The Impact of Artificial Intelligence on Governance, Economics and Finance, Volume I

FinTech is an innovation that integrates technology into financial services. FinTech contributes to the economy by increasing financial access and new products and services, therefore it has an important role in shaping the future of the financial system. FinTech market has been rapidly growing worldwide, and awareness of financial market users of FinTech has been increasing. This rapid growth in FinTech market makes evaluating status of FinTechs significant. This paper aims to evaluate the economic significance, benefits, and solutions of FinTech, status of FinTech markets worldwide and in Europe, and impact of FinTech on banking market structure. This paper evaluates the FinTech ecosystem in Turkey and effects of FinTech on financial stability and financial markets with a SWOT analysis and makes proposals for Turkey.

Technological development is changing society, economics, banks, and banking. The change in technology at first directed banking transactions from branches, which were conventional distribution channels, towards toward automated teller machines (ATM), telephone, internet Internet banking, and mobile devices later on and diversified distribution channels. This change did not cease and cloud-based applications, big data, and the concept of big data reading comprehension gained importance in a course of time. In addition to that, Cryptocurrencies have taken to the stage of everyday life. Development in the digital communication enabled the communication with people all over the world. Digital banking had taken the shape of a distribution channel in at the beginning, which had provided ease of access and cost advantage along with productivity growth by enabling banking services to be rendered without the branch, i.e., staff. This situation increased the profitability of the banks by having the banking system getting ahead in the competition. Social media has become one of the innovations that technology brought forth. Blogs and channels such as Youtube YouTube have been a part of life. Banks do not only have to exist on social media for building a new service architecture and retaining customer mass but also they need to fulfill their targets of marketing and public relations. At the same time, social networks affect the banking. Social networks such as Twitter, Facebook, and Linkedin have intensified interactive communication on the internet Internet which led to the emergence of the new business models stemming from social media. Another change brought about by digitalization and technological developments was innovative, flexible, and adaptable financial solutions supplied by the ‘Fintech’ enterprises which seemed probable to transform banks and banking too. On the one hand, Fintech enterprises pose a threat to the sector, but on the other hand, the possibility that competitive advantage would be taken by creating new business platforms in cooperation with Fintech enterprises has been realized. These technological developments affected regulations and led to official regulations about open banking. Open banking allowed for permitted sharing of data via Application Programming Interfaces (API) and enabled Fintech enterprises to develop financial services. This change turns banks into platforms and enables the foundation of structures, which would give access to more competitive financial products and services with higher quality for banks in cooperation with Fintech enterprises. Banks may have to modify their products, business processes of services, and their organizational structures and architecture probably, to keep pace with the digital change. Banks are progressing as becoming institutions transforming data into information and marketing information, not money, anymore. The digital change is directing our bank bank-centered viewpoint towards toward a customer-oriented viewpoint in an ever ever-increasing pattern. Along with the fact that banks accommodated themselves to mobile technology, the major essential development is the establishment of digital banks rendering direct digital financial services in addition to the fact that banks are institutions rendering service only by use of the digital channels and regarding the digital platform as a service channel solely.

A new data world which never get deformed, can be reached from anywhere, continuously stream and multiply, emerged with the evolution of technology. The data, in particular, created by business firms, scientific research centers, and automation systems reached great amounts. It has become the main target of many data analysts to reach meaningful, unexplored, and valuable information or deductions among these piles of data. In this chapter, firstly the techniques of artificial intelligence and the skills of these techniques were discussed. Later, the mostly-used techniques in the finance sector, the advantages and weaknesses of these techniques, and the methods which can be used to process the data created by the finance sector, which creates big data and is one of the leading sources, was comparatively shown. The current version of the mostly-used artificial intelligence methods in the finance sector was scanned and the new skills and contributions it provides to the sector were examined. What Classification, clustering, association rules, and time series analysis methods, in particular, cover and what problems they can produce solutions to were examined and the readers were informed about these techniques. It was aimed to give information about forming credit score and customer segmentation, where classification and clustering methods are especially employed, with sample studies. It was aimed to present the principles the up-to-date methods are based on and their theoretical and practical applications in a meaningful way. In addition to these, information about practical and useful software that can be used for data analysis in the finance sector was given and the skills of this software were conveyed to the readers. Finally, how the techniques of processing big data can be used was examined through samples as the finance data are classified as big data. The difficulties met during the analysis of big data, a natural result created by this sector, and solutions to them were presented. Updated big data processing solutions like Hadoop, Spark, MapReduce, Distributed computing, and GPU (Graphics Processing Unit) computing, in particular, were comparatively explained. The main principles that big data processing techniques are based on were simplified in a way that the readers could understand and were supported by examples from the sector. Especially, Spark, Hadoop, and MapReduce methods, which are leading methods in processing big data, were examined. Finally, the contributions made to the sector by artificial intelligence and big data processing techniques were generally summarized and the results were presented.

