Financial technology, or Fintech, has captured the attention of scholars, students, and institutions across worldwide for over a decade. With a plethora of new financial services, products, and innovative methods to engage with clients, the impact of technology on the financial sector has been extensively studied. This research paper provides a summary of scientific research on FinTech by using bibliometric analysis. Using the Scopus database, the paper analyzed 665 publications and identified research gaps and new study topics through “VOS-Viewer” software and “Biblioshiny” using RStudio. The study focused on FinTech’s functions and research constraints in digital finance by assessing citation links between the most significant articles. The findings provide a starting point for further investigation and offer opportunities for researchers to expand their expertise in exciting and innovative studies. Overall, this study seeks to help researchers discover new avenues for exploration in Fintech while advancing their present understanding. There exists much scope in the area of Digital Lending, Supply Chain Finance, the Internet of Things, and RoboAdvisers.

Social insurance has been a pivotal tool in implementing social security. The purpose of the study is to analyze the existing information clusters (areas) in the field of social insurance. Clusters define related and unrelated groups in the field of social insurance. These groups will help streamline and identify areas where little or no research has been conducted to present. To achieve the objective, the study employed a precise and systematic procedure to gather 562 journal articles published in Scopus-indexed journals from 1926–2022. Subsequently, VOSviewer, Science of Science (Sci2), and Gephi were utilized to conduct bibliometric analysis (such as keyword co-occurrence and bibliographic coupling) and network analysis tests (such as citation and co-citation analysis). The results of keyword co-occurrence and co-citation analysis suggest there are three knowledge clusters: welfare provisions, benefits provided by social insurance, and social insurance operational aspects. Through analysis found top article-based Inequality, social insurance, and redistribution with 408(LC) and 1042(GC) and its page rank value is 0.010574 through prestigious analysis. Additionally, it is also observed that I. Nielsen had made the most substantial contributions as an author, with R. Smyth and C. Nyland following closely in the rankings. Also, observed maximum total link strength with 109 value on social security variable. The study also drawn attention to specific deficiencies, including regional concentration of research, insufficient research in developing and underdeveloped countries, inadequate knowledge sharing among researchers, limited methodological diversity, and a lack of research on the role of social insurance in facilitating society’s recovery from the pandemic.

This paper empirically investigates the impact of foreign institutional investors on Indian market returns volatility using symmetric and asymmetric volatility models like Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) respectively. The study has been conducted on two samples using Nifty 50 returns and foreign institutional investors purchase, sale and net investment from the year 2000 in whole sample and from the year 2009 in sub sample on daily basis. The dummy variable is used for global financial crisis that has significantly increased the volatility of Nifty 50 returns. The study has found that foreign institutional investors reduce the volatility in Indian market in most of the cases while net investment by foreign institutional investors increase the volatility of Indian markets. Furthermore, the study has surpassed previous studies by employing symmetric and asymmetric models on same sample of data and in observing that the outcome of this study is largely dependent on frequency of the data and selection of the methodology. The study suggested that there should be some capital control measures in short term and encourage long-term investment.

AI is becoming more common in financial markets. It has transformed how risks are detected, how fraud is stopped, how trading across borders is done, and how laws are obeyed. But technology also brings up new problems with rules, ethics, and how things are run. Finance is global, and global systems need governance institutions that are transnational and that make sure that everyone is treated fairly, held accountable, and included in society. This chapter examines the concept of global governance of AI in finance, emphasizing equity in institutions beyond national boundaries. It looks at the problems caused by fragmented national regulation and opposing economic interests. It also stresses the need for cooperative frameworks that protect consumers, investors, and weak economies. The chapter concludes by emphasizing the necessity of worldwide regulatory harmonization, transparent AI audit mechanisms, and equitable access to emerging financial technology.