identify which macroe conomic factors and industrial indexes influenced the total Brazilian banking spread between March 2011 and March 2015. This paper considers subclassification of industrial activities in Brazil. Monthly time series data were used in multivariate linear regression models using Eviews (7.0). Eighteen variables were considered as candidates to be determinants. Variables which positively influenced bank spread are; Default, IPIs (Industrial Production Indexes) for capital goods, intermediate goods, du rable consumer goods, semi-durable and non-durable goods, the Selic, GDP, unemployment rate and EMBI +. Variables which influence negatively are; Consumer and general consumer goods IPIs, IPCA, the balance of the loan portfolio and the retail sales index. A p-value of 05% was considered. The main conclusion of this work is that the progress of industry, job creation and consumption can reduce bank spread. Keywords: Credit. Bank spread. Macroeconomics. Industrial Production Indexes. Finance.

Alexander А. Tkachev, Ruslan A. Karabashev, Viktor A. Nevdakh et al.

Purpose: to analyze the climatic characteristics of the study area and hydrological characteristics of the studied watercourse, including maximum water discharges in section lines marked along the river bed, as well as maximum water levels of the Abin River, in the volume necessary for a reasonable choice of design solutions for bank protection hydraulic structures. Materials and methods. The Abin River was studied from 1923 to 1989 at the water gage in Abinsk. Observation data allowed analyzing the regime, level and estimating the discharges. The average annual precipitation is 704 mm, with a peak in December (85 mm) and minimums in April and September (45 mm each). Heavy rains occur in the summer, with a maximum of 171 mm per day. Ice and rime phenomena occur in the area, but they are mostly short-lived. Extreme hydrometeorological phenomena are possible due to the orographic features of the region. Results. The Abin River is a mountain river with water level fluctuations of up to 8.6 m and the rising large wooden debris during floods. Calculations of maximum discharges and water levels were carried out, the average current velocity reaches 2.68–3.40 m/s, and the maximum depth is 7.54–7.64 m. Channel processes include bed erosion and limited meandering with a stable bank position. The bed erosion depth varies from 0.78 to 1.06 m. Conclusions. When developing bank protection structures on the Abin River, it is necessary to take into account several important aspects. The structures must withstand significant water discharges (at a speed of 2.68–3.40 m/s and a depth of 7.54–7.64 m BS) and ensure durability during floods. It is necessary to strengthen the foundation and reinforce the slopes using geosynthetics, and provide flexible structures (gabions and Reno mattresses) to adapt to channel changes. It is possible to use technologies that minimize interference with nature, such as anchor systems and the creation of bulkheads. It is recommended to use methods for the redistribution of sediments, including flooded spurs.

Mohammad Jafar Sharifi, Ling Xu, Nahid Nasiri et al.

Abstract Myeloid malignancies arise in bone marrow microenvironments and shape these microenvironments in favor of malignant development. Immune suppression is one of the most important stages in myeloid leukemia progression. Leukemic clone expansion and immune dysregulation occur simultaneously in bone marrow microenvironments. Complex interactions emerge between normal immune system elements and leukemic clones in the bone marrow. In recent years, researchers have identified several of these pathological interactions. For instance, recent works shows that the secretion of inflammatory cytokines such as tumor necrosis factor‐α (TNF‐α), from bone marrow stromal cells contributes to immune dysregulation and the selective proliferation of JAK2V617F+ clones in myeloproliferative neoplasms. Moreover, inflammasome activation and sterile inflammation result in inflamed microenvironments and the development of myelodysplastic syndromes. Additional immune dysregulations, such as exhaustion of T and NK cells, an increase in regulatory T cells, and impairments in antigen presentation are common findings in myeloid malignancies. In this review, we discuss the role of altered bone marrow microenvironments in the induction of immune dysregulations that accompany myeloid malignancies. We also consider both current and novel therapeutic strategies to restore normal immune system function in the context of myeloid malignancies.

Salisu Garba Abdullahi, Ajibu Jonas, Riliwan Olalekan Olanrewaju et al.

