رامین مجاب

Technological progress has transformed the payment industry. Although alternative payment instruments such as bank cards share some characteristics with banknotes, they are not direct competitors to cash. First, cash possesses unique attributes—its functionality without electrical power and its inherent anonymity. Second, statistical data indicate that the ratio of cash to M1 has stabilized at approximately 10% in recent years, primarily due to its substitution with demand deposits. In this paper, we model the demand for banknotes by incorporating technology-related characteristics alongside conventional theories of money demand. Our approach has important implications for central bank, particularly in liquidity management, currency issuance, effective management of physical currency, and continued investment in digital payment infrastructures. We adopt a macroeconomic perspective that considers banknote demand from both transactional and asset viewpoints. To this end, we estimate an autoregressive distributed lag (ARDL) model, which allows us to analyze both short-term and long-term coefficients within a framework of model uncertainty. A key focus of our study is the estimation of a payment system development index using logistic growth and Gompertz models, through which we assess its impact on cash demand. Our empirical evidence supports the notion of S-shaped growth in the adoption of payment technologies. The findings demonstrate that advancements in the payment sector have led to a significant decline in cash demand. Moreover, while increased real economic activity exerts a positive and significant influence on cash demand, a higher opportunity cost of holding cash has a pronounced negative impact.

Bank facilities can be categorized into large-scale and non-large-scale facilities based on their value and in compliance with banking regulations. Given the significant share of large-scale facilities in the country's total credit portfolio, the exposure to a limited number of borrowers, primarily major corporations, and their subsequent direct and indirect effects on the banking system and the economy, this paper investigates the credit concentration risk associated with large-scale facilities. Using the Herfindahl-Hirschman Index (HHI), the degree of name concentration in the allocation of large-scale facilities among beneficiaries was measured, and its impact on the default rate of these facilities was estimated using a Generalized Method of Moments (GMM) model for 15 banks listed on the Tehran Stock Exchange during the period 2022-2024. Additionally, the share of large-scale facilities in the total loans granted by each bank (as another concentration measure) was included in the model, along with the loans-to-deposits ratio and the capital adequacy ratio as control variables. The model results indicate stickiness and a statistically significant impact of the lagged default rate of large-scale facilities. It was observed that name concentration in the allocation of large-scale facilities has a negative effect on the default rate of these facilities. However, the share of large-scale facilities in total loans, as an alternative concentration measure, did not exhibit a statistically significant effect on their default rate. Furthermore, the loans-to-deposits ratio, which serves as an indicator of banks’ liquidity and resource management performance, had a significant negative effect. Additionally, an improvement in the capital adequacy ratio led to a statistically significant reduction in the default rate of large-scale facilities.

The integration of new technologies, although beneficial, poses significant regulatory challenges. This article emphasizes that while standardization is essential to prevent inconsistency and unpredictability, maintaining a certain degree of discretion is also necessary. Using insights from microeconomic theory and international best practices, the article categorizes regulatory challenges such as competition, conflicts of interest, job displacement, rent-seeking, security and privacy concerns, fraud, tax evasion, corruption, money laundering, and compliance with anti-terrorism measures. Several regulatory tools are identified to effectively manage these challenges. These include transparency and disclosure, collaboration and information sharing, financial incentives (grants, subsidies, and tax incentives), reducing barriers to market entry and exit, open standards and interoperability, and whistleblower protection. A survey was conducted to assess expert opinions on the gap between current regulatory practices and optimal standards for each tool. The results revealed notable discrepancies, particularly regarding barriers to market entry and exit. The results indicated that current regulations for market entry are overly restrictive compared to the optimal levels recommended. Additionally, the survey highlighted that reducing barriers to exit for large financial institutions requires greater discretionary flexibility compared to smaller firms. The results show a need for targeted regulatory adjustments based on institutional differences and systemic importance.

Relying on the Area Under the Curve (AUC) measure, we compare the performance of the Logit regression model and the LightGBM algorithm. Despite these methods being common in the literature, our study emphasizes the role of statistical inference to evaluate and compare the results comprehensively. We use the training set of the Vesta (2018) dataset, provided by Vesta—a global fraud prevention company headquartered in the United States specializing in payment solutions and risk management. Originally released as part of a Kaggle competition focused on credit card fraud detection, this dataset comprises diverse transaction records, representing a rich source for exploring advanced fraud detection methods. Our analysis reveals that while the LightGBM algorithm generally yields higher predictive accuracy, the differences between the calculated AUCs of the two methods are not statistically significant. This underscores the importance of using inferential techniques to validate model performance differences in fraud detection.

According to the Basel III regulatory framework, uniform minimum liquidity requirements have been imposed on all types of banks. Using an agent-based model of a banking system, we investigate the effects of liquidity requirements on banks' insolvency under two policy experiments in one of which the minimum liquidity requirements are applied uniformly and in the other differentially across banks. The model introduces a banking system with 12 heterogeneous banks that must also comply with two liquidity requirements while performing their daily activities of taking deposits and making loans. The model is applied to the Iranian banking system. Results illustrate that because banks respond differently to liquidity requirements, applying one size minimum liquidity requirements to all kinds of banks, strengthens the likelihood of a liquidity shock turns into banks' insolvency and could increase banking system instability. Thus our findings highlight that to achieve financial stability at the national level, policymakers should revise the current one size fits all approach when designing liquidity requirements.