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Purpose: Blockchain technology has the potential of revolutionising finance in general and entrepreneurial finance in particular. As this area is hitherto uncharted, this chapter delineates its scope to the use of blockchain technology for providing strategic business funding. Blockchain technology started off by floating initial coin offering as a way of providing long-term funds to the startups. Since then, it has evolved quickly and has already spawned several iterations. One of its most prominent offshoots, security token offering has evolved into a distinct financing resource, with its own unique characteristics.

Methodology: This chapter discusses the implications of this development for startups and new ventures. It focusses on the use of blockchain technology for innovations in the domain of entrepreneurial finance with the aim to document the emergence of cryptofinance as a viable source of funding for startups. For this purpose, a systematic literature review methodology has been undertaken. In order to provide a comprehensive view, various sources such as academic research papers, working papers and practice papers have been used for collating information.

Practical implications: This chapter provides a brief overview of entrepreneurial finance and blockchain technology, illustrating their unique aspects. It proceeds to discuss the use of blockchain technology in finance in general and in entrepreneurial finance in particular. This discussion is followed by an extensive analysis of the evolution of cryptofinance as a funding source, enumerating various iterations and their implications.

Conclusion: As the use of blockchain technology in entrepreneurial finance is still at the nascent stage, the regulatory paradigm is still evolving. The current work also looks at the development of legal framework for managing these innovations across different prominent markets. This chapter further delves into the likely future course of development for cryptofinance. It mainly focusses on the emergence of decentralised finance as a comprehensive ecosystem and smart contracts.

On 20 March 2020, the four adult convicts of the 2012 Delhi rape case were executed after a long debate regarding the punishment for their crime. The Delhi rape case, unlike others, was also given to the fast track court because of the worldwide outrage India received in its aftermath. Otherwise, most rape survivors rarely speak out and if they do, their lives are often endangered and threatened, depending on the severity of the case itself and the perpetrator's rank in the society. Through the analysis of Aniruddha Roy Chowdhury's, 2016 film Pink, and Ajay Bahl's film Section 375 (2019), this chapter explores the different ways in which mainstream Hindi cinema deals with such questions, especially in its depictions of courts. Both these films foreground India's contemporary cultural systems of fear that silence the rape survivors. They also imply that in the court cases, unless the specific court case faces intense global publicity, as was the case of the Delhi gang rape, rape survivors will never want to speak out. Moreover, the rape survivors will also hesitate to file a First Information Report (FIR) – a document that records crimes by the police against their perpetrators – limiting any possibility for justice for them. The laws surrounding rape cases are obscure and complex and finding justice for a rape victim (unless it is on a global level) is not an easy venture in India. At the time of the #metoo movement, the rape laws in India are not designed in such a way to arguably encourage victim-survivors to speak up. Instead, if rape survivors do decide to confront their perpetrators, they not only face ostracisation from society but also the danger of losing loved ones and endanger their lives as well.

Purpose: The study aims to extract insights into five significant industries, pharmaceutical, space, defence, renewal energy, and information technology (IT), which have huge potential to make India achieving a five trillion-dollar economy in the future.

Design/methodology/approach: The authors focus on future-driven industries which are not only making India a third highest gross domestic product (GDP) producer country but also reviewing the different aspects of these industries and how they can assist India in achieving a five trillion-dollar economies along with determining India’s self-reliance through different governments initiatives in this direction.

Findings: The findings highlight the importance of inclusiveness of policymakers, stakeholders, private players, foreign investors, and the masses. Their significant contributions especially in the pharmaceutical, space, defence, renewal energy, and IT sectors in terms of creativities, innovations, intellect, executions, implementations, and improvements can assist India in achieving its five trillion-dollars economy soon.

Practical implications: This study offers (1) convincing insights into five key industries, pharmaceutical, space, defence, renewal energy, and IT, which have huge potential to increase total GDP volume shortly and (2) the investment areas for the masses where they can see their world not only self-reliant but also will see huge growth in their invested amount in these industries in future.

Originality/value: The insights of five key industries, pharmaceutical, space, defence, renewal energy, and IT, highlight that India has the potential to achieve a five trillion-dollar economy in the future; however, it does not ignore the significant contribution of other industries in making of total GDP.

In the stock market, herding behavior occurs when investors mimic the actions of others in their investment decisions. As a result, the market becomes inefficient and speculative bubbles form. This study aims to investigate the relationship between information, overconfidence, market sentiment, experience and national culture, and herding behavior among Malaysian investors. A total of 400 questionnaires are distributed to bank institutions' investors. The survey design based on cross-sectional data is analyzed using the Partial Least Squares Structural Equation Model. The results indicate that information, market sentiment, experience, and national culture are positively related to herding behavior, while overconfidence has no effect. With this, the government should strengthen regulations to prevent the dissemination of misleading information. Moreover, investors are encouraged to overcome narrow thinking by expanding their understanding of different cultures when making investment decisions.

Traditionally, loan officers use different credit scoring models to complement judgmental methods to classify consumer loan applications. This study explores the use of decision trees, AdaBoost, and support vector machines (SVMs) to identify potential bad loans. Our results show that AdaBoost does provide an improvement over simple decision trees as well as SVM models in predicting good credit clients and bad credit clients. To cross-validate our results, we use k-fold classification methodology.

Purpose: This chapter discusses the role and use of chatbots adopted by the different categories of banks (private and public sector banks) in India. The chapter presents brief essential services offered by Indian chatbots regarding accuracy, technology providers and virtual assistance, ways to connect, etc. This chapter concluded that most of the questions answered by the Indian chatbots are already available on the banks’ websites, and there is a need for enhancement in the capabilities of Indian chatbots.

Need for the Study: The need for the study is based on the working of banking chatbots, customer query handling, and the efficiency of the chatbots in India. The chapter helps to analyze the services offered by various banks.

Methodology: This chapter is based on secondary data collected from banks’ websites and articles from various journals. The study is based on nine banks (both private and public sectors) those are having working chatbots (SBI, HDFC Bank, ICICI Bank, Yes Bank, IndusInd Bank, Kotak Mahindra Bank, Axis Bank, Andhra Bank, Bank of Baroda). The present study is focused on chatbots, their services, and software applications for various customer-handling capacities.

Findings: The research concluded that Indian banks are investing a small amount in using chatbots, yet Indian chatbots are deficient regarding far too provincial administrations as they are adequate just for standard and basic inquiries. Also, Indian customers are not properly aware of chatbots and virtual assistance.

Practical Implications: This study provides an overview of the working chatbots in India (for both public and private sector banks) and their functions, as well as the capacities of these chatbots. The previous conducted studies are based on the uses, importance, and working of chatbots/artificial intelligence (AI) in banking. In this study, after discussing the different services, it is found that Indian banks need to update their AI/Virtual assistance with more features.