Evaluating management efficiency in the banking sector: an integrated MCDM framework

Banking and financial sector assume a crucial role in enhancing economic development. During 1990s, when GCC countries underwent major economic reforms, the private sector had to play a leading role by participating in the economic development activities such as designing and implementing private programs in which the private sector can significantly contribute. Since the role of private sector has expanded, their financial requirements have increased. This gap was largely filled in by the banking sector for capital market has not been so effective in the Gulf region. Thus, Banking sector reforms have forced the banks in Gulf region to be more focused on satisfying customers developing new operating models, defining new business strategies to achieve operational efficiency in the changing environment. When it comes to Oman, the banking system is sound and efficient. A major challenge being faced by the banks in Oman is to manage the operational and strategic changes that are increasingly taking place after 1990 when major economic reforms were introduced. Efficient change management is the driving and resisting force for achieving the bench mark target in all functions of banks. The purpose of the study is to measure the financial efficacy of commercial banks in Oman after 1990s in the process of their way of handling the changes.

A Working Paper on the Impact of Gender of Leader on the Financial Performance of the Bank: A Case of ICICI Bank

In the rapidly evolving landscape of the banking industry, the need for efficient financial management has become paramount. This proposal aims to explore the integration of Enterprise Resource Planning (ERP) applications in the banking sector with a focus on enhancing financial efficiency. The objective is to investigate how ERP systems can revolutionize traditional banking operations, optimize financial workflows, and ensure regulatory compliance. By delving into this topic, we seek to provide a comprehensive understanding of the challenges and opportunities associated with implementing ERP solutions in the context of financial institutions. The banking sector, characterized by intricate financial processes and regulatory complexities, stands to benefit significantly from the implementation of ERP systems. In the second paragraph, we will delve into the specific ways ERP applications can address challenges faced by banks. From automating routine financial tasks to providing real-time data insights for strategic decision-making, ERP systems offer a robust framework to streamline operations. We will explore how ERP modules tailored for the banking industry can encompass core functions such as accounting, risk management, and compliance, thereby fostering a more agile and responsive financial environment. Through this proposal, we aim to highlight the symbiotic relationship between ERP applications and the banking sector's pursuit of heightened financial efficiency.

The article is aimed at researching the impact and directions of fintech risk management in the banking sector. The article identifies key tendencies in the development of fintech innovations for the banking sector and explores the potential impact of their implementation on banks. According to the results of the analysis, fintech risks are systematized and the essence of each of them is disclosed. It is defined that an efficient direction for improving the efficiency of fintech risk management is to establish cooperation between banks and fintech companies. The study of foreign and domestic practice allowed to distinguish four main areas of partnership with: 1) fintech companies operating outside the banking sector; 2) fintech companies offering fintech solutions for banks; 3) fintech companies supplying destructive technologies; 4) fintech companies relying on current bank offers. The main channels of fintech risk transmission to the banking sector are identified. It is determined that the consequences of the implementation of fintech risks are manifested through the following traditional banking risks: operational, compliance, strategic, reputational. It is proved that in most cases, the events of fintech risk implementation on different business lines will be classified as consequences of operational risk. It is determined that the management of fintech risks in the banking business should be based on generally accepted approaches to risk management, taking into account the business model of a credit institution. It is determined that minimization of the impact of fintech risks is facilitated by the regulation of cooperation with fintech companies on the implementation of fintech innovations (development of strategies, policies, procedures, methods). The prospect of further research in this direction is the development of models for quantitative assessment of fintech risks. A significant positive impact on the efficiency of risk management of banks will be the development of methodological recommendations for stress testing, taking into account fintech risks. In the future, there is a need to develop appropriate models for the impact of fintech risks on the balance sheet of banks and adequate stress scenarios.