Macroeconomics and finance drive bond markets in developing countries, allowing governments to raise money for businesses and infrastructure. However, many factors in developing countries like Nigeria hinder the growth of the bond market. This study investigates a novel contribution by focusing exclusively on the Nigerian bond market and considering a set of macroeconomic drivers that have not been studied collectively. The study applies the Autoregressive Distributive Lag (ARDL) model to examine the short-run dynamics between key macrofinancial drivers and the Nigerian bond market. The findings show that an increase in fiscal deficit does not support the development of the bond market in Nigeria. Similar results are found for GDP per capita, inflation, interest rates, and banking scale; all negatively affect bond market development. However, domestic debt and stock market development positively promote bond market development. The policy implications offered from these findings are to redirect their spending to projects that have the potential to stimulate economic activities that help the government generate more revenue. Policymakers should also cut unnecessary spending on recurrent expenditure, which is a significant part by implementing efficient fiscal discipline.

Kurada T S S Satyanarayana, Addada Narasimha Rao, Kumpatla jaya surya

This research investigates the multifaceted relationship underlying capital structure dynamics along with financial performance as a result of mergers and acquisitions, or M&As, in Indian banks. In the face of increasing competition, banks have deliberately embraced M&A as a strategy of improving commercial prospects and maintaining financial stability. The primary goal of this study is to examine the changes in the capital framework and financial results of banks before and after M&A transactions. The investigation, which employs a paired t-test as a method of statistical analysis, is based on a review of annual reports from selected banks over a two-year period before and after M&A transactions. The paired t-test approach allows for a thorough statistical analysis of interconnected datasets, revealing the subtle influence of M&A attempts on both bank financial performance as well as capital structure dynamics. The study's findings have the potential to add to the current body of knowledge on organisational planning, managing finances, and capital structure optimisation. The research has practical significance for financial companies, legislators, and scholars interested in understanding the profound effects of M&A inside the arena of financial institutions that operate within fiercely competitive landscapes because it provides comprehensive insights regarding the complex consequences of banking merger and acquisition (M&A) deals on capital structure as well as financial performance. Finally, the goal of this research is to provide the banking sector with educated decision-making capabilities and strategic guidance to businesses facing heightened competition while coping with the complexities of capital structure.

Vladimir Njegomir, Dragan Stojić, Jelena Demko-Rihter

Research Question: What type of mutual relations exist between business insurance and entrepreneurship, and how do they impact economic growth? Motivation: The primary motive for the research is the identified literature gap in the field of interdependence between business insurance and entrepreneurship and their joint contribution to economic growth. Idea: To test whether the interdependence between business insurance and entrepreneurship exists and their combined contribution to economic growth. The following independent variables were used: premium per entrepreneur, solved claims per entrepreneur, total technical reserves, GDP/p.c., a measure of demographic variables in the form of education level, a measure of the impact of banking, and the measure for institutional factors in terms of establishment costs the number of entrepreneurs by years as the dependent variable was considered. Data: Data were gathered from various sources (Serbian Statistical Office, National Bank of Serbia, World Bank) in the period from 2008 to 2019. Tools: Descriptive statistics, regression analysis, and statistical tests. Findings: The presented results show that there is a significant influence of insurance, both through insurance premiums per entrepreneur, which are paid to ensure the safety of the entrepreneur, and through resolved claims per entrepreneur, which present an indicator of insurance compensation in the case of damage to the entrepreneur, and through the impact of insurance on financial market by the amount of technical reserves. The return influence was not confirmed, considering that the number of entrepreneurs per year is a stationary variable, so the effect of the number of entrepreneurs on the development of insurance, measured by the premium per entrepreneur, could not be confirmed. Contribution: This research conducted in Serbia, a developing and upper middle-income country, confirmed the positive impact of insurance on entrepreneurship, but the return influence of entrepreneurship on insurance was not proven.

