Sachin Chaudhary

The increasing size and scope of FinTech-related financial markets are changing how startups get funded by combining digital platforms, alternative funding models and data-based financial services. These industry developments create opportunities for increased financial access and support for entrepreneurs, but they also pose a number of major challenges regarding policy development, regulatory oversight, and protecting the privacy of individuals' data. This chapter will discuss how the rapidly changing regulatory environment related to FinTech-based, startup funding affects startup scalability and investor trust due to regulatory uncertainty, compliance costs, and inconsistencies in the law when dealing with cross-border transactions. In addition, the chapter will provide an analysis of the data privacy issues associated with extensive data collection, algorithmically driven decision-making, and behavioural profiling of both entrepreneurs and investors.

Cybersecurity is one of the most pressing concerns with regard to autonomous systems' ever-increasing adoption across multiple sectors, including transportation, health care, and smart city developments. The aim of this chapter is to focus on the various methods of securing autonomous systems through artificial intelligence (AI)-powered intrusion detection systems (IDS) and privacy-preserving mechanisms. For example, this chapter will explore the use of machine learning for anomaly detection as well as secure federated learning and blockchain technology to enhance the integrity of data in autonomous systems. Furthermore, it will provide an overview of the use of adversarially attacking AI models and provide recommendations for reducing cyber risk. Utilizing AI-based security frameworks, autonomous systems can identify threats and respond to them almost instantly, while ensuring user privacy. Finally, this chapter addresses the regulatory hurdles surrounding autonomous technology, as well as potential areas for future research related to security in autonomous systems.

Recent leaps in healthcare technology have given rise to Virtual Health Assistants (VHAs) and chatbots, reshaping the way patients interact with clinics and hospitals. These VHAs, built on natural language processing and conversational AI, now handle routine tasks such as filing paperwork, sorting symptoms, sending pill reminders, and offering light mental-health tips. The chapter reviews the design sets-classic rule-based tools, retrieval systems, and full-generative models-that let these assistants speak in near-human language. It also considers how tightly they connect to electronic health records and examines worries about tech integration, patient privacy, and rules like HIPAA. Real-world rollouts are described, together with measures of their impact on patient comfort, access to care, and the daily load carried by doctors and nurses.

Autonomous systems' growing use across various industries, including transportation, healthcare, and smart city development, has created a serious concern about cyber security. This chapter will provide a brief overview of some of the ways to secure autonomous systems through AI-based intrusion detection systems (IDS) and privacy-preserving mechanisms. In particular, it will identify the use of machine learning to detect anomalies and secure federated learning and blockchain technology to provide integrity of data in autonomous systems. It also provides an overview of how adversarially attacking AI models can be used to reduce cyber risk. Once security frameworks using AI are established, autonomous systems will be able to detect threats and quickly respond, while also preserving user privacy. Finally, this chapter will address regulatory issues regarding autonomous technology and potential future research issues related to the security of autonomous systems.

The hospitality industry is experiencing a transformation with the integration of AI and robotic technology. One key element of this change will be AI-enabled concierge and guest services that are changing the way in which customers are engaged in conversation, serviced and supported in the overall guest experience. This chapter highlights the evolving role of robot concierges, especially the ability of a robot concierge's ability to delivering personalized recommendations, engaging in conversation in multiple languages, providing real time responses and integrated hotel management systems. This chapter reviews current applications, case studies from leading hotel organizations, and the technology of robot concierges to discuss the potential and challenges of AI concierge systems. The chapter discusses ethics around awareness of customers, data consent, privacy, and ethics, as well as how future intelligent concierge robots can provide satisfaction, operational efficiency, and some competitive advantage for hospitality businesses.

The rapid changes in the cyber threat landscape in the digital age have changed the design of security mechanisms to support proactive and adaptive security capabilities that can detect, predict, and respond to attacks in real time. This chapter discusses the potential of integrating Artificial Intelligence (AI), Machine Learning (ML), and Predictive Intelligence for cybersecurity through proactive threat detection and prevention. The chapter covers how AI-based predictive models can analyze vast and complex (and rich) data and identify hidden/novel attack patters, detect anomalies for faster incident response, and features supervised, unsupervised, and deep learning models for intrusion detection, malware analysis, and phishing prevention as well as predictive intelligence to help prevent zero-day attacks and other new avenues of security threats. Examples of real-world applications and case studies show how AI-powered solutions can dramatically cut down false-positives, leap-frog situational awareness, and increase resiliency and preparedness for an organization.

The urgency of climate change has placed financial markets in a position to lead the sustainability transition. Green financial products—such as green bonds, Environmental, Social, and Governance (ESG) frameworks, and generally, sustainable finance—are becoming one of the most important approaches to leveraging finance for low-carbon resilience. This chapter argues that these finance products are climate risk management tools, as they link finance flows to climate outcomes, incentivize responsible investment, and transparently disclose market actions and outcomes. The chapter examines asymmetrical growth and future potential in financing renewable energy and climate-resilient infrastructure via green bonds and how ESG practices are applied in capital decisions and corporate governance. Furthermore, the chapter argues that sustainable finance can reduce exposure to climate risks while creating opportunities for innovation, resilient business practices, and value-generating results in global markets.

The rapid wave of growth in information systems has led to millions of new attack surfaces have been created by these devices due to the rapid development of information technologies but have left many systems vulnerable to sophisticated forms of cyber-attacks as well. Modern warfare finds it increasingly harder to qualify in today's presumably automatized world, which further implies that automated systems need to be developed. The aim of such technology is to assist a user in considering and assessing huge datasets containing indicators of suspicious behaviour and even taking actions against emerging threats. This chapter outlines the use of cutting-edge technologies in the Host-Based Intrusion Detection Systems, including ML algorithms, deep learning for networks and natural language processing.

This chapter explores the transformative role of adaptive AI systems in financial fraud detection and risk management. Leveraging machine learning and deep learning techniques, these systems dynamically analyze vast amounts of financial data to identify fraudulent activities and assess risks in real time. Unlike static rule-based methods, adaptive AI continuously evolves by learning from new data and adapting to emerging fraud tactics, thereby enhancing detection accuracy and reducing false positives. The chapter highlights key algorithms, such as neural networks and anomaly detection models that underpin these systems, along with their applications in credit risk, transaction monitoring, and compliance. It also addresses challenges such as data privacy and algorithmic bias, offering insights into the future of AI-driven financial risk management.