Banking Sector Research Articles

Governance and Risk Management in Sharia Banks and Financial Performance

General Background: The financial performance of Sharia commercial banks is crucial for Indonesia's economic growth. Specific Background: This study explores the impact of Good Corporate Governance (GCG), Internal Audit, and Risk Management on the financial performance of Sharia banks, particularly after the enactment of Law No. 21 of 2008. Knowledge Gap: Existing literature shows mixed results regarding the effects of these governance factors, indicating a need for comprehensive analysis in the context of Sharia banking. Aims: The research aims to assess how GCG, Internal Audit, and Risk Management affect financial performance from 2018 to 2021. Results: Analysis of 44 Sharia banks reveals that while Internal Audit negatively affects performance, GCG and Risk Management have significant positive impacts. Novelty: This study introduces a thorough examination of Risk Management alongside GCG and Internal Audit, enhancing the understanding of their roles in Sharia banking. Implications: Findings highlight the importance of strengthening governance frameworks in Sharia banks to improve financial performance, providing insights for investors and policymakers. Highlights: Good Corporate Governance and Risk Management significantly enhance financial performance in Sharia banks. Internal Audit shows a negative impact on the financial performance, emphasizing the need for effective implementation. The study addresses a gap in literature by examining the interplay of GCG, Internal Audit, and Risk Management in the Sharia banking sector. Keywords: Good Corporate Governance, Internal Audit, Risk Management, Financial Performance, Sharia Banks

Integration level within the European Union banking sector

Purpose What is the current level of integration of the retail and wholesale banking sectors in the European Union (EU) countries, and what are the main factors and challenges affecting it? What are the solutions to promote the integration and convergence of the retail banking sector in the European countries?. Design/methodology/approach Evaluated the level of integration of the banking sector, focusing on the EU banking context, retail banking especially. The paper will adopt a comprehensive and multidimensional approach, considering different criteria and indicators of integration, such as price, quantity and information, as well as different segments and dimensions of the EU banking sector, such as wholesale and retail, assets and liabilities, products, services, price indicators and institutions and markets. Findings It reveals that while significant integration has been realized in domains like the money market and wholesale banking, it is less pronounced in the retail banking and capital markets. Originality/value This paper is an original research. The entire study belongs to me.

Impact of Motivational Factors on Employee Performance on Banking Industry in Nepal

This research investigates the impact of motivational factors on employee performance within the Nepalese banking industry, a sector critical for the nation's financial stability and economic growth. Recent growth in Nepal's banking sector, driven by factors such as increased financial inclusion and technological advancements, underscores the need for a highly motivated and productive workforce. Employee performance, defined as the measurable contribution of an individual to organizational goals, is crucial for maintaining a competitive edge. This study explores the relationship between motivation and performance, drawing on need-based theories, goal-setting theory, and social cognitive theory, while considering Nepal's unique socio-cultural context. This study investigates the impact of intrinsic and extrinsic motivational factors on employee performance within the banking sector in Nepal. Data were collected from 350 respondents across various banks through online surveys, with a pilot test conducted to ensure clarity and reliability. The survey assessed both types of motivational factors and their influence on employee performance. Demographic analysis provided key insights into the participant pool, enhancing the understanding of the sample's characteristics. The findings underscore the importance of both intrinsic factors, such as job satisfaction and professional development, and extrinsic factors, including competitive salaries and job security, as key motivators for employee performance. Additionally, the study highlights the influence of cultural values, such as collectivism and respect for hierarchy, in shaping employee motivation in Nepal. By employing a descriptive research design and rigorous methodological steps, including statistical analysis using SPSS, this study provides a comprehensive understanding of the motivational landscape in Nepal's banking sector. The insights gained can guide banks in developing effective strategies to enhance employee motivation and performance, contributing to the overall success and growth of the industry.

