Green finance is significant since it is the first organized effort by the financial industry to link financial performance with a positive environmental impact. Green finance products are being developed appropriately to achieve sustainability. The present study employs a fundamental bibliometric methodology to assess the current state and progress of academic research on green finance. 1748 papers are taken for this study. Data are extracted from a scholarly database i.e., SCOPUS and for network analysis, VOSviewer software is used. The present paper is focused on six research questions. Information is gathered to examine the above research questions and network maps are applied. We examined year-wise document publications, types of documents, subject areas, most influential articles, different journal sources, co-authorship of countries, and co-occurrence of keywords of green finance. We categorized keywords into clusters and discovered new trends in green finance. The paper also highlighted the recent issues and challenges. The study has also certain limitations and it is concluded by providing implications and suggestions for future studies. At last, this paper will give more insights to researchers, academicians, and others to discover the research gaps in this field of green finance.

The creative and digital content business, among many others, is going through a significant paradigm shift as a result of advances in AI technology. AI technologies are enhancing human talents and changing the creative scene in a variety of ways, including content production and curation. The potential for innovation and expansion could appear endless, revolutionary, and forward-thinking, but difficulties like job loss, issues with originality and ethics, and the dissolution of human connections are all producing an underlying worry and anxiety across many industries. The interaction between people and AI fosters creativity and creates new opportunities for content production. Academic dishonesty undermines the educational process and undermines the reputation of academic institutions. A few cases catalogued from the educational contexts have been highlighted to evaluate the bright and dark sides of using AI affecting the academic integrity and reliability of substances used in the modern world.

Considering the snakebite effect experience of investors and their decision-making in the era of robo-advisors, this study focuses on examining the mediating role of the snakebite effect between the value of robo-advisors and investment decisions and assessing the moderation of social-emotional well-being and resilience among active investors. The research process began with an exhaustive review of existing literature and the development of a structured questionnaire. A further survey was undertaken by collecting 361 responses from active investors residing in the region of South India using robo-advisors, and finally, the mediation and moderation were analyzed utilizing confirmatory factor analysis (CFA) to check the model fit and Structural Equation Modelling (SEM) to test hypothetical relationships. The results validate the intervening role of the Snakebite Effect in the relationship between the value of Robo-Advisors and investment decision-making. Further, social emotional well-being and resilience of investors significantly lessen the negative impact of the snakebite effect on investment decision-making. The role of social-emotional well-being and resilience is vital as high tendency leads to a low snakebite effect, better effectiveness of robo-advisors, and investment decision-making. This study provides various theoretical, practical, and managerial implications for improved robo-advisory services and increased adoption among diverse investor segments. In particular, the study emphasizes that financial institutions should focus on hybrid advisory models that combine the analytical capabilities of robo-advisors with the empathetic, personal touch of human advisors.

At the present days, the popularity of the stock marketing is very high as this is only the legal platform where the investor can doubling their investments within very short span. However, besides the chance of profitability, there is huge risk present due to the extreme volatility. In this context, to reducing the risk of financial this research study has developed a AI detection model so that a predictive future data related to the stock exchange can be obtained through the past data train. In order to predict where prices will go in the future, this model uses machine learning algorithms and NLP techniques to decipher historical market data. Backtesting on simulated data demonstrates that high levels of accuracy, precision, recall, and Fl score may be attained in the prediction of market volatility using the proposed model. The proposed model may be used by investors and financial institutions as a tool for making more informed investment decisions, reducing risk, and identifying untapped investment opportunities. An important contribution to the field of finance, this research shows how machine learning algorithms and natural language processing methods show potential in predicting market volatility.