Purpose of the article: The purpose of this article is to provide a comprehensive analysis of the role of artificial intelligence (AI) in the banking sector, focusing on its applications, challenges, and implications. By synthesizing existing research and empirical studies, the article aims to inform researchers about the transformative potential and inherent challenges of AI-driven innovation in banking. Methodology/methods: Using a systematic review approach, the relevant literature on AI integration in banking was identified from electronic databases and leading corporate research departments, ensuring a synthesis of scholarly and industry perspectives. Scientific aim: With limited academic research on AI in banking, this study aims to shed light on its applications, challenges, and implications. Findings: The integration of AI in the banking sector has significantly transformed various operational areas, including customer interactions, risk management, compliance, and operational efficiency. AI applications, such as chatbots and smart virtual assistants, have enhanced customer service by offering personalized, 24/7 support, and have demonstrated significant cost and revenue benefits. AI-driven credit scoring and fraud detection have improved risk assessment and mitigation, enabling more precise and informed decision-making. However, AI adoption faces challenges such as high computational costs, data quality issues, the "curse of recursion" where models trained on AI-generated data degrade, and the need to balance trust in AI outputs with their reliability. Furthermore, regulatory considerations play a crucial role in AI integration. While the European Union's AI Act aims to ensure the ethical use of AI in finance, it also presents challenges related to compliance and potential over-regulation. Conclusions: In conclusion, the integration of AI in the banking sector has revolutionized customer service, risk management, compliance, and operational efficiency. However, the adoption of AI also raises concerns about data privacy, security, and the need for regulatory frameworks to ensure ethical use. As AI continues to evolve, it will be crucial for banks to balance technological innovation with responsible practices to maximize benefits and mitigate risks.

The purpose of this paper to evaluate the cost of deposit insurance premium and assess moral hazard behavior in the banking sector in Sudan. The analysis of moral hazard in this paper is based on two types of risks, credit risk, and technical efficiency risk (managerial efficiency). The findings of the paper indicates credit risk is positively associated with deposit insurance coverage, and technical efficiency performance is negatively associated with deposit insurance coverage. Also indicated that banks whose risk-based insurance premium is less than the fixed rate insurance premium (over-priced) are those banks with pure technical efficiency performance. These results imply when banks characterized with managerial inefficiency deposit insurance system can incite moral hazard behavior. Additional finding of the paper includes when banking sector is classified into homogenous groups (national, foreign, and mixed ownership) risk-based average deposit insurance premium is close estimate to the average credit default risk for each group.

The purpose of this paper is to evaluate the cost of deposit insurance premium and assess moral hazard behaviour in the banking sector in Sudan. The analysis of moral hazard in this paper is based on two types of risks, credit risk, and technical efficiency risk (managerial efficiency). The findings of the paper indicate credit risk is positively associated with deposit insurance coverage, and technical efficiency performance is negatively associated with deposit insurance coverage. Also indicated is that risk-based insurance premium is less than the fixed rate insurance premium (over-priced) for banks with pure technical efficiency performance. These results imply that when banks are characterised with managerial inefficiency the deposit insurance system can incite moral hazard behaviour. An additional finding of the paper indicates that, when banking sector is classified into national, foreign, and mixed ownership classifications, risk-based average deposit insurance premium is close to the average credit risk in each group.

Performance of the economy of any country is largely dependent on the performance of its banking sector. Since, banking sector constitutes a major component of the financial service sector. Soundness of the banking sector is essential for a healthy and vibrant economy. The efficiency, productivity, profitability, stability and a shock free economy is possible only when a country is having a sound and healthy banking sector. The present research work has been undertaken to analyze the soundness of five nationalized banks in India. In order to measure the performances of these banks CAMEL MODEL Approach has been applied, incorporating important parameters like Capital Adequacy, Assets Quality, Management Efficiency, Earnings Quality and Liquidity. The finding of the study shows that Bank of Baroda has been ranked at the top position, the Union Bank of India and Dena Bank secured the 2nd position, the next was the State Bank of India which secured the 4th position and in the last position was the UCO Bank which secured the 5th position.
 JEL Code: G2, G12, G21, G32& G33.

Lately, due to its importance, the banking sector is being analysed for its efficiency by individual countries, through modern economic and mathematical methods and models. This applies to the banking sector in Serbia, as well. The paper analyses the 2016 efficiency of the banking sector in the Republic of Serbia using the DEA (Data Envelopment Analysis) model. The obtained results of the survey conducted on 30 banks show that only eight banks operated at a satisfactory efficiency level, whereas twenty-two banks were inefficient. Considering the 91.2% share of banks in the total balance sheet amount, which, at the end of 2016 amounted to RSD 3.556 billion, one can conclude that the structure of Serbian financial system remains highly bank-centric and that primarily banks dictate the overall economic activity and development directions. Consequentially, raising the efficiency level of banking operations in Serbia will imply better performance of the entire financial system. Therefore, the primary objective of the banking sector in the future period should be to increase the efficiency of operations, especially in the case of inefficient banks. This can be achieved through a more efficient management over assets and liabilities, human resources, equity, operating income and profit. Moreover, we need a more efficient cost management, achieved by using modern concepts (like cost accounting by activities), as well as a higher quality human resources management.

