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Financial technologies form the heart of considerable disruptive innovation. Fintech is the emerging financial infrastructure for modern business. Big data are the feedstock for artificial intelligence (AI) that drives many fintech sectors – start-up finance, commodities and investment instrumentation, payment systems, currencies, exchange markets/trading platforms, market-failure response forensics, underwriting, syndication, risk assessment, advisory services, banking, financial intermediaries, transaction settlement, corporate disclosure, and decentralized finance. This chapter demonstrates how analyzing big data, largely processed through cloud computing, drives fintech innovations, scholarship, forensics, and public policy. Despite their apparent virtues, some fintech mechanisms can externalize various social costs: flawed designs, opacity/obscurity, social media (SM) influences, cyber(in)security, and other malfunctions. Fintech suffers regulatory lag, the delay following the introduction of novel fintechs and later assessment, development, and deployment of reliable regulatory mechanisms. Big data can improve fintech practices by balancing three key influences: (1) fintech incentives, (2) market failure forensics, and (3) developing balanced public policy resolutions to fintech challenges.

This chapter examines the world of risk management within fintech. It initiates by emphasizing the crucial role of technology and risk assessment in shaping the fintech landscape. It discusses various risk categories prevalent in fintech operations, elucidating the nuances of technology, operational, compliance, strategic, and reputational risks. A comparative analysis across different fintech sub-sectors unveils their distinct risk profiles. The narrative extends to proactive risk management frameworks, contrasting prominent models like the COSO ERM, FAIR Risk Quantification, and NIST Cybersecurity Frameworks. Integral defense measures are scrutinized, encompassing data encryption, access controls, vulnerability assessments, and incident response plans. This chapter underscores the significance of building operational resilience through robust technology infrastructure, regular system updates, disaster recovery planning, and business continuity measures. Ultimately, this chapter culminates in a comprehensive summary, offering pragmatic recommendations to fortify technology risk management in fintech.

Innovation in service, procedure and product design is essential for long-term success in today's fast-paced and cutthroat hospitality sector. This study aims to learn how innovation may revolutionise the hospitality sector and lead to memorable guest experiences.

The research delves into new ways of thinking about service design, emphasizing how to create engaging and individual customer experiences (CXs). In order to stand out in a crowded hospitality market and keep up with customers' ever-changing demands, businesses in the industry are experimenting with new approaches to service, like co-creation, personalisation and experience design.

The study also digs into process innovation in the hotel industry, looking at how the latest tech and automation are helping to streamline processes and boost productivity. Reservation systems, guest check-in and check-out, cleaning and supply chain management are just a few areas that get studied. The study delves into how thoughtful product design may enrich visitors' hotel stays. It explores new and interesting services like in-room entertainment, eco-friendly building techniques and creative cuisine. The study investigates how these unique items affect customers' opinions of the products' worth, satisfaction and loyalty.

Methods such as in-depth interviews with experts, guest surveys and the examination of case studies highlighting cutting-edge design in the hospitality industry are all part of the research strategy. This project seeks to provide useful insights and recommendations for hospitality firms that want to adopt innovative service, process and product design methods by analysing real-world instances and gathering empirical data.