Hasil untuk "Banking"

Zuherman Rustam, Sri Hartini, Sardar M. N. Islam et al.

Context: Financial system stability is determined by the condition of the banking system. A bank failure can destroy the stability of the financial system, as banks are subject to systemic risk, affecting not only individual banks but also segments or the entire financial system. Calculating the probability of a bank going bankrupt is one way to ensure the banking system is safe and sound. Existing literature and limitations: Statistical models, such as Altman's Z-Score, are one of the common techniques for developing a bankruptcy prediction model. However, statistical methods rely on rigid and sometimes irrelevant assumptions, which can result in low forecast accuracy. New approaches are necessary. Objective of the research: Bankruptcy models are developed using machine learning techniques, such as logistic regression (LR), random forest (RF), and support vector machines (SVM). According to several studies, machine learning is also more accurate and effective than statistical methods for categorising and forecasting banking risk management. Present Research: The commercial bank data are derived from the annual financial statements of 44 active banks and 21 bankrupt banks in Turkey from 1994 to 2004, and the rural bank data are derived from the quarterly financial reports of 43 active and 43 bankrupt rural banks in Indonesia between 2013 and 2019. Five rural banks in Indonesia have also been selected to demonstrate the feasibility of analysing bank bankruptcy trends. Findings and implications: The results of the research experiments show that RF can forecast data from commercial banks with a 90% accuracy rate. Furthermore, the three machine learning methods proposed accurately predict the likelihood of rural bank bankruptcy. Contribution and Conclusion: The proposed innovative machine learning approach help to implement policies that reduce the costs of bankruptcy.

Leonardo Aldo Alejandro Barberi, Linda Maria De Cave

This paper introduces advanced techniques of Topological Data Analysis (TDA) for unsupervised anomaly detection and customer segmentation in banking data. Using the Mapper algorithm and persistent homology, we develop unsupervised procedures that uncover meaningful patterns in customers' banking data by exploiting topological information. The framework we present in this paper yields actionable insights that combine the abstract mathematical subject of topology with real-life use cases that are useful in industry.

Alex Escalante Viteri, Javier Gamboa Cruzado, Leonidas Asto Huaman

Business intelligence in the banking industry has been studied extensively in the last decade; however, business executives still do not perceive efficiency in the decision-making process since the management and treatment of information are very timeconsuming for the deliverer, generating costs in the process. On the other hand, there is no formal methodology for developing business intelligence solutions in this sector. This work aims to optimize decision-making in a business unit that works with internet banking companies, reducing the time, the number of people, and the costs involved in decision-making. To meet the objective, basic and applied research was conducted. The basic research allowed the construction of a new methodology from a study of critical success factors and approaches from the business intelligence literature. The applied research involved the implementation of a business intelligence solution applying the new methodology in a pre-experimental study. Thirty decision-making processes were analyzed using pre-test and post-test data. Tools such as a stopwatch and observation were used to collect and record data on time spent, the number of people, and the decision-making costs. This information was processed in the specialized Minitab18 statistical software, which allowed the observation and confirmation of relevant results regarding time reduction, the number of people, and the costs generated. Therefore, it was concluded that the business intelligence solution, applying the new methodology, optimized decision making in the business unit that works with internet banking for companies.

Aufa Nasywa Rahman, Bimo Sunarfri Hantono, Guntur Dharma Putra

Open banking framework enables third party providers to access financial data across banking institutions, leading to unprecedented innovations in the financial sector. However, some open banking standards remain susceptible to severe technological risks, including unverified data sources, inconsistent data integrity, and lack of immutability. In this paper, we propose a layered architecture that provides assurance in data trustworthiness with three distinct levels of trust, covering source validation, data-level authentication, and tamper-proof storage. The first layer guarantees the source legitimacy using decentralized identity and verifiable presentation, while the second layer verifies data authenticity and consistency using cryptographic signing. Lastly, the third layer guarantees data immutability through the Tangle, a directed acyclic graph distributed ledger. We implemented a proof-of-concept implementation of our solution to evaluate its performance, where the results demonstrate that the system scales linearly with a stable throughput, exhibits a 100% validation rate, and utilizes under 35% of CPU and 350 MiB memory. Compared to a real-world open banking implementation, our solution offers significantly reduced latency and stronger data integrity assurance. Overall, our solution offers a practical and efficient system for secure data sharing in financial ecosystems while maintaining regulatory compliance.

