The Impact of Artificial Intelligence on Governance, Economics and Finance, Volume 2
- 2022
- Book
- Editor
- Sezer Bozkuş Kahyaoğlu
- Publisher
- Springer Nature Singapore
About this book
This book continues the discussion of the effects of artificial intelligence in terms of economics and finance. In particular, the book focuses on the effects of the change in the structure of financial markets, institutions and central banks, along with digitalization analyzed based on fintech ecosystems. In addition to finance sectors, other sectors, such as health, logistics, and industry 4.0, all of which are undergoing an artificial intelligence induced rapid transformation, are addressed in this book.
Readers will receive an understanding of an integrated approach towards the use of artificial intelligence across various industries and disciplines with a vision to address the strategic issues and priorities in the dynamic business environment in order to facilitate decision-making processes. Economists, board members of central banks, bankers, financial analysts, regulatory authorities, accounting and finance professionals, chief executive officers, chief audit officers and chief financial officers, chief financial officers, as well as business and management academic researchers, will benefit from reading this book.
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Table of Contents
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Frontmatter
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Introduction
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Frontmatter
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Chapter 1. Introduction
Sezer Bozkuş KahyaoğluThe chapter delves into the evolving nature of competition in the business world, driven by the value offered to customers rather than just quality and price. It emphasizes the need for businesses to update their business models to integrate AI applications, which promise significant benefits such as increased productivity, cost savings, and enhanced customer satisfaction. However, the chapter also cautions about the risks and ethical dilemmas associated with AI implementation, such as data privacy concerns and the responsibility for AI-driven decisions. It further discusses the role of AI in various sectors, including finance and healthcare, and the need for regulatory frameworks to manage these transformations effectively. The chapter concludes by highlighting the potential of AI to create more intelligent and efficient ecosystems, while also warning about the need for responsible AI development to ensure the well-being of humanity.AI Generated
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AbstractIn this study, it is planned that the Volume-2 will provide technical information on thematic issues related to artificial intelligence. In particular, the impact of artificial intelligence and advanced technologies on different sectors is presented with examples. In this context, dramatically changing business model approaches are presented. In addition, it is explained how the change in the value creation approaches of enterprises is shaped by artificial intelligence implementations. There are sections that include intelligent systems developed with artificial intelligence, the use of these systems in terms of accounting, finance and fraud prevention, and different applications with specialized topics on AI. Human resources and ethical issues, which are important in the realization of all these, are also mentioned. Thus, while examining the areas where artificial intelligence can provide added value for the future, risk factors are also explained. Accordingly, it is aimed to contribute to the literature by presenting solutions and policy recommendations.
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The Impact of AI on Smart Systems
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Frontmatter
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Chapter 2. An Integrated Model and Application for Smart Building Systems with Artificial Intelligence
Emre Karagöz, Vahap TecimThe chapter discusses the transformation of traditional buildings into smart structures through technology and AI. It highlights the development of an integrated model for smart building systems, focusing on subsystems such as intelligent evacuation, guest guidance, media management, and personnel tracking. The model leverages advanced technologies like Analytical Hierarchical Process (AHP), Computer Vision, and Augmented Reality to enhance safety, efficiency, and user experience. The chapter also covers the hardware and software tools used, including smart screens, Raspberry Pi, and iBeacon technology, as well as web programming tools and AI techniques. The model has been tested and validated in a real-world setting, demonstrating its practical applicability and potential benefits for various industries.AI Generated
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AbstractThe intelligent evacuation subsystem has great importance for all intelligent buildings. In addition to natural disasters such as tornados, floods, human hazard situations such as terrorist attacks, electrical leaks, and fires reveal the necessity of emergency evacuation systems. The model proposed in this study suggests a model in which users, depending on their location, can see the exit routes from the smart screens which are placed in the smart building. This model uses instantaneous data obtained by evaluating the distance of the relevant location to the exits, the width of the exit doors, and the crowdedness in front of the exit doors. This data then proposes a method that shows the best result from smart screens by using the Analytical Hierarchical Process technique, which is a multi-criteria decision-making method. This study demonstrates the establishment and design of an integrated smart building management system that combines smart survey systems and Augmented Reality-based smart building promotion systems that use different types of technological tools and collect different types of data. Intelligent evacuation systems, smart guest guidance