Work Stress and Job Satisfaction Among Female Bankers in Nepal

The purpose of the study is to identify the causes of stress and how stress affects job satisfaction in the banking sector. Data on the employees' Satisfaction and stress levels are also provided by this study .Non-probability sampling was utilized to create a representative sample for the study, which included 16 different banks . There were 386 responders in the sample. A questionnaire was used to get the primary data from the respondents. One-way ANOVA, correlation, and regression analysis. The results of the study demonstrate that work-life balance, peer support, and stress connected to the job all significantly affect how well employees perform in the banking sector. The study also comes to the conclusion that employee happiness is not significantly impacted by organizational stress factors.

Role of Financial and Non-financial Incentives on Employee’s Performance: A Study on the Banking and Insurance Sectors of Nepal

As the banking and insurance are service sector industries, the competence and dedication of the employees of these sectors significantly influence customer satisfaction and organizational success. Therefore, the performance of employees stands as a crucial factor determining the success of banks and insurance companies. This research aims to explore the impact of financial and non-financial incentives on the performance of employees working in the Nepalese banking and insurance sectors. A structured questionnaire was employed to collect the data for the study from 400 sample employees of these sectors by using a non-probability sampling method. For the analysis of data, tools such as Cronbach’s Alpha analysis and Regression analysis was carried out to assess the relationship among the variables. In addition, to analyze the findings of ordinal variables, Ordinal Logistic Regression Model was used. The study found a positive correlation between financial incentives (bonuses and fringe benefits) and employee performance. By the value of Ordinal Logistic Regression, factors like age, experience, and job security did not indicate a significant impact on employee performance. In contrast to that, factors like age, experience, and job security did not indicate a significant impact on employee’s performance. The findings of the study also challenge the theoretical assumption that higher pay directly leads to improved performance, suggesting that high pay expectations might lead to performance pressure and reduced employee performance. Similarly, while working conditions were not significant, emphasizing upgrades solely based on this factor may not be justified in the banking and insurance sectors based on this research. Recommendations include the necessity for managers or HR professionals to strike a balance between financial and non-financial incentives to optimize employee performance and thereby enhance company productivity.

Мониторинг готовности будущих экономистов к использованию искусственного интеллекта в банковской сфере

In the context of the digital transformation of modern society, the study of the problem of introducing artificial intelligence into all spheres of life, including the sphere of economics and finance, is extremely relevant. The purpose of this article is an attempt to analyze the readiness of future economists in the school-university system to use artificial intelligence in the context of developing digital financial literacy. The authors studied the nature of the influence of artificial intelligence on the formation of basic competencies necessary in the era of digitalization of economic education, through the prism of the views of the younger generation. The importance of various aspects of the digital transformation of society, which are in the focus of attention of global cooperation organizations (UNESCO, World Bank, etc.) in the interests of sustainable development, is noted. Using Google-Forms tools, a sociological survey was conducted among students of the Financial University under the Government of the Russian Federation (Omsk branch) and students of specialized economic classes at Gymnasium 19 in the city of Omsk on the topic “The attitude of future economists to the use of artificial intelligence in the banking sector.” The results of the analysis of the obtained empirical data made it possible to state the complex and ambiguous nature of the influence of artificial intelligence on the young generation of future economists in the conditions of total digitalization. Along with the positive attitude of young people towards the prospects for the introduction of artificial intelligence, the risks of a decrease in natural intelligence against the backdrop of digitalization reaching all spheres of life have been identified. This, in turn, requires the formation of value guidelines and the development of a strategy for the continuous development of future economists in the digital society in the context of the active implementation of artificial intelligence.

Distributed Ledger Technology and Financial Reporting Integrity in Nigerian Quoted Banks: A Study on Error Reduction and Enhanced Transparency

