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The great recession of 2008–2009 busted the market bubble and highlighted the loopholes in the banking sector related to excessive leverage and inadequate capital. It has led to the increased rigidity of financial regulations, forcing banks to focus more on compliance rather than moving towards innovation. All these factors together led to the emergence of new players in the financial market in the name of financial technology (Fintech) companies. With the help of Fintech, banking operations are now being revolutionised and transformed into techno-friendly systems. They, hence, can promise to act as a game changer for the banking sector as a whole.

This chapter aims to understand different perspectives of Fintech and how it helps the banking sector to improve its operations. This chapter will also offer insight into various types of Fintech instruments used by the banking sector, collaboration between banks and Fintech, and the benefits of its application to the banking sector.

This chapter attempts to lay out a literature review on Fintech. It examines the implications of applying Fintech in the banking sector to revolutionise its traditional banking operations and achieve its pre-established targets. Different techniques banks use to match up with Fintech and adapt it easily in its organisational structure.

This chapter presents a list of challenges linked to the application of financial technology in the banking industry. The chapter will also address the difficulties of using Fintech and ways to deal with them.

In today's advanced economy, there is a broader presence of information revolution, such as artificial intelligence (AI). AI primarily drives modern banking, leading to innovative banking channels, services and solutions disruptions. Thus, this chapter intends to determine AI's place in contemporary banking and stock market trading.

Stock market forecasting is hampered by the inherently noisy environments and significant volatility surrounding market trends. There needs to be more research on the mantle of AI in revolutionising banking and stock market trading. Attempting to bridge this gap, the present research study looks at the function of AI in banking and stock market trading.

The researchers have synthesised the literature pool. They undertook a systematic review and meta-synthesis method by identifying the major themes and a systematic literature review aided in the critical analysis, synthesis and mapping of the body of existing material.

The study's conclusions demonstrated the efficacy of AI, which has played a robust role in banking and finance by reducing risk and operational costs, enabling better customer experience, improving regulatory complaints and fraud detection and improving credit and loan decisions. AI has revolutionised stock market trading by forecasting future prices or trends in financial assets, optimising financial portfolios and analysing news or social media comments on the assets or firms.

AI's debut in banking and finance has brought sea changes in banking and stock market trading. AI in the banking industry and capital market can provide timely and apt information to its customers and customise the products as per their requirements.

Banks play a vital role in the economy. Investigating their competitive environment is crucial to ensuring economic stability and development. The FinTech disruption has risks and opportunities for incumbent banks, and it can be valuable to investigate its effects on banking performance. Therefore, the aim of this study is to assess whether investment in FinTech is associated with better performance of Indian banks during 2012–2018.

To do this, a sample of Indian banks was investigated between 2012 and 2018 using k-means and hierarchical cluster analysis, ANOVA, and pairwise comparison tests.

Results of the analysis strongly suggest that investment in FinTech is associated with better banking performance. Higher FinTech investments, represented by mobile transaction volume, are associated with higher efficiency scores and accounting-based performance. In particular, banks that invest in FinTech and have relatively low non-performing loans have a 7.7% higher Return on Employment (ROE) than banks with exceptionally low FinTech use and no significant investment in smart branches.

Therefore, it can be recommended that Indian banks adopt a forward-looking strategic approach when making investment decisions regarding new technologies. Failing to adapt to the FinTech disruption may result in poor value creation prospects in the long run.

To the best of the authors' knowledge, this is the first study that analyses. We are not aware of any similar study on whether investment in FinTech is associated with better performance of the Indian banks during 2012–2018.

