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While a voluminous literature exists on the measurement of financial institution efficiency, little work has been directed at investigating the properties of data envelopment analysis (DEA) scores by examining the relationships between these scores and traditional measures of bank performance. Following the seminal work of Barr, Killgo, Siems and Zimmel (1999), this paper employs data on Singapore financial institutions for the period 1993 to 1999 to develop efficiency scores for Singapore banks. It then examines the manner in which derived DEA efficiency scores interact with traditional measures of profitability, size, risk and soundness.

The purpose of this paper is to analyze the effects of organizational formalization on the behavioral, market, product, and process types of firm innovativeness as well as the interplay between these different innovativeness types. Based on data collected through a survey of the financial services industry in Turkey, the analyses show that formalization directly hinders both behavioral and market innovativeness. Moreover, as behavioral innovativeness influences product and process innovativeness, formalization's effect on these types of innovativeness are indirect. As expected, the study also finds that process innovativeness facilitates both product and market innovativeness and that product innovativeness foster market innovativeness. This study makes a contribution to the literature by examining the linkages between formalization in firms and the various firm innovativeness types, which have previously been studied only separately. The study thus provides a richer understanding of the relationship between formalization and firm innovativeness types.

Blockchain is undoubtedly considered one of the most innovative technologies in financial services from the past decade. Interests in blockchain technology continue to grow on a daily basis, while many promising blockchain-enabled applications and services continue to draw financial interest in the industrial sector and the broader financial services communities. Blockchain technology has the potential to streamline lending services and banking, decrease counterparty risk, and reduce settlement times and issuance. It indeed enables authenticated documentation and anti-money laundering (AML)/Know Your Client (KYC) data, minimizing operational ventures and allowing real-time/online validation of financial documents. Furthermore, as blockchain matures, it increases the value of a diversified range of industries and institutes with a better return on investment. However, such organizations must continue to adopt rigorous approaches to enhance their financial services and regulate their policies. In this article, we discuss the current status of blockchain-enabled financial services and applications, in addition to the impacts, implications, and regulations of incorporating blockchain technology in the finance industry. We further tackle the adaptation model for blockchain technology in global banking and provide a vision for the next generation of financial services based on this emerging technology.

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. Artificial intelligence (AI) and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.

This paper presents a comprehensive bibliometric analysis aimed at mapping the convergent landscapes of artificial intelligence (AI) and financial technology (Fintech), fields poised for significant disruptive potential in global financial services. Given the rapid integration of AI within financial operations, this research investigates the publication trends, key contributing nations, and prevalent themes within this intersection, crucial for understanding the trajectory of Fintech innovations and their alignment with AI advancements. Employing a robust methodology utilizing VOSviewer and the Scopus database, the analysis distilled insights from 298 selected papers, focusing on co-authorship, bibliographic coupling, and keyword occurrences to highlight the global influential works and primary research hubs. Key findings reveal that AI and Fintech are not only predominant themes but are also the nucleus of emerging scholarly discussions, with the United States, China, and India leading in contributions. These nations, alongside others like France and the United Kingdom, form critical nodes in our analysis, indicating a robust interconnection of global research efforts. This study introduces the novel application of advanced bibliometric techniques to dissect dense academic outputs, offering a granular view of how AI influences financial technologies. The implications of this research are manifold; it provides a strategic blueprint for academics, industry practitioners, and policymakers to understand the focal areas of AI in Fintech, suggesting an amplified focus on collaborative innovations and policy-making that aligns with technological advancements. Future research should expand the analysis to include diverse databases and explore the integration of AI across various financial sectors, emphasizing the socioeconomic impacts, ethical considerations, and regulatory challenges posed by AI-driven financial services. This extended focus will enhance our understanding of AI’s role in shaping the future of Finance, ensuring comprehensive coverage of this dynamically evolving field.

On June 23, 2016, the UK decided to leave the European Union (EU), commonly known as “Brexit”. The UK has two years to conclude their new arrangement with the EU27 after evoking Article 50 Treaty of Lisbon officially, which it did on March 27, 2017. While there is a range of possible trade agreements most are unlikely as they would either imply repudiating firm EU legal principles or strong promises that the current UK government is committed to maintain. The article discusses these options. Moreover, the article focuses on the trade and investment flows between the UK and EU27 and discusses the possible short-term implications of Brexit with a specific attention to the most impacted sector, that of financial services.

