فاطمه راستی

Regulations regarding financial technologies (fintech) refer to laws that aim to balance financial innovation with the maintenance of security, transparency, and financial stability in digital markets. This research study aims to analyze the trends, challenges, and future directions in the field of fintech regulations using a Bibliometric approach. To this end, relevant keywords were initially searched in Scopus and Web of Science databases, resulting in the selection of 191 papers as the research dataset. Subsequently, the Bibliometrix package in R was employed to identify the influential authors and institutions, analyze temporal trends, and detect the related research clusters. The results indicated that research on fintech regulations has shown a significant growth. Core challenges in this field include maintaining data security, achieving international regulatory coordination, and facilitating the acceptance of financial innovations. Ultimately, this study provided some insights into the challenges of regulating innovation and offered guidance for researchers, policymakers, and fintech professionals in understanding the current trends and designing more effective regulatory frameworks to support the financial innovation.

This systematic mapping study provides a comprehensive review of the existing literature on Fintech and its role in banking, exploring the current state, development, and future prospects of Fintech research. By analyzing 687 Fintech-related articles from academic databases covering the years 2015 to 2024, this article examines the evolution of Fintech. After describing the process of this phenomenon we identified a significant increase in research activity within this field during the past 5 years. This study offers a unique viewpoint, enabling both researchers and practitioners to reconsider the future direction and scope of Fintech research. This paper reviews the literature on Fintech and its interaction with banking, encompassing innovations in payment systems, credit markets, and insurance, with Blockchain-powered smart contracts also playing a role. It defines Fintech, presents relevant statistics and key insights, and reviews both theoretical and empirical studies. This review is centered around research questions, summarizing current knowledge, and concluding with recommendations for future research avenues.

Data mining is known as one of the powerful tools in generating information and knowledge from raw data, and Clustering as one of the standard methods in data mining is a suitable method for grouping data in different clusters that helps to understand and analyze relationships. It is one of the essential issues in the field of investment, so by using stock market clustering, helpful information can be obtained to predict changes in stock prices of different companies and then on how to decide the correct number and shares in the portfolio to private investors and financial professionals' help. The purpose of this study is to cluster the companies listed on the Tehran stock exchange using three methods of K-means Clustering, Hierarchical clustering, and Affinity propagation clustering and compare these three methods with each other. To conduct this research, the adjusted price of 50 listed companies for the period 2019-07-01 to 2020-09-29 has been used.  The evaluation results show that the obtained silhouette coefficient for K-means Clustering is higher and, therefore, better than other methods for stock exchange data. In the continuation of the research, calculating the co-integration of stock pairs that have the same co-movement with each other were identified, and finally, clusters were compiled using the t-SNE method.