إبراهيم قاضي محمد زاهد

إن رعاية البيئة في الإسلام جاءت بصورة شاملة لجميع عناصرها، واليوم ومع هذا التطور الهائل والتقدم التكنولوجي، أصبحت قضايا البيئة في مقدمة اهتمام المجتمعات الحديثة نظراً لتدهور الحياة البرية والبحرية، ومن بين الدول التي برزت فيها المشكلات البيئية جمهورية بنغلاديش الشعبية والتي ازداد فيها حد التلوث والفساد البيئي، فجاء هذا البحث بعنوان: (معالجة الفساد البيئي من منظور الاقتصاد الاسلامي مع دراسة الواقع البنغلاديشي) لإبراز الدور الإسلامي الحضاري في رعاية البيئة وحمايتها، وكيفية معالجة ومواجهة الفساد البيئي والحد من التلوث، عبر التوجيهات والتدابير الشرعية التي تعتبر كفيلة بحفظ التوازن البيئي ومواجهة تحدياتها، ثم الكشف عن الفساد البيئي في بنغلاديش وأسبابه وسبل مواجهته، مع بيان التوجيهات المقترحة لمعالجة التلوث البيئي في بنغلاديش، متتبعًا في ذلك المنهج الوصفي، وتوصل البحث إلى أن حماية البيئة أصبحت قضية عالمية تهم كل البشر، وأن تطبيق التوجيهات والتشريعات الإسلامية السامية مآله تنمية اقتصادية مستدامة وخضراء، فبالتالي يجب تطبيق القواعد الفقهية في القضايا البيئية، لتحقيق رعاية البيئة وحمايتها، وتعزيز التمويل الأخضر الإسلامي، والتوسع في الصناعات الخضراء، مع وضع برامج في القيم البيئية، وضرورة اتخاذ تدابير قوية لمنع التدهور البيئي والتلوث.

Ronald Heijmans, Froukelien Wendt

Large banks and critical financial market infrastructures (FMIs) that are not able to fulfill their payment obligations, for example following a bankruptcy or cyber-attack, can be a source of financial instability and contagion in the financial system. This paper develops a composite risk indicator to evaluate the criticality of participants in a large value payment system network, combining liquidity risk (i.e. size of incoming and outgoing payments) and systemic impact or interconnections between network participants in one approach. It is applied, as a proof of concept, to the TARGET2 payment system that links banks and FMIs in a tight network of interdependencies. We find that the most critical participants in TARGET2 are other payment systems (large value and retail) because of the underlying gross size of their payment flows. Some banks may be critical, but this is mainly due to their interconnectedness with other TARGET2 participants. Central counterparties and central securities depositories are less critical to the payment system. Our findings can be used by (1) financial stability experts to evaluate the impact of a failing critical participant in the financial system, and (2) central banks in their role as payment system operator and overseer. Besides, it feeds into policy discussions on payment system access, oversight, and crisis management.

Intan Kusuma Pratiwi

Purpose — This research aims to identify the factors that influence customers in using digital Islamic banking services by modifying the UTAUT2 model to include Perceived Credibility and Perceived Risk variables. Method — This research employs a quantitative approach to test and validate the hypotheses formulated. The study population consists of Islamic bank customers in Indonesia. For sample selection, a purposive sampling technique was employed, with the inclusion criterion being that respondents must be Islamic bank customers who have utilized digital Islamic banking services. Data were collected from 373 Islamic bank customers through online Google Forms. The data analysis technique utilized in this research is the Partial Least Squares (PLS) method, conducted using SmartPLS software. Result — The research results indicate that nearly all UTAUT2 variables significantly impact customers' adoption of digital Islamic banking services. Specifically, Perceived Credibility significantly influences customers' adoption of these services, and similarly, Perceived Risk significantly affects customers' adoption of digital Islamic banking services. Contribution — This research introduces a novel framework by modifying the UTAUT2 model, incorporating the variables of Perceived Credibility and Perceived Risk as extensions to the UTAUT2 model.

Williams Haruna, Toyin Ajiboro Aremu, Yetunde Ajao Modupe

Cyber security threats to the payment and banking system have become a worldwide menace. The phenomenon has forced financial institutions to take risks as part of their business model. Hence, deliberate investment in sophisticated technologies and security measures has become imperative to safeguard against heavy financial losses and information breaches that may occur due to cyber-attacks. The proliferation of cyber crimes is a huge concern for various stakeholders in the banking sector. Usually, cyber-attacks are carried out via software systems running on a computing system in cyberspace. As such, to prevent risks of cyber-attacks on software systems, entities operating within cyberspace must be identified and the threats to the application security isolated after analyzing the vulnerabilities and developing defense mechanisms. This paper will examine various approaches that identify assets in cyberspace, classify the cyber threats, provide security defenses and map security measures to control types and functionalities. Thus, adopting the right application to the security threats and defenses will aid IT professionals and users alike in making decisions for developing a strong defense-in-depth mechanism.

Zheng Zhang, Yingsheng Ji, Jiachen Shen et al.