In today’s financial services landscape, staying ahead of the innovation curve and being disciplined at enhancing core service offerings entail careful resource planning. A well-structured outsourcing arrangement can go a long way towards enhancing long term organizational strategic growth. In the post-2014 FinTech era, (i) strategic management with an innovation focus; and (ii) financial technology-associated risks, have brought about changes to outsourcing in the financial services industry. Presently, most outsourcing life cycle models in existing literature seek to provide comprehensive, yet industry-neutral guidelines lacking industry context and depth of coverage. A newly licensed financial institution deciding to embark on outsourcing but is uncertain about how to thread the increasingly complex FinTech and financial regulatory landscape, will likely find domain-specific outsourcing life cycle models useful. A more targeted financial services outsourcing life cycle approach, with a focus on strategy and risk management in today’s context, can contribute more effectively to the application and review of outsourcing implementation. This chapter discusses a new Strategy-Risk outsourcing life cycle model. This is an elegant and simple-to-use end-to-end framework which can be utilized by the financial services industry to guide outsourcing decisions. The chapter also recommends areas for future research.

Estonia has become one of the best known digital societies through its advanced FinTech industry. It is one of the top-ranking nations for internet accessibility, with 91% of its population using the internet. Estonians’ lives are influenced by information technology through the range of various digital solutions available for both individuals and businesses. This chapter elaborates on the story of Estonia’s Fintech evolution from the post-Soviet era to modern times with governmental cooperation and considers possible future development that will allow Estonia to continue its FinTech evolution. It also describes the foundation of technological development for its existing financial services offerings, and it forecasts future trends. The research is built on conceptual theoretical findings, secondary data on FinTech development in the Baltic countries and empirical insights from interviews conducted in 2021 with three Estonian FinTech experts. From these interviews and exploration of the historical evolution of FinTech in the country, it is shown that Estonia’s success comes from the country’s past, as trust in the government was still a large part of the people’s culture at the time. Estonia has succeeded in its transformation due to the support of the government towards information technology. This can also be seen in the current evolutionary turn point whereby the push for the finalization of a regulatory framework for the FinTech ecosystem can allow further transparency. There are, however, limitations with any advancement towards the future.

In recent years, the use of Sentiment Analysis (SA) has proliferated across a wide variety of fields, including financial markets and services. SA can help predict market trends, detect changes in consumer behavior, and identify potential investment opportunities. In this chapter, we explore the impact and applications of SA in financial markets and services and review both traditional and state-of-the-art algorithms for sentiment analysis. We demonstrate how SA approaches can help make better decisions and predictions in financial markets, stock exchange, trading, and cryptocurrencies. We provide examples of popular algorithms used in financial SA and discuss their pros and cons. In addition, we mention the main metrics for evaluating SA performance. By the end of this chapter, readers will have a deeper understanding of how sentiment analysis can contribute to financial markets and services, and the tools and techniques used to achieve accurate and reliable results.

FinTech, the rapidly evolving new breed of technology-driven financial offerings, generates a renewed momentum by bringing together disconnected trends challenging incumbent financial services firms. In response to the increasing FinTech-enabled forces of technology innovation, process disruption, and services transformation, along with the fact that FinTech is gradually becoming an area of academic research, this chapter contributes to the innovation and technology management literature by (1) providing a mapping of current FinTech research and (2) suggesting a FinTech research agenda. A scoping review across six major databases has been conducted, leading to an N = 92 sample chosen and analyzed based on pre-determined selection criteria and according to the developed theoretical framework. The review illuminates observations with regards to the scope, conceptualization, topical themes, and research approaches revolving around extant FinTech scholarship, while providing novel and comprehensive knowledge on the underlying technology aspects, FinTech-enabled business models, and value-creation outcomes.

The emergence of the most recent FinTech phase resulted in multidimensional challenges, putting pressure on the traditional modus operandi within the financial services industry. Whilst incumbents initially responded to this FinTech phase by waiting out or attacking the new market entrants, the environment more recently shifted toward a more collaborative approach built on open innovation. The transition is further encouraged through regulatory initiatives (e.g. open banking), paving way for the development of FinTech-enabled ecosystems. Based on a review of publicly available information, this research is founded on academic and industry literature and aims at illuminating how banking organizations utilize open innovation as a vehicle for strategic intervention in order to respond to the changing market environment. Several cases that correspond to the attributes and characteristics of the three main types of open innovation (inside out, outside in, and coupled open innovation) are investigated, and common determinants contributing to the success of those initiatives derived in order to provide an outlook on future developments within the industry.

Despite the fact that it could help to overcome the current global financial crisis, the concept of open innovation is only very scarcely applied in the financial services sector. This international literature review covering the past decade provides an overview of the relevant body of literature on this topic. Two questions represent the starting point of this work: (1) Why is open innovation so scarcely applied in the banking, wealth management and insurance industries? and (2) Should the financial services sector use open innovab2202 tion more widely? Our findings show that various organisational factors as well as monetary reasons prevent financial services companies from applying open innovation processes. Yet, by taking into account the potential benefits that the concept of open innovation may yield, this approach should indeed be applied more widely in the financial services industry.