Quarterly Studies in Banking Management and Islamic Banking

Abstract In this study, the challenges and limitations of the Iranian banking system in financing investment in the industrial sector during the years 2006-2013 were assessed with an analytical approach based on official data. Considering the principles of Islamic economics and the need to direct credit to productive activities, this issue is of particular importance. Considering the pivotal role of financing in sustainable growth, this study examined the bank-based structure of the Iranian financing system and estimated the effects of key factors, including banking facilities in the industrial sector, real interest rates on deposits as an opportunity cost of investment, inflation rates, and exchange rates, on industrial investment using the ARDL Model. The findings show that factors such as low capital adequacy ratios, chronic bank balance sheet imbalances, and overreliance on traditional instruments have weakened the capacity of the banking system to support industrial investment. This weakening is such that in the first eight months of 2023, only 35.5 percent of bank facilities were allocated to the industrial sector. Finally, based on the research findings, solutions such as developing and deepening Islamic financial instruments (including sukuk, mortgage-backed securities, and industrial project funds), reforming regulatory frameworks, and strengthening the regulatory role of the Central Bank of Iran have been proposed to align monetary policy with industrial development goals. It seems that these measures can pave the way for sustainable financing for the growth and transformation of the industrial sector of Iran.

Abstract With the rapid growth of data in today’s world, leveraging data science to develop optimal solutions has become essential. One of the key challenges in the banking industry is ensuring the repayment of granted loans and the efficient allocation of financial resources. Therefore, assessing and predicting customer credit levels can serve as a reliable and profitable indicator for banks. In this study, based on financial transaction data from customers of Mellat Credit Institution, the current credit level was analyzed using the RFM model combined with clustering techniques. In the proposed approach, the future credit level of customers was also predicted using a Random Forest machine learning algorithm built upon the results of model-based clustering. Compared with traditional static scoring methods, this approach improves prediction accuracy and enables more confident credit decisions. Using the Bayesian Information Criterion (BIC) to identify the optimal model and cluster count, five distinct customer clusters were detected through the RFM framework. Furthermore, a Random Forest model achieved an average accuracy of 72%, recall of 75%, and an F1-score of 91% for predicting customers’ future credit levels. By incorporating modern financial technologies, this research presents a data-driven framework for intelligent credit scoring in Islamic banking, contributing to enhanced predictive performance and reduced credit risk in loan allocation processes.

Abstract The present study aimed to investigate the effect of business intelligence adoption on the performance of bank support services companies with an emphasis on the mediating role of agility. To this end, a case study was conducted on Sepah Bank branches in Shiraz. The methodology of this study is of an applied and descriptive-survey type, and the statistical population includes all employees of Sepah Bank branches and those of Omid Net Infrastructure Support Company in Shiraz. The sample size was determined to include 310 people using the Krejcie and Morgan’s (1970) table, and simple random sampling method was used to select the sample. Data were collected through a questionnaire and its reliability was confirmed by means of the Cronbach's alpha coefficient being higher than 0.7. The results of the analysis show that agility acts as a mediating variable between business intelligence adoption and the performance of bank support services companies. Also, the tendency towards entrepreneurship has the greatest impact on the performance of these companies. This research helps to explain and clarify the role of business intelligence and agility in improving the performance of bank support services companies and can help managers of these organizations in making strategic and operational decisions. The results of this research can be the basis for future studies revolving around the performance improvement of organizations based on business intelligence and agility.

Abstract As quantum computing advances, the conventional foundations of information security, rooted in asymmetric cryptography and classical hash functions, face unprecedented vulnerabilities. This poses a critical challenge for Islamic banking, which is predicated on the authenticity, transparency, and Shariah compliance of financial contracts. While contemporary technological solutions frequently rely on blockchain infrastructures, both jurisprudential analysis and empirical evidence indicate that blockchain is not fully congruent with key principles of Islamic law, including the prohibition of gharar (excessive uncertainty) and the clear delineation of ownership. This paper proposes a blockchain-averse framework designed to strengthen the digital security of non-interest-based contracts by leveraging quantum key distribution and post-quantum-resistant cryptographic algorithms. Within this framework, three foundational Shariah principles—verification of contract authenticity, prevention of tampering and forgery, and assurance of ownership transparency—are embedded in a multilayered cryptographic architecture. After examining the doctrinal underpinnings of Islamic commercial law and anticipating future security requirements, the paper outlines the proposed model and assesses its feasibility within the Islamic banking system of Iran and those of other Muslim-majority jurisdictions. The findings demonstrate that it is possible to deliver a secure, ethically grounded, and scientifically robust framework for safeguarding Islamic contracts against quantum-era threats, thereby transcending blockchain’s limitations and preserving both public trust and Shariah integrity in the digital environment.

Abstract The present study aimed to examine change management in banks with an emphasis on innovative human resource models in the branches of Bank Mellat in Ardabil, Iran. In terms of purpose, this research is applied, and in terms of methodology, it is descriptive–correlational, conducted through a survey approach. The statistical population included 685 employees of the branches of Bank Mellat located in Ardabil. Using a stratified random sampling method, a sample size of 248 was determined, of which 220 valid questionnaires were ultimately collected. The data collection instrument was a researcher-developed questionnaire whose content validity was confirmed by experts, and its reliability, measured by Cronbach’s alpha, was at an acceptable level. Data were analyzed using structural equation modeling with SPSS and AMOS software. The findings indicated that the dimensions of change management, including leadership, readiness, and overcoming resistance, have a positive and significant effect on the dimensions of innovative human resource models, including recruitment, training, and retention of employees. These results suggest that improving each component of change management can directly enhance the quality and effectiveness of innovative human resource activities, thereby strengthening the bank’s capacity to attract, develop, and retain creative and innovative personnel. Consequently, coordinated planning for the simultaneous development of change management and innovation in human resource management is proposed as a key factor in ensuring the sustainability and competitiveness of banks

Abstract This study aimed to explain the role of customer-centric banking in enhancing customer loyalty among state-owned banks in Ardabil, Iran. The research is applied in purpose and descriptive–survey in nature, conducted through a mixed-methods (qualitative–quantitative) approach. In the qualitative phase, the statistical population consisted of banking experts, managers, and university professors specializing in digital banking. A total of 20 participants were selected through purposeful and snowball sampling until theoretical saturation was achieved. Data were collected via semi-structured interviews and analyzed using open, axial, and selective coding. In the quantitative phase, the statistical population included approximately 3,500 customers of state-owned banks in Ardabil. Based on the Krejcie and Morgan’s (1970) table, a sample of 346 customers was determined and selected through stratified random sampling. Data collection tools included a researcher-made questionnaire on customer-centric banking and Keeling’s customer loyalty questionnaire, which contains 12 items. The validity of the instruments was confirmed by experts, and reliability was established by means of the Cronbach’s alpha coefficient, being higher than 0.70. In total, 200 valid questionnaires were analyzed. Data analysis was performed using SPSS and LISREL software. The results indicated that customer-centric banking, through the dimensions of data-driven decision-making, internal cultural development, experiential learning, and service digitalization, has a positive and significant impact on customer loyalty, with customer-oriented organizational culture playing a central mediating role in this relationship.