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Purpose: This chapter aims to unveil the challenges of adopting and using banking chatbots in India and identify the challenges of Chat Generative Pre-trained Transformer (ChatGPT) for future banking.

Need for the study: Unveiling the challenges of chatbots and ChatGPT in the banking industry in India is crucial to understand the limitations and areas of improvement to enhance customer experience, ensure data security, and maintain regulatory compliance.

Methodology: The researchers conducted a narrative review systematically summarising and analysing existing literature on chatbots and ChatGPT, providing a comprehensive overview of the challenges faced in the industry.

Findings: The authors identify perceived risk, platform quality, connectivity and infrastructure, data privacy and security, user education and acceptance, existing legacy systems, and regulatory guidelines as the challenges of adopting chatbots. Additionally, the findings reveal that the challenges posed by ChatGPT in future banking include the potential reduction of traditional banking jobs, linguistic diversity, data privacy and security, ethical considerations and bias mitigation, explainability and accountability, integration with existing banking systems, and user trust and acceptance. However, implementing these new technologies also presents opportunities for individuals with unique human skills, such as critical thinking, empathy, and creativity, which are difficult to replace with technology.

Practical implications: By minimising the challenges of ChatGPT and chatbots, the banking industry could achieve improved customer service, cost efficiency, automation of routine tasks, and 24/7 availability, leading to enhanced customer satisfaction and operational efficiency in the banking industry. Additionally, these artificial intelligence (AI) tools enable data-driven insights, personalised experiences, scalability, and efficient handling of large customer volumes, contributing to better decision-making and enhanced customer engagement.

This chapter considers aspects of ensuring competitive advantages of the banking sector of Ukraine in the context of global digital transformation. It is proved that operations that previously required a large amount of resources and time can now be carried out automatically using software solutions and artificial intelligence (AI). Companies like FinTech and BigTech that combine financial technology with their core services pose a serious threat to traditional banks. The authors noted that digital technologies make it possible to provide faster and more efficient customer service and improve the security of operations and reduce costs. The emphasis is placed on the fact that the use of AI in the banking sector will contribute to improving the efficiency and accuracy of banks, reducing risks and costs, and also contributes to the selection of personalized approaches to their customers. It has been proven that, thanks to AI, banking companies can improve the efficiency of their work, reduce costs, and improve interaction with their customers, thereby gaining competitive advantages in the market.

The recent period, also called the “era of algorithms”, has brought remarkable transformations both in personal life and in the way of doing business. Developing and implementing digital technologies depend on the availability of human capital, a resource that is increasingly difficult to find. Countries are in competition for this resource, and attracting talent has become a goal often difficult to achieve. In this context, the current chapter seeks to evaluate human capital contribution to the digital competitiveness of the EU countries and to describe the moderating role of digitization in the development of human capital through migration management. Investigating the digital competitiveness of the EU countries, we demonstrated that the most important driver of the major differences between these countries is human capital. In this context, migration becomes both an opportunity and a threat, and its digital management is of tremendous importance.

This chapter focusses on how qualitative research can capture the lived experiences of entrepreneurial individuals by exploring their subjective experiences. Traditional methods of data collection involve listening to what entrepreneurial individuals say and observing their actions, although particular attention to their feelings is often absent. To achieve data congruence and to gain a deeper understanding of their lived experiences, it is crucial to also take into consideration how they feel. This chapter will recount a confessional tale recorded during a recent field study in entrepreneurship that will shed light on the pivotal role that a researcher’s sensory processing sensitivity (SPS) trait can play during qualitative data collection in helping researchers becoming mindful of the feelings of entrepreneurial individuals, even when those feelings are not directly expressed by them. The introduction of the researcher’s SPS trait in promoting data congruence during qualitative data collection will be this chapter’s principal contribution.

This chapter proposes a model for discovering suspicious financial operations such as money laundering. To achieve this, the authors reviewed research papers on money laundering and financial institutions’ cases and problems, especially those related to financial transfers. They also collected primary data through face-to-face semi-structured interviews with financial companies’ owners and experts in financial transfers to identify hypotheses that help discover suspicious transfers. The chapter discusses the six big data analysis cycle phases from problem discovery to model deployment to identify suspicious transfers. The chapter uses hypothetical data and models to discuss the results and focuses on exchange companies willing to analyze financial operations. The chapter proposes tools that exchange companies can use to monitor and prevent suspicious transfers including data visualization and machine learning algorithms.

We use design thinking in the context of accounting pedagogy to exploit recent advances in cybernetics in the form of generative artificial intelligence technology. Relying on the intuition that supplementing or augmenting human argumentation (natural intelligence or NI) with parallel AI output can produce better student written assignments, we posit the “augmentation premise,” that is, ((NI + AI) > AI > NI). To test the augmentation premise, we compare student written submissions in an Accounting Information Systems (AIS) course with and without the benefit of parallel generative AI output. We then evaluate how the generative AI output enhances student-crafted revisions to their initial submissions. Using a summative quality improvement index (QII) consisting of quantitative and qualitative assessments, we present preliminary evidence supporting the augmentation premise. The augmentation premise likely extends to other accounting subdisciplines and merits generalization for enriching accounting pedagogy.

Financial technology (fintech) has emerged as a major player in the global financial system, providing a range of services such as payments, digital currencies, money transfers, loans, crowdsourcing, and insurance. Fintech startups in Arab countries have also gained traction due to economic openness and globalization. However, concerns remain about the safety, durability, and security of traditional financial services, especially with the increasing use of artificial intelligence (AI) and digitization. The Central Bank of Bahrain and other regulatory authorities need to balance risk avoidance with the global trend toward innovation in fintech, as well as ensure that these technologies are not used for fraud, money laundering, piracy, or terrorist financing. The Bahraini government and supervisory authorities must strike a balance between preserving the integrity and robustness of the financial and banking sector and developing innovation. This can be achieved by adjusting the rhythm of comparison, strengthening and enhancing the safety of banks, achieving financial stability, and ensuring compliance with laws and legislation. It is important to address gaps in regulatory rules, information security, and the business environment, and launch financial awareness at the community level before embracing the potential of fintech and its unseen future development at the level of cryptocurrencies and others. The current work examines the impact of Fintech on the Future of banking in Bahrain and the opportunities and challenges.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.