Financial and Banking Strategic Studies

Abstract Purpose: This paper aimed to explain the effect of information transparency and financial reporting quality of banks on customers’ trust in the value of the national currency, with perceived fairness and the legal protection of stakeholders as mediating variables, given the importance of such trust under inflationary conditions and its role in the effectiveness of monetary policy, inflation control, and banking system stability.
Methodology: This research employed a descriptive–survey and correlational design. The statistical population consisted of individual customers of banks in Tehran, from whom 400 respondents were selected using a combined sampling method. Data were collected through a researcher-developed questionnaire containing 68 items and analyzed using structural equation modeling through the partial least squares method and the Sobel test.
Findings: The results showed that all twelve research hypotheses were significant. Among the direct paths, legal protection had the strongest effect on trust (0.448), followed by perceived fairness (0.302), whereas the direct effects of information transparency (0.103) and financial reporting quality (0.098) were weaker but significant. In addition, the indirect effects of information transparency on perceived fairness (0.075), financial reporting quality on perceived fairness (0.129), information transparency on legal protection (0.068), and financial reporting quality on legal protection (0.091) were all significant at the 0.01 level. The VAF values ranged from 0.398 to 0.569. The coefficient of determination for trust was 0.681, while the GOF = 0.603 and SRMR = 0.062 indices indicated an appropriate model fit.
Originality/Value: The originality of this research lies in developing and testing a model that explains the role of information transparency and financial reporting quality of banks in shaping customers’ trust in the value of the national currency, while simultaneously considering the mediating roles of perceived fairness and the legal protection of stakeholders. The findings indicate that, under inflationary conditions, these factors contribute to restoring customers’ trust in the value of the national currency not only through their direct effects but, more importantly, through strengthening perceived fairness and legal protection.

Abstract Purpose: In addition to benefits, the adoption of Artificial Intelligence (AI) technology in banks is associated with costs and risks. The fragmented nature of prior research findings highlights the need for a comprehensive understanding of the implications of AI adoption in the banking industry in order to support the development of more effective implementation processes. Accordingly, this study aims to examine the implications of AI adoption in the banking industry.
Methodology: In terms of purpose, the study is applied, and in terms of data, it adopts a qualitative approach. Relevant sources were identified through a systematic search using Persian and English keywords across major national and international databases, including Scopus, Web of Science, ScienceDirect, Noormags, Magiran, and others, covering the period from 2010 to 2026 and from 1389 to 1405 in the Iranian calendar. Following the screening process, 32 studies were selected for final analysis. Data were analyzed using Sandelowski and Barroso’s seven-step meta-synthesis method. The credibility of the classifications and findings was assessed through Cohen’s kappa coefficient, face validity, and expert judgment.
Findings: The analysis resulted in the extraction of 169 basic codes, which were subsequently compared, consolidated, and classified into 65 conceptual codes and 13 main categories. The identified categories included economic, operational and process-related, strategic and organizational benefits, among others; explicit and hidden costs; and technical, data, security, governance, implementation, and other risks.
Originality/Value: The results indicate that AI adoption in the banking industry is a multidimensional phenomenon that cannot be evaluated solely in terms of efficiency gains or cost reduction. The proposed framework can assist bank managers, policymakers, and regulatory authorities in making more accurate and balanced decisions regarding the design, implementation, and development of AI systems by simultaneously assessing their benefits, explicit and hidden costs, and implementation risks.

Abstract Purpose: Banks’ ownership structure is not merely a reflection of shareholding composition and may be associated with managerial power, political connections, and credit-risk conditions. This study examines the relationships of Chief Executive Officer (CEO) political connections and CEO structural power with banks’ ownership structure and evaluates the interaction of credit risk with these relationships.
Methodology: The study uses 119 bank-year observations from 14 banks listed on the Tehran Stock Exchange over the 2016–2025 period (corresponding to 1395–1403). Credit risk is measured using the ratio of non-performing loans to equity and the ratio of loan loss provisions to net interest revenue. The primary specifications are estimated using multivariate panel-data regression and Panel Generalized Least Squares. To assess the robustness of the interaction findings to model specification, hierarchical specifications that simultaneously include the constituent main effects are also considered.
Findings: CEO political connections and CEO structural power are positively and significantly associated with the ownership-structure indicator examined in this study. The interaction terms between the two credit-risk measures and these managerial characteristics are positive and statistically significant in the initial restricted specifications; however, their significance does not persist once the constituent main effects are entered simultaneously. The evidence therefore does not provide robust support for a stable moderating role of credit risk, and the interaction findings should be interpreted as specification-sensitive.
Originality/Value: By jointly examining political connections, CEO structural power, the ownership-structure dimension considered in this study, and credit risk, this research provides an integrated framework for analysing the links between governance mechanisms and banks’ risk conditions. The findings highlight the importance of considering managerial characteristics and credit-risk conditions together when interpreting ownership structure, while also showing that conclusions regarding credit-risk moderation require caution.