This study investigated the effect of distributed ledger technology (DLT) factors on eliminating financial reporting errors (FREs) in quoted Nigerian banks. Using an exploratory survey design, data was collected from 300 employees of 14 quoted banks involved in financial reporting. DLT factors of public, private, hybrid, and blockchain were examined as independent variables affecting the dependent variable of FRE elimination. Descriptive analysis showed that all DLT types were perceived as highly effective for error reduction. Correlation analysis revealed strong positive relationships between DLT factors and FRE mitigation. Regression modeling found that hybrid DLT had the largest impact on error elimination, followed by private, public, and blockchain DLT. Together, the DLT factors explained 98.1% of the variance in FRE reduction. The results statistically established the significant positive effects of DLT factors on eliminating prevalent FREs like principle, omission, entry, disclosure, and reversal errors. Key contributions include providing robust empirical evidence that leveraging DLT, especially hybrid DLT, can eliminate common financial reporting errors in Nigerian banks. The pioneering study expands conceptualizations, theories, and literature regarding DLT's potential to comprehensively transform financial reporting accuracy. It offers important implications for policy, practice, and research on regulating, adopting, and studying DLT solutions to address persistent financial statement errors undermining stakeholder trust in Nigeria's banking sector. The study concludes by strongly recommending for policy and, in practice, the regulation and full adoption of DLT for the elimination of FREs in Nigeria.

Digital Transformation in Georgian Banking: Trends and Current Level of Adoption

The significance of digital technologies is crucial in contemporary times. This is especially pertinent for the banking industry, enabling it to deliver high-quality customer service and address market challenges promptly. This article examines the current state of the Georgian banking system, focusing on digital services and products, and the advances banks have made in integrating innovative digital technologies. Using specific criteria, the study evaluates the level of digitization within the banking sector, identifying strengths and areas for improvement. The research also explores the digital transformation experiences of developed countries' banking sectors and considers how these could be implemented in Georgia, taking into account the unique context and challenges faced by the country. The study aims to assess the digital readiness and challenges faced by Georgia's banking sector, leading to the formulation of recommendations to speed up the digital transformation process and enhance competitiveness. Both quantitative and qualitative research methods are used in the research. The digital banking products and services offered by commercial banks are analyzed, with statistical conclusions derived from quantitative data, providing a comprehensive overview of the sector's digital maturity. In the end, the study evaluates the challenges and future prospects for the digital transformation of Georgia's banking system, offering strategic insights and practical solutions for policymakers and industry stakeholders. Keywords: Georgian banking, innovation, digital transformation, mobile banking, online banking, digital payments

Customer-Driven Evaluation of Service Quality in Nepal's Banking Sector

Service quality is a critical determinant of customer satisfaction in the banking sector, where customers' perceptions of various service attributes directly influence their loyalty and engagement. In Nepal, understanding these perceptions is essential for banks to maintain competitiveness and improve service standards. This study utilized a quantitative approach to assess customer satisfaction across key service attributes in Nepal's banking sector. A structured questionnaire was distributed to 120 respondents, measuring their agreement with statements related to service quality using a Likert scale. Descriptive statistics, including frequency, percentage, and cumulative percent, were employed to analyze customer feedback. The results indicate that while customers largely appreciate the banks' modern facilities, neat-appearing employees, and secure services, there is a notable neutrality regarding the reliability of timely service delivery and problem-solving efforts. Furthermore, many respondents were neutral about the personal attention and enthusiasm provided by bank employees. However, a significant majority of customers expressed satisfaction with the banks’ ability to meet their needs, recommending the bank to others and intending to continue using its services. The analysis reveals that Nepalese banks perform well in terms of infrastructure and employee professionalism but have room for improvement in service reliability and responsiveness. Addressing these gaps can enhance customer satisfaction and loyalty, offering banks valuable direction for maintaining high service standards and improving customer engagement.

Impact of Work-Life Balance, Job Satisfaction, and Stress Levels on Employee Retention in Banking Sector Companies

This study examines the impact of work-life balance, job satisfaction, and stress levels on employee retention in the banking sector. Utilizing a sample of 300 banking employees, data were collected through structured questionnaires and analyzed using multiple regression analysis. The findings indicate that work-life balance and job satisfaction have significant positive effects on employee retention, while stress levels negatively affect retention. Work-life balance emerged as the strongest predictor of retention, followed by job satisfaction. The results highlight the importance of creating a supportive work environment to improve retention rates in the banking industry. Practical implications suggest that banking companies should focus on fostering employee well-being and reducing workplace stress to retain talent. This study contributes to the existing literature on employee retention by providing insights into the banking sector, offering managerial strategies to reduce turnover, and enhancing overall employee satisfaction.