systems, smart voice, music, announcement and video usage systems, computer vision-based smart face recognition, and personnel tracking systems are some of the technologies used. For what purposes, particularly in terms of management, how these tools can be used, what hardware and software tools are utilized to create this system, and the procedures to be carried out in the application process are the subjects of the study. The importance of emergency evacuation systems, especially in feature buildings such as hospitals and educational institutions, is increasingly being managed by intelligent systems that will work at any time, not just on paper and at the initiative of some people, but also independent of individuals. This study demonstrates with an example how an integrated model can be designed and implemented. The technological system that is able to think like people and make the right decisions will spread from houses to buildings, from buildings to campuses, from campuses to regions, from regions to provinces, and from provinces to countries. With the right approach, the societies that use human intelligence and experience with the philosophy of Industry 4.0 at every point of life will increase their welfare as they will increase productivity. If artificial intelligence is used as suggested in this study, it will continue to affect lives positively and open new horizons. -
Chapter 3. Artificial Intelligence for Smart Cities: Locational Planning and Dynamic Routing of Emergency Vehicles
Ugur EliiyiThis chapter delves into the application of artificial intelligence for optimizing emergency services in smart cities, with a particular focus on locational planning and dynamic routing of emergency vehicles. The introduction highlights the significance of AI in managing emergency responses, as demonstrated by the Wuhan case study during the COVID-19 pandemic. The authors discuss various optimization models and solution approaches for vehicle routing problems, including the Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time Windows (VRPTW). The chapter also explores ambulance location and relocation problems, emphasizing the importance of strategic, tactical, and operational planning. Real-world applications and future research directions are discussed, with a focus on the integration of AI and data analytics to enhance emergency service efficiency and response times.AI Generated
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AbstractTo enhance the efficiency and effectiveness of essential emergency services such as ambulances, fire brigades, and police, one of the most important problems to tackle is to minimize the response times of these emergency vehicles. As well as attaining optimal waiting sites and deployment strategies for the emergency vehicles, the optimal routing and traffic preemption of the vehicles are also crucial in minimizing response times and maximizing coverage. As locational planning and dynamic routing of the emergency vehicles relate to many different situations with varying emergency levels, they are a crucial part of the smart city concept. In this chapter, we present a perspective for the use of artificial intelligence and optimization in sustainable healthcare logistics within a smart city. We provide a survey of literature and identify many applications from around the globe. Related mathematical models and solution approaches are also presented, as necessary. -
Chapter 4. The “Transformative” Effect of Artificial Intelligence Systems (AIS) in Entrepreneurship
Umut Sanem ÇitçiThe chapter delves into the 'transformative' effect of Artificial Intelligence Systems (AIS) in entrepreneurship, focusing on how AI can overcome the limitations of human decision-making under uncertainty. It discusses the potential benefits and challenges of AI in entrepreneurship, including the emergence of new entrepreneurial types such as digital and information entrepreneurs. The text also explores the macro-level impacts of AI on entrepreneurship, such as increased democratization and potential ethical concerns. Additionally, it highlights the role of AI in entrepreneurship education and research, suggesting future research directions and the need for ethical regulation in AI use.AI Generated
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AbstractIn this chapter, one of the scholars working on entrepreneurship shares her ideas about how the field of entrepreneurship meets artificial intelligence systems and what this meeting can mean for the field of entrepreneurship today and in the future, with a supple wording as possible. Entrepreneurship is an important phenomenon that increases individuals’ and countries’ welfare and plays a role in social transformation. Its importance appeals to a large number of researchers. What makes these studies possible to stand out from the research crowd depends on their design as modern, contemporary, and preliminary studies. The author also wanted to reveal the possibilities of artificial intelligence systems, which offer the opportunity to conduct innovative research, in the field of entrepreneurship.The chapter titled “The ‘Transformative’ Effect of Artificial Intelligence Systems (AIS) in Entrepreneurship” primarily targets researchers who are interested in entrepreneurship research and want to gain general information about the intersections with artificial intelligence. As it is known, artificial intelligence systems have highly specialized literature. This situation causes most researchers to develop a perception of the difficulty of the field. The author promises to transform this perception positively and discover artificial intelligence, which we inevitably have to dive into and learn, from the perspective of entrepreneurship, when the development of technology is considered. For this purpose, in the content design of the chapter, how artificial intelligence will affect the entrepreneurship classification and the way entrepreneurs do business; how artificial intelligence will transform the big picture through entrepreneurship; what can be the usage of artificial intelligence for entrepreneurs; how artificial intelligence