Risk assessment is a substantial problem for financial institutions that has been extensively studied both for its methodological richness and its various practical applications. With the expansion of inclusive finance, recent attentions are paid to micro and small-sized enterprises (MSEs). Compared with large companies, MSEs present a higher exposure rate to default owing to their insecure financial stability. Conventional efforts learn classifiers from historical data with elaborate feature engineering. However, the main obstacle for MSEs involves severe deficiency in credit-related information, which may degrade the performance of prediction. Besides, financial activities have diverse explicit and implicit relations, which have not been fully exploited for risk judgement in commercial banks. In particular, the observations on real data show that various relationships between company users have additional power in financial risk analysis. In this paper, we consider a graph of banking data, and propose a novel HIDAM model for the purpose. Specifically, we attempt to incorporate heterogeneous information network with rich attributes on multi-typed nodes and links for modeling the scenario of business banking service. To enhance feature representation of MSEs, we extract interactive information through meta-paths and fully exploit path information. Furthermore, we devise a hierarchical attention mechanism respectively to learn the importance of contents inside each meta-path and the importance of different metapahs. Experimental results verify that HIDAM outperforms state-of-the-art competitors on real-world banking data.

Scott W. Hegerty

A lack of financial access, which is often an issue in many central-city U.S. neighborhoods, can be linked to higher interest rates as well as negative health and psychological outcomes. A number of analyses of "banking deserts" have also found these areas to be poorer and less White than other parts of the city. While previous research has examined specific cities, or has classified areas by population densities, no study to date has examined a large set of individual cities. This study looks at 319 U.S. cities with populations greater than 100,000 and isolates areas with fewer than 0.318 banks per square mile based on distances from block-group centroids. The relative shares of these "deserts" appears to be independent of city population across the sample, and there is little relationship between these shares and socioeconomic variables such as the poverty rate or the percentage of Black residents. One plausible explanation is that only a subset of many cities' poorest, least White block groups can be classified as banking deserts; nearby block groups with similar socioeconomic characteristics are therefore non-deserts. Outside of the Northeast, non-desert areas tend to be poorer than deserts, suggesting that income- and bank-poor neighborhoods might not be as prevalent as is commonly assumed.

Geoffrey Goodell, Hazem Danny Al Nakib

The People's Bank of China (PBOC) has launched an ambitious project to develop a digital currency for use in domestic, retail transactions, and is, by far, the most advanced globally in this regard. In addition to involving a diverse set of stakeholders, the PBOC established a set of fundamental principles, including privacy, inclusiveness, and conservatism, and has articulated its progress in a public document translated into English. We maintain that although both its first principles and its conclusions drawn from the research conducted by the PBOC from 2014 to date are broadly reasonable and appropriate, the PBOC has also missed some important considerations and entertained some questionable assumptions, which many central banks around the world have also done. In this analysis, we consider the strengths and weaknesses of the digital currency proposition articulated by the PBOC as it exists today, and we propose one fundamental and specific change for the PBOC and other central banks around the world: The architecture must accommodate privacy-preserving, non-custodial wallets. With this change and a related set of minor adjustments, China has an opportunity to lead the world in the implementation of a central bank digital currency (CBDC) solution that protects the authority of the central bank to implement monetary policy, preserves the role of public-sector and private-sector banking institutions, promotes the efficiency of retail transactions and businesses, satisfies regulatory objectives, and safeguards the human rights of retail consumers, including their privacy and their right to participate in the economy. We hope that the PBOC, and other central banks around the world, will have the resolve and strength of purpose to implement our proposed change and carry on with implementing a CBDC architecture that serves the interests of its users.

Salman Mousaeirad

Customer Segmentation is the process of dividing customers into groups based on common characteristics. An intelligent Customer Segmentation will not only enable an organization to effectively allocate marketing resources (e.g., Recommender Systems in the Banking sector) but also it will enable identifying the customer cohorts that are most likely to benefit from a specific policy (e.g., to discover diverse patient groups in the Health sector). While there has been a significant improvement in approaches to Customer Segmentation, the main challenge remains to be the understanding of the reasons behind the segmentation need. This task is challenging as it is subjective and depends on the goal of segmentation as well as the analyst's perspective. To address this challenge, in this paper, we present an intelligent vector-based customer segmentation approach. The proposed approach will leverage feature engineering to enable analysts to identify important features (from a pool of features such as demographics, geography, psychographics, behavioral, and more) and feed them into a neural embedding framework named Customer2Vec. The Customer2Vec combines the neural network classification and clustering methods as supervised and unsupervised learning techniques to embed the customer vector. We adopt a typical scenario in the Banking Sector to highlight how Customer2Vec significantly improves the quality of the segmentation and detecting customer similarities.