The aim of the study was to investigate the mechanisms of bank insolvency and explore the possibilities of applying technological innovations for risk management and ensuring financial stability of banks. The relevance of this research lies in its focus on understanding the causes and mechanisms of bank insolvency, as well as identifying possible ways to prevent this phenomenon and maintain financial stability in the banking sector. Given the dynamics of the economic environment, changes in legislation, and technological innovations, understanding risks and mechanisms of their management becomes extremely important for ensuring the stability of the banking system and protecting the interests of financial market participants. Such research can serve as a basis for developing risk management strategies in the banking sector and contribute to strengthening trust in banks and the financial system as a whole. The study employed the following research methods: specification – the research refined the terminology and definitions of bank insolvency to understand which criteria determine this status; systematization – various aspects of bank insolvency were structured, such as causes and indicators; literature review with analysis of the material found; theoretical modeling – development of theoretical models of causes and consequences of bank insolvency; classification. The obtained results of the research provide a comprehensive understanding of the causes of bank insolvency, determine the parameters of this phenomenon, and consider possible ways of regulation and risk management. The importance of using technological innovations, such as machine learning and data analytics, to improve the efficiency of risk management and ensure financial stability of banks is also emphasized. This article makes an important contribution to the study of financial stability of banks through its systematic analysis and scientific approach to the issue of insolvency. It examines various aspects of this phenomenon, from conceptual foundations to practical applications, focusing on economic, legal, and technological aspects. The article introduces innovative approaches to risk analysis in the banking sector, using advanced research methods.

Introduction. Modern trends in globalisation and digitalisation have a significant impact on the architecture of the banking sector, requiring the adaptation and transformation of traditional banking models. The rapid development of financial technologies, the growing popularity of cryptocurrencies, changing regulatory requirements, and changes in consumer behaviour require a review of the conceptual framework of banking institutions. Purpose, objectives and research methods. The purpose of this study is to determine the conceptual changes that are taking place in the architecture of the banking sector under the influence of modern technological, economic and social changes. The object of the study is the banking sector as part of the financial system, which is undergoing changes under the influence of global economic processes, innovative technologies and regulatory reforms. The study used such methods as comparative analysis and system analysis. To obtain qualitative results, an analytical review of financial and economic literature and the regulatory framework on the architecture of the banking sector was conducted. Research results. The study showed that the modern architecture of the banking sector is undergoing significant changes under the influence of digital technologies and innovations. One of the key trends is the integration of financial technologies, which allows banks to optimise their operational processes, increase the efficiency of risk management and improve the quality of customer service. Banks are actively implementing artificial intelligence to automate processes, blockchain to increase transparency and security of transactions, and big data to analyse customer behaviour and make decisions. At the same time, new challenges arise from cybersecurity, regulatory changes, and competition from non-bank financial institutions. The practical value of the results is that the analysis allows banking institutions and regulators to adapt to new conditions more effectively. The findings can be used to optimise banking activities, improve internal processes, develop new financial products and strengthen resilience to risks arising from constant changes in the market. Conceptual changes in the banking sector's architecture are defining new approaches to management, development strategy and service delivery. Digitalisation has become an integral part of banking activities, forcing banks to review their business models and actively invest in technological innovations.

The main objective of this chapter is to investigate the effects of cybercrime on the banking sector in ASEAN. Global challenges on the evolution of cybercrime are in continuous dynamics in the case of emerging or developing countries, so that sustainable development plays an essential role. Moreover, the propagation effects can generate significant damages in the banking sector. Efficient bank management is essential in the context of providing advanced techniques for cyber security. Traditional cyber security measures are insufficient to ensure data protection and online information privacy. Consequently, investigations of cyber-criminal activity must become a priority especially in the context of globalization.