Hemlata Sharma, Aparna Andhalkar, Oluwaseun Ajao et al.

Macroeconomic factors have a critical impact on banking credit risk, which cannot be directly controlled by banks, and therefore, there is a need for an early credit risk warning system based on the macroeconomy. By comparing different predictive models (traditional statistical and machine learning algorithms), this study aims to examine the macroeconomic determinants impact on the UK banking credit risk and assess the most accurate credit risk estimate using predictive analytics. This study found that the variance-based multi-split decision tree algorithm is the most precise predictive model with interpretable, reliable, and robust results. Our model performance achieved 95% accuracy and evidenced that unemployment and inflation rate are significant credit risk predictors in the UK banking context. Our findings provided valuable insights such as a positive association between credit risk and inflation, the unemployment rate, and national savings, as well as a negative relationship between credit risk and national debt, total trade deficit, and national income. In addition, we empirically showed the relationship between national savings and non-performing loans, thus proving the paradox of thrift. These findings benefit the credit risk management team in monitoring the macroeconomic factors thresholds and implementing critical reforms to mitigate credit risk.

Qiang Wan, Jun Cui

This paper explores the impact of banking fintech on reducing financial risks in the agricultural supply chain, focusing on the secondary allocation of commercial credit. The study constructs a three-player evolutionary game model involving banks, core enterprises, and SMEs to analyze how fintech innovations, such as big data credit assessment, blockchain, and AI-driven risk evaluation, influence financial risks and access to credit. The findings reveal that banking fintech reduces financing costs and mitigates financial risks by improving transaction reliability, enhancing risk identification, and minimizing information asymmetry. By optimizing cooperation between banks, core enterprises, and SMEs, fintech solutions enhance the stability of the agricultural supply chain, contributing to rural revitalization goals and sustainable agricultural development. The study provides new theoretical insights and practical recommendations for improving agricultural finance systems and reducing financial risks. Keywords: banking fintech, agricultural supply chain, financial risk, commercial credit, SMEs, evolutionary game model, big data, blockchain, AI-driven risk evaluation.

Ankur Agarwal, Shashi Prabha, Raghav Yadav

This paper explores the application of Exploratory Data Analytics (EDA) in the banking and finance domain, focusing on credit card usage and customer churning. It presents a step-by-step analysis using EDA techniques such as descriptive statistics, data visualization, and correlation analysis. The study examines transaction patterns, credit limits, and usage across merchant categories, providing insights into consumer behavior. It also considers demographic factors like age, gender, and income on usage patterns. Additionally, the report addresses customer churning, analyzing churn rates and factors such as demographics, transaction history, and satisfaction levels. These insights help banking professionals make data-driven decisions, improve marketing strategies, and enhance customer retention, ultimately contributing to profitability.

Dimas Pratomo, Muhammad Kurniawan, Nur Fitri Handayani

Introduction: In the distribution of financing, the implementation of green banking is outlined in green finance, which is one of the financing schemes or lending to environmentally friendly businesses. Green finance activities focus on risk mitigation in providing financing to sustainable development projects by considering the impact that these projects will have. Objectives: This research aims to find out how the application of green banking implementation analysis mitigates the risk of financing distribution. Method: This research is qualitative research of descriptive type. The data sources in this study are the BSI and BRI Sustainability Reports for the 2021-2022 period. The data collection technique used is the library method. Results: The results of this study are seen based on a comparative study conducted between Bank Syariah Indonesia and Bank Rakyat Indonesia, there are differences and similarities between the two banks. One of the differences between the two banks lies in terms of bank monitoring of the company being financed, BSI monitors its business once every three months while BRI monitors it once a year. In addition, there are also similarities between the two banks, one of the similarities between the two banks lies in providing financing to palm oil companies that must have ISPO or RSPO certificates before financing. Implications: This research is expected to provide implications for the green banking program implemented by Bank BSI and Bank BRI in mitigating the risk of financing distribution to customers. Both banks can complement each other to complement the shortcomings in their respective sectors.