The most important feature of the last twenty years in the world economy is the digitalization of the social and economic field. This new trend is a process that can not be analyzed by unconventional methods, approaches, and techniques. This process is a dynamic mechanism that involves rapidly spreading effects. Therefore, digitalization has revealed an economic and social situation in which institutions are constantly transformed, innovations are applied very quickly, and are in demand. The most important problem in the studies to be carried out on this subject is the discussions about the measurement of digitalization and whether its numerical indicators are representative of the process or not. The extent of digitalization in the economy is Fintech applications in industry 4.0 money markets and financial markets in real terms. In today’s business world, the size of the relationship between production and the market changes in the digital economy. Achieving the accumulation of knowledge in the economy at lower costs with the effect of digitalization has led to the production of an important digital information. This accumulation of knowledge led to changes in economic behavior and preferences in business models. The economic area where the effect of this change is seen most rapidly is the financial area. Digitalization in the financial area is emerging as a new source of risk. In this respect, the increase in the volume of financial data with digitalization made the necessity of new analysis techniques necessary. Data sets resulting from the increase in the volume of data are defined as big data. In general, these big data have high frequency and real time or instant data feature in the financial system. The analysis of these data is a basic tool for measuring financial risks with systemic financial risks and the risk level of the markets. Digital economy is defined as a new economic structure as a result of changing the structure of the internet and communication systems. In this new structure, economic relations are created within the framework of the relationships established between the platforms. Establishing relationships between people, firms, and institutions through platforms reveal a lot of digitizable data. The continuous accumulation of this data online makes it necessary to carry out continuous analyzes according to each piece of information that is constantly received. The analysis of the information as well as the information turns into a product of economic value. The most important tool for this new transformation is artificial intelligence. Artificial intelligence and deep learning methods with machine learning, which are its tools, also cause changes in the financial and monetary relations of the new economy. The first major impact of this change was on the banking system. The changes in the banking system and the digital currencies and the developments that emerged with Facebook’s announcement on the issue of the Libra currency cause changes in the primary functions of the central banks and in the monetary transfer mechanism. The main reason for the change in the primary function of the Central Bank and the change in the monetary transmission mechanism is the differentiation in the property of the money. The differentiation in the feature and function of the central bank has to redefine its functions along with the monetary definitions of the central banks. Within the framework of this trend, the aim of this study is to analyze the change in the structure of central banks, the characteristics of money, and the functions of monetary policies, with the artificial intelligence and digitalization process.

The aim of this study is to analyze the effects of digitalization and artificial intelligence applications on financial performance of banks which emerged as a result of developments in information and communication technologies (ICT). In this paper, the role of financial system in the modern economy, as well as the impact of technological change and financial innovation on the structure of the financial sector are tried to be explained. In this context, the literature and its findings are evaluated to explain the impact of financial innovations classified as new production processes and new products or services on the performance of financial sector. Finally, important examples of how fintech transform the structure of the banking sector are provided. In this analysis, technical efficiency scores of banks representing the performance of the financial sector were used as the main determinant variable. The data set of 26 commercial banks active in the Turkish banking sector between the years of 2010 and 2016 constitute the sample size of the study. While technical efficiency scores of each bank are obtained by Data Envelopment Analysis (DEA), the effect of digitalization on financial performance is estimated by using “truncated regression model combined with bootstrap confidence intervals.” According to the results of the truncated regression model, digitalization has a positive effect on financial performance.