will affect entrepreneurship education; how will artificial intelligence affect entrepreneurship research with the use of new technologies for data gathering and data analyzing are included. It can be said that the chapter has a guiding quality, especially for graduate students who want to do entrepreneurship research but have not yet determined the research subject. For example, the subject of postgraduate research may be how artificial intelligence systems will transform the gender-based problems faced by women entrepreneurs. The steps in the literature regarding this subject can be found in the chapter.This book, in which artificial intelligence is evaluated from various perspectives, could be said to be incomplete if it did not include a discussion in terms of entrepreneurship. With this awareness, the author endeavored to offer her readers a good reading and knowledge acquisition experience by referencing as many sources as possible and sharing different ideas. -
Chapter 5. A Machine Learning Framework for Data-Driven CRM
Serhat Peker, Özge KartThis chapter introduces a comprehensive machine learning framework designed to enhance data-driven CRM strategies. It underscores the critical role of machine learning in understanding customer behavior through transactional data, enabling businesses to build stronger relationships and increase loyalty. The framework is structured into five main steps: problem formulation, data preparation and pre-processing, implementation of ML algorithms, evaluation, and interpretation of results. By integrating both supervised and unsupervised learning techniques, the framework offers a holistic approach to customer segmentation, market-basket analysis, customer-centric classification, and forecasting. The chapter also reviews relevant literature on CRM and machine learning techniques, providing a solid foundation for practitioners seeking to leverage these tools effectively. The proposed framework is designed to be practical and systematic, making it an invaluable resource for researchers and professionals aiming to enhance their CRM strategies through data-driven insights.AI Generated
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AbstractIn today’s digital world, enterprises accumulate large quantiles of customer data which drives firms to implement data-driven CRM strategies to manage customer relationships. In CRM, machine learning techniques are widely used as a tool for using customer data and thereby acquiring knowledge from such data. In this context, this research presents a holistic framework for the implementation of machine learning methods in data-driven CRM applications. The proposed framework relies on past transactional data of customers and employs state-of-art machine learning techniques. This research serves as a foundation for future studies on data-driven CRM applications utilizing machine learning techniques.
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The Impact of AI on Accounting, Finance and Fraud
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Frontmatter
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Chapter 6. How Blockchain and Artificial Intelligence Will Effect the Cloud-Based Accounting Information Systems?
Betül Şeyma AlkanThis chapter delves into the transformative potential of blockchain and artificial intelligence in cloud-based accounting information systems. It begins by defining accounting as an information system and highlighting the traditional functions of recording, classifying, summarizing, and analyzing financial data. The text then explores how these functions can be significantly enhanced through the integration of AI, which can automate data processing, improve accuracy, and enable real-time analysis. The chapter also discusses the role of blockchain in creating a decentralized, secure, and transparent accounting system. It introduces the concept of triple-entry accounting, which combines traditional double-entry methods with blockchain technology to provide an additional layer of verification and security. The synergy between AI and blockchain is emphasized, showcasing how these technologies can work together to optimize data management, enhance security, and reduce the risk of fraud. The chapter concludes by proposing a model that integrates these technologies to create a modern, efficient, and reliable accounting information system. By reading this chapter, professionals will gain valuable insights into the future of accounting and the critical role that AI and blockchain will play in shaping it.AI Generated
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AbstractDigitalization reduces unnecessary workload by accelerating and diversifying business processes and enables it to focus on more useful areas. In this context, the transformation of accounting in the digital age aims to ensure efficiency in accounting transactions with real-time accounting information system integration. In terms of real-time accounting, it is important to benefit from the power of new technologies by connecting all current technologies to every stage of financial accounting processes and to establish a solid integrated system. The contribution of every new technology to the process is inevitable within the scope of cloud-based accounting information systems. In order to find an answer to the question of how artificial intelligence and blockchain technologies will affect cloud-based accounting systems, a detailed literature review has been conducted and discussed conceptually. In this context, the study focuses on the advantages of cloud-based accounting systems unlike traditional accounting systems, the effectiveness of blockchain and artificial intelligence technologies in accounting processes, and the synergy of blockchain and artificial intelligence. Today, accounting basic functions have been significantly integrated into artificial intelligence technology. Decentralized artificial intelligence emerging as a combination of artificial intelligence and blockchain allows the processing of reliable, digitally signed, and secure shared data that is stored on a decentralized and