SENTIMENT ANALYSIS OF CUSTOMER FEEDBACK IN THE BANKING SECTOR: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS

This study investigates the application of sentiment analysis to customer feedback in the banking sector, utilizing natural language processing (NLP) techniques and machine learning models to classify customer sentiments into positive, neutral, and negative categories. Feedback was sourced from online platforms, including bank websites, social media, and third-party review sites. Data preprocessing steps, such as tokenization, stemming, and feature extraction using TF-IDF, were employed to prepare the text for analysis. Various machine learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Naïve Bayes, were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that LSTM outperformed all models with a 91% accuracy, followed closely by SVM at 89%. These findings demonstrate the potential of advanced machine learning techniques in accurately classifying sentiments and provide valuable insights into customer satisfaction and areas for improvement within the banking sector. Future work aims to further optimize models for better classification of neutral feedback and explore more advanced deep learning models, such as BERT.

Unveiling the Rise :Exploring Growth and Performance in India's Flourishing Banking Sector

This study delves into the growth and performance of India's burgeoning banking sector , highlighting its pivotal role in driving credit , investment and infrastructure development .

A HOLISTIC APPROACH TO THE BANKING SECTOR: PERFORMANCE AND EFFICIENCY ANALYSIS OF TURKISH BANKS USING CoCoSo AND DEA METHODS

The banking sector is a vital component of global economies, playing a critical role in ensuring financial stability by gathering savings, providing credit, and facilitating various financial transactions. Banks not only support economic growth but also act as intermediaries between surplus and deficit units within the economy. Therefore, evaluating their financial performance and efficiency is crucial for understanding the overall health of the financial system. This study analyzes the financial performance and efficiency of the top 10 banks in Turkey, which are ranked by asset size, over a 10-year period from 2013 to 2022. Using the Combined Compromise Solution (CoCoSo) method, the study ranks these banks based on various financial criteria, such as capital adequacy, asset quality, liquidity, and profitability. CoCoSo is a multi-criteria decision-making method that allows for comprehensive performance analysis by considering multiple factors simultaneously. In addition to CoCoSo, Data Envelopment Analysis (DEA) is used to evaluate the efficiency of these banks in utilizing their resources to generate financial outputs. The study’s findings reveal that privatelyowned banks, particularly Akbank T.A.Ş., consistently rank at the top in terms of both financial performance and efficiency. Akbank’s strong performance is attributed to its effective resource utilization and strategic management decisions. State-owned banks, on the other hand, generally show lower financial performance, even though they exhibit high efficiency levels. This discrepancy suggests that public banks may be focusing on non-profit-driven projects that contribute to public welfare, which in turn affects their overall financial performance. Foreign-owned banks, such as Garanti Bankası and Denizbank, also demonstrate strong financial performance but show varying degrees of efficiency, with Denizbank being relatively less efficient in resource utilization. By employing both the CoCoSo and DEA methods, this study offers a unique dual-method approach that provides a comprehensive evaluation of the Turkish banking sector. The combination of these two methods allows for a more nuanced understanding of both financial performance and efficiency, offering valuable insights for policymakers, bank management, and researchers. The study not only highlights the strengths and weaknesses of individual banks but also underscores the importance of efficient resource management for sustaining competitive advantage in a highly dynamic banking environment. The results of this study can serve as a robust framework for future research on bank performance evaluation, especially in emerging markets. Moreover, the study’s methodology can be applied to other sectors or countries to provide cross-sectional and time-series analysis, contributing to the broader literature on financial performance and efficiency.

