Monetary & Financial Economics

Abstract This research examines how to improve supervisory effectiveness through AI functions in Iranian banking institutions using MAXQDA software. With the increasing complexity of banking operations and regulatory requirements, AI has emerged as an essential transformative technology that has great potential to enhance supervisory functions along with providing services and security measures. The research findings show that AI technologies including machine learning and natural language processing are able to accurately detect anomalies and fraud by monitoring large sets of financial data in real-time operations. This technology provides effective results through intelligent systems in three important areas including anti-money laundering (AML), financial advisory services, and improving customer experience with intelligent chatbots. Through these technologies, banks achieve higher operational efficiency, reduce human errors, and accelerate response times, which leads to increased customer trust and security. However, implementing AI in banking operations requires solving three fundamental challenges: transparency of system performance, data protection measures, and the development of appropriate legal frameworks to ensure privacy and security. This study, using qualitative methods and analyzing data collected in the software environment, examines the key variables affecting the supervisory performance of banks and provides operational solutions for the optimal and beneficial use of AI. The results of this study indicate that integrating AI into the Iranian banking system can lead to improved financial supervision, reduced risks, customer satisfaction, and the realization of smart and resilient banking.

Abstract 1. INTRODUCTION
In addition to creating a wave of infections and deaths, the COVID-19 pandemic has involved various countries in a particular economic crisis. Therefore, researchers have tried to study and investigate the issue from different aspects of this field. For example, some researchers showed that the rapid spread of the COVID-19 pandemic had a great impact on financial markets and caused heavy losses to investors in a short period (Mou, 2020). Others showed that the main economic effects of the COVID-19 pandemic were not caused by death, illness, and the duration of patient care, but rather fear and discrimination were the main factors and drivers affecting the economy in this crisis (Gong et al., 2020). Anyway, these studies should be supported by review articles including bibliometric articles. As far as the author has checked, no special research has been done in the bibliometric analysis of articles in the subject area of COVID-19 and economics in Iran. Therefore, this bibliometric study attempts to quantitatively analyze the literature on this field in Iran and visualize the knowledge structure in this field to help future researchers and policymakers.
2. METHODOLOGY
This applied study was conducted based on bibliometrics. The statistical population of the research includes all the articles published in the field of the COVID-19 pandemic and economics in scientific journals in the field of economics in Iran from February 2020 to January 2023. Related articles were extracted and analyzed by referring to the website of these journals and through their advanced search section and limiting the search to the words "coronavirus", "COVID-19", "epidemic", and "pandemic". The software used in this research is Excel, VOSviewer, and RavarMatrix software, each of which was used for a part of the research.
3. RESULTS & DISCUSSION
3-1. The process of publishing articles in the field of COVID-19 researches and the economics in Iran.
Results show that the trend of publishing articles in the field of COVID-19 research and economics in Iran is on the rise, and the highest number of published articles is related to the year 2022 with 32 titles and 39% of all articles. In any case, the publication of scientific articles in the field of COVID-19 research and economics in Iran, due to the large number of scientific journals in the field of economics in Iran, has not gone through a successful process in the studied period.
3-2. The intellectual structure of researches in the field of COVID-19 and the economics in Iran.
Evaluation of the intellectual structure of research in bibliometric analysis is usually done by examining the most frequent keywords, and the words co-occurrence. The most frequent keywords in identifying the main core of topics that make up the intellectual structure of research and the co-occurrence of words in identifying clusters, the time trend, and also the density of keywords that make up the intellectual structure of research from the point of view of introducing dominant, developed, saturated, extinct or emerging topics, they are used.
In this research, to investigate the most frequent keywords, the main words used in the title and the keywords selected by the authors in each of the articles were used. Results show that researchers in the field of COVID-19 research and economics in Iran have used 518 keywords in their articles. Keywords of COVID-19 with 82, Tehran Stock Exchange and pandemic each with 14, stock market with 9, industry and oil price each with 8 repetitions in order of the most frequent keywords of the main core of the topics that make up the intellectual structure is the researches in the field of COVID-19 and the economy in Iran, which has started in recent years.
To investigate the words co-occurrence also, social network mapping was used. Analysis of the social network map of the co-occurrence of words through three methods 1. Analysis of clusters of the most frequent keywords, 2. Investigating the time process of formation of clusters of the most frequent keywords, and 3. Density analysis of the most frequent keywords is done. Due to its novelty and the small number of articles in this field in Iran, analysis of the articles by examining the time trend and the density of the most frequent keywords will not be meaningful. Therefore, the co-occurrence of words was done only through the analysis of clusters of the most frequent keywords.
In any case, drawing the keywords co-occurrence map in the field of COVID-19 research and the economics in Iran only led to the identification of one thematic cluster. This cluster has eight main keywords including; COVID-19, Tehran Stock Exchange, pandemic, stock market, industry, oil price, economy, and exchange rate connected by red lines. Thus, it can be said that the keywords of this cluster show the direction of studies on COVID-19 and the economics in Iran, which has started in recent years.
3-3. Cooperation networks in the field of COVID-19 researches and the economy in Iran.
One of the most important cases in bibliometric evaluations is the analysis of collaboration networks between authors and universities. Knowledge of these networks facilitates the provision of financial and human resources for research and leads to the improvement of scientific interactions and communications (Ding, 2011).
In this research, to investigate collaboration networks among authors, the map of collaboration networks based on co-authorship was used.
To investigate cooperation networks among universities, the map of social networks based on co-authorship was also used. However, due to the lack of data related to universities in the reference section of Iranian journals, instead of using the relations of citation and keywords, the focus was on the direct relationship between universities.
In any case, the examination of the map of cooperation networks among authors and universities in Iran shows that Although there has been good cooperation between universities in the subject area of COVID-19 and economics, there has not been an outstanding subject connection between the authors in this area.
4. CONCLUSIONS