Iranian Banking

In the aftermath of the global financial crisis (2007-2009), policymakers in the developing countries and emerging economies have generally relied on macroprudential policies to achieve financial stability. Since the banking system's vulnerability plays an essential role in financial instability, and the banking system's stability is exposed to vulnerability, we examine macroprudential policies' effectiveness in reducing banking vulnerability and economic instability through containing credit growth. We estimated a dynamic panel for 14 Iranian banks using GMM and Arellano-Bovar / Blundell-bond two-stage estimators during 2009-2018. The results indicate that the increase in lending rates in the interbank market leads to the banking system's contraction of lending capacity. The positive and significant effect of the economic growth index indicates the banks' procyclical behavior. That financial institutions in the business cycles behave procyclical in lending. The diminishing effect of the macroprudential policy index on the bank credit expansion indicates that macroprudential authority and policy tools' application reduces the banking system's instability and vulnerability. Therefore, to reduce financial intermediation instability, the financial sector regulator can institutionalize macroprudential policies.

This study explores how AI is perceived and operationalized in credit risk assessment within Iranian banking institutions, with a particular focus on the experiences of electronic banking professionals in Tehran. Drawing on grounded theory methodology and semi-structured interviews with 38 practitioners from both public and private banks, the research reveals a complex landscape of technological promise and institutional constraint. Participants emphasized the efficiency, consistency, and expanded analytical reach afforded by AI models, particularly in leveraging alternative data and enhancing fraud detection. However, these benefits are tempered by operational challenges, including fragmented data systems, outdated IT infrastructure, and opaque algorithmic outputs. Ethical and regulatory concerns—especially surrounding algorithmic bias, accountability, and the absence of formal oversight—emerged as significant barriers to responsible deployment. Moreover, organizational resistance, hierarchical decision-making structures, and cultural skepticism toward automation further complicate adoption. The findings suggest strong practitioner support for hybrid decision-making models that integrate AI capabilities with human expertise. This model offers a viable pathway toward responsible innovation, balancing the computational advantages of AI with the contextual judgment and ethical sensitivity of human agents.