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This chapter reviews new technologies, new types of players and new types of financial products that together are fundamentally affecting supply and demand dynamics in the financial sector and contributing to the emerging digital financial landscape. The aim of this chapter is to set a common understanding on the underlying forces of digital disruption in the financial sector before exploring the challenges to monetary and financial stability that are arising. In later chapters, the book will examine how central banks might deal with the challenges and help shape the emerging digital financial landscape.

This chapter introduces what a digital ID is, why it is important, how it works, the design choices, as well as how central banks can collaborate with other stakeholders in promoting digital ID infrastructures for use in digital financial services.

Fintech has revolutionized personal finance, introducing innovative tools that offer unprecedented access, efficiency, and security in managing finances. This chapter explains fintech's personal finance applications, from intuitive budgeting apps and advanced robo-advisors to peer-to-peer payment platforms. It articulates how these tools have shifted the control and management of finances into the hands of consumers, providing real-time financial data, customized investment strategies, improved credit scores, and streamlined transactions that eliminate the need for traditional intermediaries. Furthermore, this chapter features a select list of FinTech50 firms and highlights how individuals can leverage their services. This comprehensive guide is invaluable for individuals seeking to leverage fintech for personal finance optimization and for professionals keen on understanding and navigating the rapidly evolving fintech landscape.

The surge of artificial intelligence (AI) applications and subsequent adoption by consumers and marketers has ignited substantial research exploring the benefits and opportunities of AI. Despite this, little attention has been given to its unintended negative consequences. In this paper, the authors examine both the practitioner and academic sides of ethical AI. In doing so, the authors conduct an extensive review of the AI literature to identify potential issues pertaining to three areas: individual consumers, societal and legal. The authors identify gaps and offer questions to drive future research.

The authors review recent academic literature on AI in marketing journals, and top ethical principles from three top technology developers (Google, IBM and Meta) in conjunction with media reports of negative AI incents. They also identify gaps and opportunities for future research based on this review.

The bibliographic review reveals a small number of academic papers in marketing that focus on ethical considerations for AI adoption. The authors highlight concerns for academic researchers, marketing practitioners and AI developers across three main areas and highlight important issues relating to interactive marketing.

This paper highlights the under-researched negative outcomes of AI adoption. Through an extensive literature review, coupled with current responsible AI principles adopted by major technology companies, this research provides a framework for examining the dark side of AI.

This chapter examines three common fintech use cases transforming the financial industry. First, the chapter introduces fintech's role in enhancing financial services and promoting financial inclusion, especially through digital platforms. Second, it investigates various fintech applications that support financial institution management by harnessing the power of artificial intelligence (AI) and machine learning (ML). Finally, the chapter explores fintech use cases related to the regulatory environment, including regulatory technology (regtech), blockchain technology, and cryptocurrencies. The insights presented in this chapter cater to researchers and practitioners keen on better understanding fintech's diverse applications in the ever-evolving financial industry landscape.

We explore the effect of selective schooling, where students are assigned to different schools by ability, on adult health, well-being and labour market outcomes. We exploit the 1960s transition from a selective to a non-selective secondary schooling system in England and Wales. The introductio3n of mixed-ability schools decreased average school quality and peer ability for high-ability pupils, while it increased them for low-ability pupils. We therefore distinguish between two treatment effects: that of high-quality school attendance for high-ability pupils and that of lower-quality school attendance for low-ability pupils, with mixed-ability schools as the alternative. We address selection bias by balancing individual pre-treatment characteristics via entropy balancing, followed by ordinary least squares (OLS) regression. Selective schooling does not affect long-term health and well-being, while it marginally raises hourly wages, compared to a mixed-ability system, and school aspirations for high-ability pupils. Cognitive and non-cognitive abilities measured prior to secondary school are significantly and positively associated with all adult outcomes.

The paper aims to examine the impacts and ethics of utilizing Artificial Intelligence (AI) in Indian policing. It explores both the positive and negative consequences of using AI, as well as the ethical considerations that have be taken into account.

This study is based on secondary sources of information, such as national and international reports, journal articles, and institutional websites that discuss the use of AI technology by the police in India.

AI has proven to be effective in policing, from preventing crime to identifying criminals, by detecting potential crimes in advance with fewer resources and in more areas. In India, the police use AI technology not only for facial recognition but also for crime mapping, analysis, and building blocks. However, factors such as caste, religion, language, and gender continue to cause conflict. India has shown a strong interest in using AI technology for policing, and wishes to accelerate its implementation in various policing contexts, including law and order. This paper calls for an assessment of the complexities and uncertainties brought about by new technologies in policing with ethical considerations.

This paper can provide valuable insights for policy-makers, academics, and practitioners engaged in discussions and debates concerning the ethical considerations associated with the adoption of AI tools in policing practices.

This chapter looks at challenges that are arising from emerging business models and those that are related to digital finance in general. This chapter first looks at the four challenges relating to new business models, i.e. walled gardens, shadow banking, monetary sovereignty and singleness of money. The chapter then looks at the four challenges relating to digital finance in general, i.e. consumer's data rights, AI ethics, cybersecurity and financial exclusion.