Lê Thanh Tiệp

Nghiên cứu này kiểm tra tầm quan trọng của Trách Nhiệm Xã Hội doanh nghiệp (TNXH) đối với Kết Quả Hoạt Động Bền Vững (KQHDBV) của các doanh nghiệp vừa và nhỏ (SMEs) tại Việt Nam. Bên cạnh đó, mối liên hệ trung gian của Năng Lực Xanh (NLX) và Thu Mua Xanh (TMX) cũng được làm rõ. Cùng với đó, lý thuyết quan điểm dựa trên nguồn lực (RBV) và lý thuyết các bên liên quan cùng được sử dụng nhằm đóng góp vào sự hiểu biết đa chiều cho các hiện tượng được nghiên cứu hiện nay. Phương pháp nghiên cứu được lựa chọn dùng cho phân tích là phương pháp định tính và phương pháp định lượng. Dữ liệu nghiên cứu dựa trên ý kiến từ 438 quản lý cấp trung và quản lý cấp cao của các doanh nghiệp tại Việt Nam. Từ dữ liệu nhận về, bài nghiên cứu đã kết luận về các vấn đề cũng như đề xuất các giải pháp và bước phát triển của công ty thông qua mối liên hệ của TNXH và KQHDBV của các SMEs đến với các nhà quản lý để họ có thể đề xuất các chiến lược toàn diện hơn cho công ty thông qua vai trò trung gian của năng lực xanh và thu mua xanh.

Katarzyna Prędkiewicz

This paper examines whether companies’ innovativeness affects the availability of capital and, therefore, whether this group is financially constrained. It uses an objective, indirect way of measuring financial constraints based on the assumption that financially restricted firms only invest when internal cash flow allows them to do so. Therefore, the research was based on the investment-cash flow equation, more precisely the adjusted ECM model adapted to the SME sector. The companies’ innovativeness was measured based on a proprietary synthetic indicator covering a broad range of information on innovation activity. The research was conducted on a sample of 403 firms, including large companies. The modified methodology for studying financial constraints confirmed that innovative small and medium-sized enterprises are financially constrained, contrary to non-innovative SMEs and large enterprises, both the innovators and those with low innovation potential. Based on the model analysis, it has been noticed that in innovative enterprises, there may be substitution between investing in tangible assets and human capital – personnel costs decrease a year before investment projects are implemented. Furthermore, innovative SMEs implement projects in response to a deteriorating situation, shrinking sales market, falling revenues and accompanying employment reduction.

Mohammad Heydari, Matineh Moghaddam, Habibollah Danai

In this study, the relationship between burnout and family functions of the Melli Iran Bank staff will be studied. A number of employees within the organization using appropriate scientific methods as the samples were selected by detailed questionnaire and the appropriate data is collected burnout and family functions. The method used descriptive statistical population used for this study consisted of 314 bank loan officers in branches of Melli Iran Bank of Tehran province and all the officials at the bank for >5 years of service at Melli Iran Bank branches in Tehran. They are married and men constitute the study population. The Maslach Burnout Inventory in the end internal to 0/90 alpha emotional exhaustion, depersonalization and low personal accomplishment Cronbach alpha of 0/79 and inventory by 0/71 within the last family to solve the problem 0/70, emotional response 0/51, touch 0/70, 0/69 affective involvement, roles, 0/59, 0/68 behavior is controlled. The results indicate that the hypothesis that included the relationship between burnout and 6, the family functioning, problem solving, communication, roles, affective responsiveness, affective fusion there was a significant relationship between behavior and the correlation was negative. The burnout is high; the functions within the family will be in trouble.

Md Shah Naoaj

This study investigates the factors that influence the capital adequacy of commercial banks in Bangladesh using panel data from 28 banks over the period of 2013-2019. Three analytical methods, including the Fixed Effect model, Random Effect model, and Pooled Ordinary Least Square (POLS) method, are employed to analyze two versions of the capital adequacy ratio, namely the Capital Adequacy Ratio (CAR) and Tier 1 Capital Ratio. The study reveals that capital adequacy is significantly affected by several independent variables, with leverage and liquidity risk having a negative and positive relationship, respectively. Additionally, the study finds a positive correlation between real GDP and net profit and capital adequacy, while inflation has a negative correlation. For the Tier 1 Ratio, the study shows no significant relationship betweenleverage and liquidity risk, but a positive correlation with the number of employees, net profit, and real GDP, while a negative correlation with size and GDP deflator. Pooled OLS analysis reveals a negative correlation with leverage, size, and inflation for both CAR and Tier 1 Capital Ratio, and a positive correlation with liquidity risk, net profit, and real GDP. Based on the Hausman test, the Random Effect model is deemed moresuitable for this dataset. These findings have important implications for policymakers, investors, and bank managers in Bangladesh by providing insights into the factors that impact the capital ratios of commercial banks.