New trade patterns emerge with the changing organization and geography of supply chains under Industry 4.0. An eventual shift from Global Value Chains—GVCs is an actual concern for policy makers. This study designates the technical and managerial innovations that are shaping the new supply chain (Supply Chain 4.0) and identifies the forces of geographic agglomeration and dispersion. We feature two leading characteristics of Supply Chain 4.0: a huge amount of data flows and a customer-oriented production. Both of these two imply that the location choices are driven by data-related costs. Producer chooses between to locate closer to “data tower” to profit the scale economies in data analytics or instead, approach to customer. Finally, the form of the conventional bell-curve of Puga (1999) resulting from the trade-off between scale economies of production and transport costs change the form by putting from now on the Win-Win trade between North and South on a knife-edge equilibrium conditional on data frictions. Our research emphasizes that new trade policy takes the form of “data policy” and a joint and mutually benefiting international policy approach is essential for a sustainable trade. In this research, we basically made use of the literature to drive theoretical insights for future work on the geography of Supply Chain 4.0. However, since the limits of Industry 4.0 are not yet clear-cut, we used resources of very different nature (academic, reports, case studies, etc.) and from different disciplines (engineering, managerial sciences, economics).

The global system continues to process by keeping its essence through some protective and nationalist attempts. It is actually significant that the global system involves a multidimensional and technology-driven structure and that national-states’ regulations occur along with transnational practices. In this system, new technologies have a big role. New technologies consist of some innovations such as artificial intelligence, robotics, big data, internet of things, cloud technology, and so on, that describe the concept of ‘Society 5.0’ proposed by Japan and the concept of ‘Industry 4.0’ proposed by Germany. These technologies create super smart society in terms of their impacts on improving human life. Smart technologies improve human life in the fields of education, health, security, etc. On the other hand, smart technologies used in the process of production shape the global production network and the innovative strategies of firms. It is required that new technologies should not be remained uncontrolled within the logic of ‘laissez faire’ because their impacts and power are so great. Thus, national-states should make efficient economic policies by also considering transnational practices; like that Japan and Germany develop their own national view in terms of Society 5.0 and Industry 4.0. Such fields come into prominence in the economic policies in respect of new technologies: Competition policy on the basis of global competition and innovativeness—Industrialization policy on the basis of emerging industries of new technologies—Intellectual property policy on the basis of the rights of people and firms producing knowledge and innovation—Employment policy on the basis of new occupations and specialities in the global age—Social policy on the basis of new technologies’ control on individual and social life.

Artificial intelligence is progressing at an increasing speed. Science fiction often depicts artificial intelligence as a robot with human characteristics. Today’s artificial intelligence, with examples of face recognition, internet search, or self-driving cars without a driver, is called weak AI, which is designed to take on a technically limited task. However, many researchers aim to create strong artificial intelligence in the long run. For example, a weak artificial intelligence can defeat people in a particular task of playing chess or solving equations, while a strong AI can overshadow people in almost any cognitive task (Future of Life Institute 2016). I think the question is: Who is the production for? For people or for machines? Of course, for people. Perhaps the correct question is: Why are people part of production? In this study, I will discuss whether it is needed rather than the risks or benefits of artificial intelligence. The basic criterion of the capitalist system is to make a profit. In order to make a profit, the product produced must be sold/purchased at a certain price. For this, people must participate in the production process. If there will be no need for human beings in the production process (if the machines will replace man in the production process at increasing speed and there will be no limits here), how will labor be part of the distribution process? The question to be asked is: for whom will the production be produced, since the labor force who does not participate in the production process cannot participate in the distribution process? Contrary to what has been stated in almost all literature, I argue that the proliferation of artificial intelligence in the long run will adversely affect economic growth. These practices aiming at economic growth will destroy economic growth. This study raises different claims from the main literature and focuses on analyzing these claims.