distributed blockchain without trusted third parties or intermediaries. The basic understanding of this decentralized, reliable, and secrecy system is based on the reliability and credibility of information. Central data storage can be highly sensitive in terms of security and privacy when it contains personal and private data about users, operations, and financial information. Artificial intelligence applications can expose the capacity and scaling issues of the centralized infrastructure that needs to process, transform, and store big data sets. Blockchain-based decentralized storage infrastructure will simplify cryptographically secure data storage across participatory networks. Thus, technology integration will offer benefits such as enhanced data security, collective decisions making, decentralized intelligence, and high efficiency. Multi-user accounting processes involving stakeholders such as business management, regulators, financial institutions, or government are inherently inefficient by reason for the multilateral authorization of business transactions. The integration of artificial intelligence and blockchain will enable automatic and rapid verification of data-asset-value transfers between different stakeholders. Thus, it is clear that the stakeholders involved in the process (financial advisors, auditors, public and fiscal authorities, shareholders, creditors) will also provide practical solutions to all their needs. -
Chapter 7. Machine Learning Applications for Fraud Detection in Finance Sector
Pelin Yıldırım Taşer, Fatma BozyiğitThe chapter delves into the increasing prevalence of financial fraud due to the rise of online banking and financial services. It discusses the limitations of traditional fraud detection methods and highlights the advantages of machine learning techniques, such as supervised and unsupervised learning, in identifying fraudulent activities. The chapter reviews various machine learning algorithms, including Naive Bayes, Decision Trees, Support Vector Machines, and Artificial Neural Networks, and their applications in detecting bank fraud, insurance fraud, and corporate fraud. It also explores ensemble learning methods like Random Forest, AdaBoost, and Stacking, which have shown promising results in improving fraud detection accuracy. Additionally, the chapter discusses the emergence of deep learning techniques, such as Convolutional Neural Networks and Autoencoders, in the finance sector. The chapter concludes by providing insights into the future applications of machine learning in financial fraud detection and emphasizes the importance of these advanced techniques in addressing the growing problem of financial fraud.AI Generated
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AbstractDue to advances in information technology, instantaneous accessibility to financial services through digital channels has increased. Although digital platforms’ usage makes an individual’s life more comfortable, it may also cause some critical consequences like financial fraud which causes critical losses for companies in the industrial sector, investors, and governments. Identification of frauds can be challenging task for a human because it may be necessary to analyse high volume data during long time periods. An alternative is to use financial data as a fraud detection tool to automatically classify fraudulent activities. Currently, there are many practical solutions for automatically detect frauds in the finance domain. In this chapter, we examined on three different fraud types (bank fraud, insurance fraud, and corporate fraud) in finance sector and reviewed the studies using machine learning methods to detect financial fraud in a detailed manner. The findings from this review show that most commonly applied algorithms for financial fraud detection are Decision Tree, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Random Forest and most of machine learning-based studies were performed in bank fraud field. This chapter also reveals that deep learning and ensemble-based machine learning applications has been frequently preferred in recent years to improve detection performance of the frauds in finance sector. -
Chapter 8. The Importance of Graph Databases in Detection of Organized Financial Crimes
Buket DoğanThe chapter delves into the critical role of graph databases in detecting organized financial crimes, which are characterized by illegal money flows and complex relationships. It compares graph databases with traditional relational databases, showcasing the former's superior ability to model and query social networks. The text also includes a case study on first-party bank fraud, illustrating how graph databases can effectively uncover fraudulent networks that traditional methods often miss. By leveraging graph databases, professionals can gain a deeper understanding of the intricate relationships between entities involved in financial crimes, enabling more effective prevention and detection strategies.AI Generated
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AbstractThis paper aims to reveal how important and irreplaceable graph databases are in discovering and preventing organized financial crimes, with their reasons. In order to find out organized financial crime, its perpetrators, and victims, it is necessary to uncover the multi-layered and hidden relationships between different entities. It is almost impossible to achieve this by developing a software solution on a relational database due to the large volume of data and intricate multi-layered relationships, and it is insufficient at many points. In this paper, we propose to overcome this by visualizing these complex relationships on graph databases. Graph databases have much more natural analytical power for social networks than relational databases. Graph databases are designed to model, store and query social networks with multiple complex relationships. Each entity in the social network can act as an individual entity and interact with other entities. While there are several different ways to reveal these illegal relationships, the most obvious, most used, and most effective method is to track money transfers between different entities. In this paper, we will display money transfers between different entities in a graphical database. -