Boris Radovanov, Nada Milenković, Branimir Kalaš et al.

This study aims to determine whether the same bank-specific and macroeconomic determinants affect banks’ profitability and liquidity. To achieve the set goal, panel data regression analysis was applied with fixed effects or random effects depending on the results of the Hausman test, as explained in the Results. The research is based on the use of aggregate data on bank-specific and macroeconomic determinants of banks’ profitability and liquidity in West Balkan countries during the period from 2007 to 2022. The dependent variables in the study are ROA, ROE used as proxies for banks’ profitability, and banks’ liquid reserves to banks’ total assets as a proxy for banks’ liquidity. The findings confirm that the bank-specific and macroeconomic determinants affect both banks’ profitability and liquidity in the same direction, except for a few variables. The main contribution of this research is a comprehensive and parallel view of banks’ profitability and liquidity determinants that enables a guide for bank management to better understand the significance of bank-specific and macroeconomic determinants’ effects on their business. The obtained results can improve the balance between the two important principles of banking business.

Naiwei Chen, Min-Teh Yu

Abstract This study examines whether and how Sharia compliance and national governance affect the value of corporate cash holding (cash) in Organization of Islamic Cooperation (OIC) countries. Study results indicate that cash can enhance firm value and such cash value is higher for Sharia-compliant firms than for Sharia non-compliant firms. In addition, cash is particularly valuable when national governance is strong. Furthermore, the positive effect of Sharia compliance on cash value is more pronounced when national governance is strong. Results suggest that internal governance (i.e., Sharia compliance) and external governance (i.e., national governance) should be in sync to maximize cash value.

Yitong Niu, Linqian Jiao, Andrei Korneev

With the increase of harmful substances and greenhouse gases that need to be discharged from the traditional thermal power in industrial production in China, the phenomenon of climate warming is becoming more and more prominent. Clean energy will continue to increase in China's future energy consumption structure and market share, hydropower, nuclear power, and other energy as China's main clean energy, the future in China still has a huge market development and use of space. The new policies further adopted by the central bank of China include: continuously optimizing the structure of reasonable credit fund allocation and risk fund application for electric power enterprises to enhance the return rate of assets of electric power enterprises; continuously supporting the development of smart grid and strengthening the linkage between network and electric power; reasonably and categorically guiding the source of clean utilization of electric power, actively supporting large hydropower generation and solar and nuclear power generation, and investing funds in a controlled manner to support large thermal power generation, promote the upgrading of the thermal power generation industry structure, cautiously guide funds into large biomass power generation, wind power generation and small and medium-sized micro-hydro power, strictly control small and medium-sized thermal power, as soon as possible to withdraw from the implementation of the national preferential policies for small and medium-sized power industry management system, energy conservation and reduction of harmful emissions of environmental gases of enterprises is not possible to meet the standards and there are financial risks business efficiency situation Small and medium-sized electric power enterprises that continue to seriously deteriorate.