A COMPREHENSIVE STUDY OF MACHINE LEARNING APPROACHES FOR CUSTOMER SENTIMENT ANALYSIS IN BANKING SECTOR

This study explores the application of sentiment analysis in the banking sector, focusing on customer feedback to enhance service quality and customer experiences. We collected a comprehensive dataset of approximately 100,000 entries from diverse sources, including customer satisfaction surveys, social media platforms, and direct feedback. A robust preprocessing pipeline was employed to address challenges associated with unstructured data, informal language, and mixed sentiments. We evaluated several machine learning and natural language processing models, including Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, Long Short-Term Memory (LSTM), and BERT (Bidirectional Encoder Representations from Transformers), using metrics such as accuracy, precision, recall, F1 score, AUC-ROC, and training time. The results revealed that advanced models, particularly BERT, achieved superior performance with an accuracy of 88% and an F1 score of 0.86, demonstrating an exceptional ability to capture nuanced sentiments. This study underscores the importance of employing sophisticated sentiment analysis techniques in banking to derive actionable insights from customer feedback. The findings suggest that leveraging advanced models can significantly improve service quality and customer satisfaction, while also presenting avenues for future research into real-time sentiment analysis and its integration with customer relationship management systems.

Assessment of Risk Strategies in Banking Sector of Nepal

This article examines the impact of strategies in mitigating the risk in commercial banks of Nepal. First the paper introduces the risk in the financial institutions and then discusses the types of risks in the banks that rely on the IT systems for the operations and services. Besides, it forwards some strategies that can help the banking sector to reduce the risk and to promote the security from the attackers. I have used the mixed method of research design and have massively used the secondary data to assess the impact of the tools and techniques employed in mitigating the risk in the banking sector of Nepal. Despite the reliance on the secondary materials for information, the paper is analytical and critical contribution to the existing knowledge on the subject i.e. the risk management in the financial institutions of Nepal. The major objective of the paper has been to scrutinize the current risks faced by the banks in Nepal and the process of reducing the risks as adopted by the financial institutions in general. The finding of the study is the spread of awareness and updating the IT systems by the banks can help to protect the systems from the intruders and the cyber security must be strengthened to let the financial institutions feel safe.

Identifying extreme events in the stock market: A topological data analysis.

This paper employs Topological Data Analysis (TDA) to detect extreme events (EEs) in the stock market at a continental level. Previous approaches, which analyzed stock indices separately, could not detect EEs for multiple time series in one go. TDA provides a robust framework for such analysis and identifies the EEs during the crashes for different indices. The TDA analysis shows that L1, L2 norms and Wasserstein distance (WD) of the world leading indices rise abruptly during the crashes, surpassing a threshold of μ+4∗σ, where μ and σ are the mean and the standard deviation of norm or WD, respectively. Our study identified the stock index crashes of the 2008 financial crisis and the COVID-19 pandemic across continents as EEs. Given that different sectors in an index behave differently, a sector-wise analysis was conducted during the COVID-19 pandemic for the Indian stock market. The sector-wise results show that after the occurrence of EE, we have observed strong crashes surpassing μ+2∗σ for an extended period for the banking, automobile, IT, realty, energy, and metal sectors. While for the pharmaceutical and FMCG sectors, no significant spikes were noted. Hence, TDA also proves successful in identifying the duration of shocks after the occurrence of EEs. This also indicates that the banking sector continued to face stress and remained volatile even after the crash. This study gives us the applicability of TDA as a powerful analytical tool to study EEs in various fields.

Is Dhaka Stock Exchange An Efficient Capital Market? - Evidence Of Weak-Form Efficient Market Hypothesis

This paper uses Random walk hypothesis to test the efficiency of Dhaka Stock Exchange Ltd (DSE), an emerging capital market of Bangladesh. In this paper, with Runs test, Dickey-Fuller Unit root test have been processed and analyzed to observed the behavior of daily return of Dhaka Stock Exchange indices during the study period. The sample includes the daily price indices of all securities listed on the DSE general, DSI (All Share), DSE-30, and Daily indices listed with DSE. As a proxy of movement of individual stock prices, daily closing prices of 30 companies operating in the Banking sector has been analyzed. The results provide evidence that DSE does not follow the random walk model and so DSE is not efficient even in weak form. To improve this capital market, the timely disclosure and dissemination of information to the shareholders and investors on the performance of the listed companies should be given emphasized.