Chapter 9. Practices of Natural Language Processing in the Finance Sector
Fatma Bozyiğit, Deniz KılınçThis chapter delves into the utilization of Natural Language Processing (NLP) in the finance sector, focusing on two key areas: financial market forecasting and sensitive data detection. The first part of the chapter discusses how NLP techniques, such as lexical, syntactic, and semantic analysis, can be applied to financial reports, news, and social media comments to improve market dynamics modeling. The second part addresses the challenge of detecting and preventing unintended distribution of sensitive content, emphasizing the use of Named Entity Recognition (NER) and domain ontologies to enhance data security. The chapter concludes by summarizing the advantages of existing approaches and identifying open issues in both financial market forecasting and sensitive data detection, making it a valuable resource for professionals seeking to leverage NLP in financial applications.AI Generated
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AbstractNatural language processing (NLP) is a subfield of artificial intelligence that focuses on extracting meaning from unstructured data. It has become widely used due to advances in information technology and so increasing textual data in recent years. Since a large portion of the available information in the finance domain is in textual form (e.g., reports, contracts, agreements), researchers have increased their scrutiny of using NLP that is necessary to obtain insight from such collections. This chapter synthesizes the recent literature using NLP methods in financial tasks to demonstrate the state of current knowledge and its implications for future studies. Accordingly, we examine the usage of NLP methods under two sections. In the first section, we focus on NLP analysis models to determine financial market dynamics using news and user comments in the digital platforms. In the second section, we discuss NLP methods to detect sensitive user data (e.g., identity number, credit card number, telephone number) in the financial documents.
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Specialized Topics on AI Implementations
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Frontmatter
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Chapter 10. Higher Education and Labor Market Transformation in the Era of Industry 4.0 in a Developing Country: The Case for Turkey
Aslı Dolu, Hüseyin İkizlerThe chapter delves into the profound changes wrought by Industry 4.0 on higher education and the labor market in Turkey. It begins by tracing the historical trajectory of industrial revolutions, highlighting the unique aspects of Industry 4.0, such as smart systems and internet-based solutions. The focus then shifts to Turkey, where the automotive sector has been significantly transformed by these technologies. The study employs the Synthetic Control Method to analyze the impact of Industry 4.0 on various industrial sectors, revealing both positive and negative effects on education ratios. Notably, while sectors like furniture manufacturing saw an increase in education ratios, others like automotive and textiles experienced a decline. The chapter concludes with policy recommendations, emphasizing the need for strategic planning and vocational training to adapt to the new industrial landscape.AI Generated
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AbstractWith Industry 4.0, especially in Germany, as of 2013, the industrial countries started to form their strategy documents. Industry 4.0 is a part of the global megatrends of digitalization in all areas of life and economy. This transformation in Turkey, primarily to the automotive and telecommunications companies have started by the end of 2014. In the study, we analyze to what extent the adjustment in education policy and the labor market develops in the era of Industry 4.0. For this purpose, we use the TURKSTAT Household Labor Force Survey data (2009–2018). We evaluate the impact of Industry 4.0 with the synthetic control method, the change in the sectoral employment rates, and analyze whether the share of the workers who graduated from particular departments vary in this era. Using the synthetic control method, we find that while Industry 4.0 positively affects the furniture sector's Industry 4.0 related education ratio, the impact occurs negatively in the automotive, textiles, and transport equipment sectors. Also, we find no effect on the food sector. -
Chapter 11. The Role of Artificial Intelligence in Health Care
İpek Deveci KocakoçThe chapter delves into the profound impact of artificial intelligence on healthcare, discussing key technologies such as natural language processing, machine learning, and computer vision. It explores how AI is transforming medical diagnosis, drug discovery, and patient care, while also addressing ethical concerns and future prospects. The chapter emphasizes the potential of AI to enhance healthcare efficiency, reduce costs, and improve patient outcomes, making it a must-read for those interested in the intersection of technology and medicine.AI Generated