Considering the increasing importance of productivity in increasing the economic growth of developing countries and also the transition to Industry 4.0, the fourth generation of the Industrial Revolution, optimizing the use of production resources and increasing the productivity of industries based on science and knowledge are among the most important goals of countries. This research has been conducted with the aim of providing a model for promoting industrial productivity with an emphasis on the role of intellectual capital in the leather and footwear industry of East Azerbaijan province of Iran in 2017. The present study is descriptive in terms of its implementation method with field approach, and is considered as an applied research in terms of its purpose. Data were collected using Bontis’ (1998) Intellectual Capital Standard Questionnaire and Moghimi’s (2009) Industry Productivity Questionnaire. The results of the test of research hypotheses obtained using the structural equation model showed that intellectual capital has a positive and significant effect on promoting organizational productivity in leather and footwear industries in East Azerbaijan province. Also, the test of secondary hypotheses shows that human, structural, and relational capital have a positive and significant effect on the promotion of organizational productivity in the leather and footwear industries of East Azerbaijan province. Therefore, any steps that can be taken to improve the quality and quantity of these variables can be effective in promoting the productivity of this industry.

Developments in information and communication technologies lead to radical changes in traditional business models. This transformation process is rapidly changing the principles underpinning existing systems and governance models and makes the traditional role of centralized institutions questionable. Perhaps the newest and most important example of these changes is the “Blockchain” technology. Blockchain claims to provide a deep-rooted solution to the problem of “trust” that exists in traditional commercial relations. Blockchain technology is a technology that does not require a central structure and allows the storage and transmission of commercial or value-containing data (money, identity, valuable papers, etc.) safely and quickly. This contributes to reduced costs, increased efficiency, reduced errors as a result of continuous storage of records in the chain, and the reliability of records kept. Blockchain technology enables it to be implemented in many sectors such as finance, manufacturing, logistics, energy, health care, retail, telecommunications, media, insurance, as well as in public transactions thanks to its technological infrastructure and smart contracts. Due to the cost-cutting effect of blockchain technologies, the use of this technology is of great importance for the health sector and interest in this field is increasing. Blockchain’s applications in the medical field cover a wide range of processes, including electronic health records, health insurance, biomedical research, drug supply, purchasing processes, and medical education. Blockchain networks have many promising uses in the healthcare sector, from increasing transparency in the drug supply chain to creating and sharing unchangeable medical records. In the health sector, blockchain technologies can be used at different stages, from drug and medical product development processes to diagnosis, from the e-prescription process to better preservation and use of patient records.

The national innovation system is defined as a network of public and private sectors that initiate, import, modify, and distribute new technologies, activities, and interactions. These interactions constitute the structure of the system and are shaped by culture, norms, institutional arrangements, and public policies. They are the primary innovative actors in the innovation process as companies are eager to seek, accumulate, and retain innovative talents. However, firms’ ability to innovate is influenced by interactions with a wide range of external organizations, government and private actors, suppliers, customers, and markets. As the state is responsible for providing infrastructure and creating an appropriate institutional platform for exchange and dissemination of information, it has an important role to stimulate the capabilities of firms. The national innovation system emerges in different fields including social sciences or engineering, and is used by policy makers. The components of innovation vary from country to country, and these differences can be explained by systemic characteristics. These differences in economic and institutional structure are reflected in the ratstructure are reflected in

tion. According to the national innovation system, the innovation ability of a firm depends on the factors including the quality of the national education system, the industrial relations, the quality of technical and scientific organizations, government policies, cultural traditions, and the interactions between them. In this paper, the most important variable affecting a national innovation system which is the firm variable will be discussed. Additionally, theoretical foundations on the interaction between firms, research and development (R&D), and innovation will be provided.

The chapter explores the challenges and opportunities of Blockchain-based Financial Technology applications from the Indian perspective. Blockchain has a recent hype worldwide and India responds to it making itself open to both the challenges and opportunities. India has niche societal characteristics, which makes its Blockchain confrontation unique than the other countries exposed to this technology. This chapter contributes to the understanding of these niche characteristics to identify the unique challenges and opportunities of implementing the Blockchain technology from the Indian perspective.