Mutiara Azura Mulyawan, Gemala Dewi

Abstract: This study aims to describe the authority of a non-muslim notary in making a sharia banking deed and the validity of the deed if the notary who makes and reads the deed is a non-muslim. Through Jurisdical-normative approach, this research found that basically a notary, whether they are moslem or non-moslem, has an authority granted by the Act to make a deed, including sharia banking deed.  The most important thing, the notary is able to understand and apply all the principles of Islamic banking as part of sharia economic law. For this reason, Sharia Banking Deed made and read by a non-Muslim Notary remains valid as long as it is based on the Act of Notary. However, according to Islamic Law, regarding to al-Baqarah verse 282 and At-Talaq verse 2, the Sharia Banking Deed is invalid if the deed is drawn up and read by a non-Muslim Notary. Abstrak: Penelitian ini bertujuan untuk mendeskripsikan kewenangan Notaris non muslim dalam pembuatan akta perbankan syariah dan keabsahan akta yang dibuat dan dibacakan oleh Notaris non muslim. Pendekatan yuridis ormatif digunakan dalam penelitian ini. Hasil penelitian menunjukkan bahwa kewenangan yang diberikan oleh Undang-Undang kepada Notaris terhadap pembuatan akta membuat semua Notaris baik muslim ataupun non muslim dapat membuat akta perbankan syariah. Yang terpenting ia mampu mengerti dan memahami segala prinsip-prinsip dan asas-asas perbankan syariah yang memang tunduk pada hukum ekonomi syariah. Secara hukum positif, keabsahan dari suatu Akta Perbankan Syariah yang dibuat dan dibacakan oleh Notaris non muslim tetap sah selama berdasarkan UUJN. Namun, secara Hukum Islam Akta Perbankan Syariah tidak sah jika akta tersebut dibuat dan dibacakan oleh Notaris non muslim dengan merujuk pada al-Baqarah ayat 282 dan at-Talaq ayat 2.

Akrati Saxena, Yulong Pei, Jan Veldsink et al.

We construct a network of 1.6 million nodes from banking transactions of users of Rabobank. We assign two weights on each edge, which are the aggregate transferred amount and the total number of transactions between the users from the year 2010 to 2020. We present a detailed analysis of the unweighted and both weighted networks by examining their degree, strength, and weight distributions, as well as the topological assortativity and weighted assortativity, clustering, and weighted clustering, together with correlations between these quantities. We further study the meso-scale properties of the networks and compare them to a randomized reference system. We also analyze the characteristics of nodes and edges using centrality measures to understand their roles in the money transaction system. This will be the first publicly shared dataset of intra-bank transactions, and this work highlights the unique characteristics of banking transaction networks with other scale-free networks.

Lulu Lugina Kurniawan

This study aims to determine the effect of Financial Performance on Green banking Disclosures, with a control mechanism as a moderating variable. The control mechanisms used in this study are the Board of Commissioners, the Audit Committee and Public Ownership. The method used is content analysis of 21 items of Green Banking Disclosure based on the Green Banking Disclosure Index (GBDI) developed by Bose at al. (2018). Model Moderating Regression Analysis (MRA) using SPSS software. The population in this study were all banks listed on the IDX during 2017-2019 and reported Green Banking Disclosures respectively.  The results showed that financial performance directly had a positive effect on Green Banking Disclosure. Of the three elements of the control mechanism, only Public Ownership variables moderate the positive effect of Financial Performance on Green banking Disclosures. Meanwhile, the Board of Commissioners and the Audit Committee failed to moderate the effect of financial performance on Green Banking Disclosures. However, together these three control mechanism variables significantly moderate the positive influence of financial performance on Green Banking Disclosures in banking companies listed on the IDX during the study period.

Linwei Hu, Jie Chen, Joel Vaughan et al.

This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking. The SML techniques covered include Bagging (Random Forest or RF), Boosting (Gradient Boosting Machine or GBM) and Neural Networks (NNs). We begin with an introduction to ML tasks and techniques. This is followed by a description of: i) tree-based ensemble algorithms including Bagging with RF and Boosting with GBMs, ii) Feedforward NNs, iii) a discussion of hyper-parameter optimization techniques, and iv) machine learning interpretability. The paper concludes with a comparison of the features of different ML algorithms. Examples taken from credit risk modeling in banking are used throughout the paper to illustrate the techniques and interpret the results of the algorithms.

Hanie. Vahabi, Ali Namaki, Reza Raei

One of the most important features of capital markets as an adaptive complex networks is their collective behavior. In this paper, we have analyzed the banking sectors of 4 world stock markets,which composed of emerging and matures ones. By applying one the important complexity notions, Random matrix theory(RMT), it is founded that mature markets have a higher degree of collective behavior,Even though we used RMT tools: participation ratio(PR), node participation ratio(NPR)and relative participation ratio(RPR) , which NPR illustrated independent banks than whole market and RPR compared collective behavior of markets by a normal range. By applying local and global perturbations, we concluded that mature markets are more vulnerable to perturbations due to the high level of collective behavior. Finally, by drawing the dendrograms and heat maps of the correlation matrices,