Cyber Fraud, Causes and Its Effect on Money Deposit Banks

The study examined cyber fraud, causes and its effect on money deposit banks (DMB) in Nigeria: a study of Zenith Bank. The main objective is to examine electronic frauds, causes and its challenges in the banking sector. The study is hinged on two hypotheses; 49 branches; sample of 557 staff. Structured questionnaire was used to capture the response of the bank staff and it was analyzed using statistical package for social science version 27 (SPSS). Descriptive statistics and multiple regression were employed in the data analysis. Among the findings were accounting fraud by bank staff, identity theft, money laundering, phishing, pharming, computer virus, lack of oversight function,lack of standards, financial loss, absence of national central control, lack of credible infrastructure, porous internet, lack of national functional database, and inadequate awareness by bank customers were the challenges militating the effort towards curbing the cyber fraud in the banking system in Nigeria. The findings also revealed that cybercrime reduced productivity; vulnerability of banks Information and Communication Technology (ICT) systems and security audit. The study recommends adequate antivirus and antimalware software technology and the use of multi-factor authentication, use of biometrics, automatic logout are the solutions identified to curb cybercrimes in banks. The study concluded that the place of security in the cyber space of banks in Nigeria cannot be oversemphasied, therefore necessary measures should be put in place towards mitigating cybercrime in Zenith Bank. Equally, Zenith Bank should employ stringent measures to monitor staff activities especially in the confidentiality of customer information, cyber security audit should be done on regular basis, sensitization of bank customers, and Multifactor authentication, biometrics and automatic log out, and strong firewall should be adopted by Zenith Bank.

Artificial Intelligence: A Twenty First Century International Regulatory Challenge

Artificial Intelligence (AI) is a twenty first century evolution. Certain aspects of AI have been integrated into daily living. AI applications have also been incorporated into the aviation, banking, cyber security, educational, employment, health, and military sectors respectively. However, the unpredictable nature of AI is a cause for concern because ‘In many instances, AI remains under the control of users and designers, but in increasing numbers of applications, the behaviour of a system cannot be predicted by those involved in design and application […]. Newly developed machines are able to teach themselves and even collect data’. Consequently, ‘The potential benefits and harms of AI have led to calls for governments to adapt quickly to the changes AI is already delivering and the potentially transformative changes to come. These include calls to pause AI development and for countries […] to deliver a step-change in regulation’. ‘In March 2023, more than 1,000 artificial intelligence experts, researchers and backers signed an open letter calling for an immediate pause on the creation of “giant” AIs for at least six months, so the capabilities and dangers of such systems can be properly studied and mitigated’. What are the benefits of AI? What are the risks of AI? Which crimes can be committed via AI? What are the regulatory challenges? What has been the international response? In this article, we will explore whether there is a justification for regulating AI from ethical, legal and law enforcement perspectives. Keywords: Artificial intelligence; Ethics; Regulation; Law enforcement

AI as decision aid or delegated agent: The effects of trust dimensions on the adoption of AI digital agents

AI digital agents may act as decision-aid or as delegated agents. A decision-aid agent helps a user make decisions, whereas a delegated agent makes decisions on behalf of the consumer. The study determines the factors affecting the adoption intention of AI digital agents as decision aids and delegated agents. The domain of study is banking, financial services, and Insurance sector (BFSI). Due to the unique characteristics of AI digital agents, trust has been identified as an important construct in the extant literature. The study decomposed trust into social, cognitive, and affective trust. We incorporated PLS-SEM and fsQCA to examine the factors drawn from the literature. The findings from PLS-SEM suggest that perceived AI quality affects cognitive trust, perceived usefulness affects affective trust, and social trust affects cognitive and affective trust. The intention to adopt AI as a decision-aid is influenced by affective and cognitive trust. The intention to adopt AI as delegated agents is influenced by social, cognitive, and affective trust. FsQCA findings indicate that combining AI quality, perceived usefulness, and trust (social, cognitive, and affective) best explains the intention to adopt AI as a decision aid and delegated agents.