The purpose of this chapter is to cover banking efficiency using the concept of the Meta frontier function and to study group and subgroup differences in the production technology. This study estimates the technical efficiency (TE) and technology gap ratios (TGRs) for banks in Islamic countries. Using the assumption of the convex hull of the Meta frontier production set using the virtual Meta frontier within the nonparametric approach as presented by Battese and Rao (2002), Battese et al. (2004), and O'Donnell et al. (2007, 2008) and after relaxing this assumption, the study investigates if there is a significant difference between these two methods. To overcome the deterministic criterion addressed to nonparametric approach, the bootstrapping technique has been applied. The first part of this chapter covers the analytical framework necessary for the definition of a Meta frontier function and its estimation using nonparametric data envelopment analysis (DEA) in the case where we impose the assumption of the convex production set and follows in the case of relaxation of this assumption. Then we estimated the TE and the TGR in concave and nonconcave Meta frontier cases by applying the Bootstrap-DEA approach. The empirical part will be reserved for highlighting these methods on data bank to study the technical and technological performance level and prove if there is a difference between the two methods. Three groups of banks namely commercial, investment, and Islamic banks in 17 Islamic countries over a period of 16 years between 1996 and 2011 are used.

This study developed a new analytical model to quantify the influence of business intelligence (BI) adoption on bank performance. An in-depth review of academic literature revealed a significant research gap exists in investigating BI's performance impacts, especially in the under-studied Indian banking context. Additionally, customer relationship management (CRM) was incorporated as a moderating variable given banks' large customer databases.

A survey was administered to 413 employees across leading Indian banks to collect empirical data for evaluating the conceptual model. Relationships between variables were analysed using partial least squares structural equation modelling (PLS-SEM). This technique is well-suited for theory building with smaller sample sizes and non-normal data.

Statistical analysis supported the hypothesised positive effect of BI adoption on bank performance dimensions including growth, internal processes, customer satisfaction, and finances. Furthermore, while CRM did not significantly moderate this relationship, its inclusion represents an incremental contribution to the limited academic literature on BI in Indian banking.

The model provides a quantitative basis for strategies leveraging BI's performance benefits across the variables studied. Moreover, the literature review revealed an important knowledge gap and established a testable framework advancing BI theory in the Indian banking context. Significant future research potential exists through model replication, expansion, and empirical verification.

This research thoroughly reviewed existing academic literature to develop a novel testable model absent in prior studies. It provides a robust conceptual foundation and rationale for ongoing scholarly investigation of BI's deployment and organisational impacts.

In the last 10 years, the global financial services industry has significantly benefited from fintech. As the Indian entrepreneurial ecosystem continues to change, more fintech-use case-driven firms are created, and more investors are supporting these enterprises. India is acknowledged as a powerful fintech centre internationally.

The goal of the current research is to comprehend the revolutionary landscape of the Indian financial system.

Methodology: The research methodology entails a thorough review of several research papers and government reports better to understand fintech's role in the Indian financial system. This requires examining the trends, regulations and technical breakthroughs driving the fintech ecosystem to present a comprehensive picture of its influence.

The present chapter indicates that the fintech industry is flourishing in India. Over the following years, technological improvements will fuel the market's continuous expansion and change how financial products and services are produced, distributed and used.

The present study apotheosises on the relationship between blockchain and fintech and its impact on the financial services sector. It then gauges into the various aspects that have been included in the published literature by the authors all across the world.

Post-pandemic, technology has led to tremendous opportunities in the financial sector, and the customers have started assessing financial services in online mode, thus enabling companies to innovate their business strategies. The present chapter aims to explore the areas where blockchain is benefitting the financial sector.

The objectives of this study were achieved through systematic review of literature performed with the help of bibliographic analysis. Further, a PRISMA model was developed, and the networks were derived with the help of Scopus analyser software and VOSviewer Version 1.6.15.

It is observed that since the introduction of the term and development of the first blockchain, a lot of work has been performed in the said domain, but it is still at a nascent stage. It is thus discovered that the string specific research documents got published in Scopus database from 2016 onwards, and a considerable amount is being performed by authors from China, the United States and India.

The present chapter exhibits the various dimensions in which a lot has to be yet explored and needs to devise innovative and full proof strategies which will enable to overcome the challenges, thus strengthening the position and working of the companies in Industry 5.0.