Abstract Purpose: The purpose of this study is to identify and prioritize Artificial Intelligence (AI) applications in the banking industry and to develop a decision-making framework to assist bank managers in allocating resources and selecting investment priorities for AI projects.
Methodology: This study is applied in terms of purpose and descriptive-analytical in terms of methodology, based on a Multi-Criteria Decision-Making (MCDM) approach. First, the main applications of AI and the criteria affecting their prioritization will be identified through a structured review of previous studies. Subsequently, the initial criteria and alternatives will be validated and refined using the Delphi method and the opinions of experts in banking, information technology, and AI. After determining the final criteria, the Best-Worst Method (BWM) will be used to determine the weights of the criteria, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) will be employed to rank AI applications. Finally, sensitivity analysis will be conducted based on changes in the criteria weights to assess the robustness of the results
Findings: Based on the review of previous studies, applications such as fraud detection, credit assessment and scoring, risk management, anti-money laundering, customer services and chatbots, process automation, identity verification, and cybersecurity have been identified as initial alternatives. In addition, six preliminary dimensions, including economic value, operational value, risk and security value, customer value, implementation feasibility, and regulatory and responsible AI requirements, have been identified for evaluating and prioritizing these applications. The Delphi results are expected to validate and refine these criteria and provide the basis for determining their weights and ranking AI applications in the banking industry.
Originality/Value: The contribution of this study lies in developing a banking-specific framework that, unlike studies focusing solely on AI applications or AI adoption factors, simultaneously compares multiple AI applications based on a set of economic, operational, risk and security, customer, implementation feasibility, and regulatory and responsible AI criteria. The integration of a literature review, Delphi, BWM, and TOPSIS enables the combination of scientific evidence and expert judgments and provides a systematic decision-support tool for prioritizing AI investments in the banking industry.

Abstract Purpose: By signaling systematic and idiosyncratic risks associated with transparency, corporate environmental disclosure and credit ratings enhance stock attractiveness; having evolved beyond a merely ethical obligation into a strategic necessity for risk management and the reduction of the cost of capital, they are consequently significant for the decisions of stakeholders and creditors. The purpose of this study is to examine the relationship between corporate credit rating and Environmental, Social, and Governance (ESG) disclosure with stock liquidity considering the role of auditor size and company losses.
Methodology: The research is applied in terms of purpose; in terms of nature and content it is a correlation type. Multiple regression based on panel data analysis (panel data) was used to test the relationship between variables and the significance of the model. The information from this study is taken from the financial statements of 164 companies during the period 2015-2024.
Findings: The results of the research hypotheses test showed that there is a positive and significant relationship between the disclosure of ESG information and credit rating with stock liquidity. The loss of the company has a negative moderating role on the relationship between the disclosure of ESG information with stock liquidity; But the moderating role of auditor size on the relationship between ESG and liquidity is not significant.
Originality/Value: This research shows that factors affecting liquidity in recent research, apart from financial ratios, have focused on the company's credit and reputation, as well as sustainability and environmental reports, and have become one of the key factors for companies that intend to attract investors and improve market liquidity

Abstract Purpose: This study analyzes the structure of mutual dependence and market risk clustering in Iran's banking industry. Specifically, it identifies connectivity hubs, bridging institutions, and behavioral groups of banks, examines the time-varying co-movement of their returns, and provides actionable recommendations for network-based risk management and financial stability monitoring.
Methodology: Weekly log returns of nine listed banks over 2015–2026 (593 weeks) were computed and, after percentile winsorization, a Pearson correlation matrix was estimated. The matrix was converted into a distance metric to build a minimum spanning tree and a threshold correlation network (0.25), yielding centrality measures. Ward's hierarchical clustering grouped the banks, and 52-week rolling correlations tracked temporal dynamics.
Findings: Correlations are positive yet heterogeneous; the highest coefficient links Saderat and Tejarat (0.773), while the lowest involve Shahr. In the minimum spanning tree, Saderat (betweenness 23) is the primary gatekeeper and Tejarat the secondary hub. In the strong network, Tejarat (strength 3.95) and Saderat (3.88) hold the greatest connection intensity. Clustering yields three groups: a six-bank core, a middle layer (Eghtesad Novin and Post Bank), and a single peripheral bank (Shahr). Rolling correlations rose during COVID-19 (mean 0.438) and the regional tensions (mean 0.430) relative to the overall mean (0.367), indicating intensified co-movement and temporary weakening of diversification during crises.
Originality/Value: By jointly applying network tools to long-horizon weekly returns and covering recent shocks, the study offers a multi-layered map of Iranian banking connectedness and distinguishes behavioral patterns from connection intensity—an approach rarely adopted in prior studies and complementary to interbank-data research.