Service quality in the banking sector is a critical determinant of customer satisfaction, loyalty, and competitive advantage. As banks strive to meet the evolving expectations of customers and navigate an increasingly complex regulatory landscape, the role of data analytics in enhancing service quality has become paramount. This review explores how data analytics can be leveraged to improve service quality in the banking sector, offering insights into the methods, benefits, and practical applications of this approach. The review begins by outlining the importance of service quality in banking, emphasizing its impact on customer retention and the overall success of financial institutions. Traditional methods of assessing and improving service quality, such as customer surveys and manual audits, are often limited by their reactive nature and the inability to handle large volumes of data effectively. In contrast, data analytics provides a proactive and comprehensive approach, enabling banks to identify patterns, predict trends, and make data-driven decisions that enhance service delivery. Data analytics encompasses various techniques, including descriptive, predictive, and prescriptive analytics, each offering unique benefits for service quality improvement. Descriptive analytics allows banks to gain insights from historical data, identifying key areas for improvement. Predictive analytics uses statistical models and machine learning algorithms to forecast future customer behavior, enabling banks to anticipate needs and address potential issues before they escalate. Prescriptive analytics goes a step further by recommending specific actions to optimize service quality, based on the analysis of past and predicted data. Key areas where data analytics can significantly enhance service quality in banking include customer relationship management (CRM), operational efficiency, and risk management. In CRM, data analytics enables banks to personalize services, segment customers effectively, and predict their needs with greater accuracy. This personalized approach not only enhances customer satisfaction but also fosters loyalty and long-term relationships. Operational efficiency is another critical area where data analytics can drive improvements. By analyzing transaction data, banks can optimize processes, reduce waiting times, and improve the overall customer experience. For instance, data-driven insights can help banks streamline branch operations, optimize ATM placements, and manage workforce allocation more effectively. Risk management, particularly in the areas of fraud detection and credit risk assessment, also benefits from data analytics. Advanced analytics techniques can detect unusual patterns and flag potential fraud in real-time, reducing the risk of financial losses and enhancing trust. Similarly, predictive models can assess credit risk more accurately, ensuring that banks make informed lending decisions and maintain a healthy loan portfolio. The adoption of data analytics in banking is not without challenges. Issues such as data privacy, security, and the need for skilled personnel to interpret and act on data insights are significant considerations. However, with the right strategies and technologies in place, these challenges can be effectively managed, paving the way for substantial improvements in service quality. Data analytics offers a powerful toolset for banks aiming to enhance service quality. By leveraging data-driven insights, banks can deliver more personalized, efficient, and secure services, ultimately leading to greater customer satisfaction and competitive advantage. As the banking sector continues to evolve, the integration of data analytics into service quality improvement strategies will be essential for staying ahead in a competitive market.

The study aims to evaluate the efficiency of the banking sector in Jordan from 2011 to 2022, employing the Data Envelopment Analysis (DEA) methodology, Slacks-Based Models (SBM), and Logit regression models. The findings show that, overall, the Jordanian banking sector demonstrates high efficiency, though there is variation across individual banks, despite the economic and geopolitical challenges faced during the study period. The study also examines the factors influencing the efficiency of Jordanian commercial banks, including banking and economic variables, as well as the impact of the COVID-19 pandemic. The results reveal that capital adequacy and Fintech adoption have the most significant positive effect on bank efficiency, while market concentration, measured by the Herfindahl-Hirschman Index (HHI), has the most substantial negative impact. The paper emphasizes the importance of continuous improvements in risk management, technological adoption, and operational efficiency, as well as fostering greater competition between banks. Additionally, it highlights the critical role of macroeconomic stability in supporting long-term banking sector efficiency.

Purpose This study aims to develop a framework for evaluating environmental, social and governance (ESG) factors in the context of commercial banks, addressing the need for a more robust and transparent assessment of ESG subfactors. Design/methodology/approach Using an integrated multi-criteria decision-making approach, this study uses rough stepwise weight assessment ratio analysis (R-SWARA) to determine the weights of ESG factors, followed by the combined compromise solution (CoCoSo) method to assess the sustainability performance of five major commercial banks. This study involves insights from 15 experts in the banking sector, ensuring a comprehensive understanding of ESG integration. Findings The research reveals that governance is the most significant ESG factor in the banking sector, followed by social and environmental factors. The CoCoSo method’s results, aligning with the R-SWARA findings, identify the top-performing banks regarding ESG practices, highlighting the importance of robust governance structures for sustainable banking operations. Research limitations/implications This study provides a strategic framework for banks to prioritize and implement ESG initiatives effectively. It provides insights into allocating resources toward areas with the most significant impact on ESG performance, thereby enhancing operational efficiency and stakeholder trust. Originality/value This study contributes to the existing literature by offering a unique, integrated approach to ESG evaluation in the banking sector, combining the qualitative and quantitative aspects of ESG factors. It addresses the subjectivity issue inherent in ESG evaluation and provides a comprehensive ranking system for ESG factors in commercial banks.