Artificial intelligence (AI) is a swiftly evolving phenomenon that bears both economic and organizational significance. As organizations are increasingly benefiting from AI for both routine and highly complex tasks and decision-making, AI has developed as a key concern when contemplating the future of organizations and organizing. The ability of the AI to act autonomously distinguishes it from technologies historically used in organizations. This also entails new forms of organizing with a non-human actor and challenges existing conceptualizations of technology in organization studies. As AI is contributing to the automation of many aspects of management and impacting organizational dynamics, it has emerged as a very significant organizational phenomenon that entails both theoretical challenges and opportunities for management and organization studies scholars. Although the implications of AI for organizing has been at the centre of practitioner-oriented journals, the scholarly work has remained more nascent with regard to theory-driven research that could explicate the mechanisms between empirical cases and theoretical perspectives. This chapter aims to reveal the state of scientific knowledge on the relevance of AI in organization studies and delves into the potential implications of AI for management scholarship. The chapter first presents the historical trajectory of AI in organization studies by discussing both important antecedents for and consequences of adopting AI-based systems in organizations. It then systematically examines the extant research on the impact of AI on organizations published in the top management journals of the last two decades. The articles are delineated between theory-building and theory testing and further classified with respect to aspects of AI (such as AI as task input, task process or task output) and themes raised in them. The systematic review of these articles contributes to both identifying knowledge gaps and growing research agenda by introducing possible research questions with regard to future research directions for AI in organization studies. This review chapter ends with a brief discussion on the implications for organizational theorizing and the future of organization studies in light of AI.

The rapid development of artificial intelligence in recent years has led to an increase in artificial intelligence-based applications in many areas. One of the important application areas of artificial intelligence has been the field of Financial Technology (Fintech) and artificial intelligence has been widely integrated into financial services. Artificial intelligence-based Fintech applications such as workflow automation, fake and fraud detection, algorithm-based asset management (robo advisors), and intelligent consultant provide significant benefits to the finance industry. Fintech applications, which means using technology to improve financial services, may cause financial risks, despite its many benefits. Especially after the 2008 Global Crisis, it is observed that there are significant deficiencies in the regulation and supervision of financial markets. In this context, regulatory technologies (Regtech) are needed in order to eliminate deficiencies and minimize financial risks. In other words, developments in Regtech make secure the improvement of Fintech. The main purpose of Regtech is to find technological solutions that help regulate Fintech without harming their positive potential. Therefore, Regtech allows both an effective financial risk management and provides significant cost saving. In order to fintech and supervision authorities to get maximum efficiency, it is very important that the application processes of Regtech are standardized and technology-oriented. The purpose of this study is to provide an overview of how artificial intelligence will transform the financial system. It is also to discuss how financial technologies (Fintech) and regulatory technologies (Regtech) will be affected by this transformation.

With the dramatic development of information technology, the Covid-19 pandemic period has created the opportunity to access a new phase in terms of the age that is becoming more and more digitized every day. Covid-19 pandemic has emerged as a driving force in changing certain things in the global world order. In order to minimize the effects of the current crisis, countries have entered into a digital transformation process in many different areas, from the education sector to the health sector, judicial practices to monitoring social distance rules. Artificial intelligence (AI), which already existed in our lives, started to be used in certain application areas, especially in this period. Pandemic period once again demonstrated that artificial intelligence and digital technologies have become a big part of our lives. The global world has witnessed a digital transformation with the applications developed within the scope of the Covid-19 fight. However, while using AI-based apps provides many benefits to manage the pandemic process, it also brings with several ethical and legal concerns regarding human rights and fundamental freedoms. Ethics provides several frameworks in the name of the promotion of human values and dignity. Among human rights and ethics, there is a clear and strong connection. The rights and freedom demands that arise based on human rights are ethically based. The legitimacy of human rights-based demands is originally based on ethics. For that reason, using artificial intelligence is directly linked to “human safety, health and safety, liberty, confidentiality, integrity, dignity, autonomy, and non-discrimination,” and these also include ethical concerns. Therefore, artificial intelligence practices related to human rights and freedoms that cause some human rights violations during the pandemic period also reveal an ethical violation. Another risk factor is the use of “surveillance technology” in the “new normal” lifestyle, which was effectively used by national governments during the pandemic period. In this extraordinary period, there is concern that artificial intelligence-based practices developed to protect public health will be permanently used as usual in the post-pandemic period. AI has tremendous potency to advance the lives of many people and procure human rights for all. It is necessary to evaluate these potentials and minimize the risks associated with artificial intelligence. Likewise, artificial intelligence also poses deep risks to security, democracy, and human dignity.

The form of competition varies in the business world. Now, a competition starts over the value offered to the customer instead of just a competition on quality and/or price. The prerequisite for realizing these goals is to update the “business model” of companies and make them compatible with AI applications.