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AbstractAs the global population ages and longevity increases, health systems around the world are facing significant difficulties in building the workforce required to provide healthcare services due to increasing service demands and increasing innovation costs. The healthcare industry produces large volumes of data, and challenges in cost and patient outcomes are increasing. In order to provide health services in the fastest, most accurate, and highest quality way while also meeting the needs of both patients and healthcare organizations, healthcare professionals must reach the most accurate and up-to-date information and utilize this information by using computerized support systems. For this reason, it is inevitable for healthcare systems to become a structure supported by artificial intelligence that both speeds up and facilitates the way of doing business and provides some basic information and services to patients without being too dependent on the system. Artificial intelligence (AI) aims to mimic human intelligence to perform tasks and can recursively improve itself based on the information it gathers. By increasing the availability of healthcare data and enabling rapid progress in analytical techniques, it brings paradigm changes to healthcare services. Artificial intelligence has been used or tested for a variety of health and research purposes, including management of chronic conditions, workload reduction of doctors and nurses, drug discovery, provision and prevention of health care, diagnosis, treatment of diseases, and patient monitoring. Artificial intelligence has the ability to transform medicine through the role of physicians and nurses and to transform medicine through new science research and delivery models that revolutionize medical practices and enhance person and public health outcomes. Today, artificial intelligence practices are carried out in many private and public health institutions in many countries. In this section, we will discuss in detail the application of artificial intelligence in the modern healthcare system with advantages and possible disadvantages and challenges. -
Chapter 12. An Overview of New Generation Bio-Inspired Algorithms for Portfolio Optimization
Hilal Arslan, Onur Uğurlu, Deniz Türsel EliiyiThe chapter delves into the complexities of portfolio optimization (PO), a critical problem in finance, and presents an overview of new generation bio-inspired algorithms designed to tackle it. It begins with a formal definition of PO and its mathematical formulation, including real-life constraints such as floor-ceiling, cardinality, and transaction costs. The chapter then provides a detailed review of traditional bio-inspired algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), and their adaptations for PO. The main focus, however, is on more recent algorithms such as Artificial Bee Colony Optimization (ABCO), Bacterial Foraging Optimization (BFO), Bat Algorithm (BA), and others. Each algorithm is discussed in terms of its origin, inspiration, flow, and state-of-the-art applications in PO. The chapter also highlights the advantages and adequacy of these algorithms, offering insights into their performance and potential for future research. By exploring these advanced methods, the chapter aims to inspire further innovation and application in the field of financial optimization.AI Generated
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AbstractBio-inspired computing is one of the foremost subfields of artificial intelligence, which aims to tackle complex optimization problems. The main advantage of bio-inspired algorithms over traditional methods is their searching ability. Portfolio selection is a popular optimization problem in economics and finance. It aims to find an optimal allocation of capital among a set of assets by maximization of return with simultaneous minimization of risk. Since the portfolio optimization problem is NP-hard, a large number of researchers have resorted to bio-inspired algorithms to deal with the computational complexity. This study provides an overview of the new generation bio-inspired algorithms from the recently published literature for portfolio optimization. Besides, opportunities for future research within this area discussed. -
Chapter 13. The Effects of Artificial Intelligence on the Insurance Sector: Emergence, Applications, Challenges, and Opportunities
Işıl Erem CeylanThis chapter delves into the significant impact of artificial intelligence (AI) on the insurance sector, examining its emergence, applications, and the challenges it presents. It discusses how AI technologies such as machine learning and blockchain are transforming processes like claim handling, fraud detection, and customer relations. The chapter also explores the opportunities AI brings, such as the creation of new insurance products and services, and the potential for increased efficiency and cost savings. Additionally, it highlights the future trends and predictions for AI in the insurance sector, making it a must-read for professionals seeking to understand the evolving landscape of the industry.AI Generated
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AbstractLike many sectors around the world (health care, manufacturing, transportation, customer service, banking, etc.), insurance sector has also started to adopt the artificial intelligence (AI) technologies to a large extent. With this adoption process, insurance companies can instantly respond to customer needs in the light of digital data and focus more on value-added areas. AI technologies also contribute to effective communication with customers, reduce costs, develop new products and services, and improve the competitive environment to prevent insurance companies from being indifferent to these technological developments. On the other side, these technologies identify potential risks in advance, inform the insured, and support them to take the required precautions. While this situation is supportive for the insured part, it may create a number of threats for insurance companies as it causes a decrease in insurance premiums. These threats are pushing insurance companies to go through a number of transformations in their business models to turn the disadvantaged situation in their favor. This being the case, addressing these innovations is of great importance not to fall behind these developments. From this point of view, this chapter explores the AI innovations and their effects on the insurance sector and reveals the use cases of global and Turkish insurance in the implementation of AI. -
Chapter 14. Understanding the Utilization of Artificial Intelligence and Robotics in the Service Sector
Büşra Alma Çalli, Levent ÇalliThe chapter delves into the application of artificial intelligence (AI) and robotics in the service sector, highlighting the transformative impact these technologies are having on various industries. It begins by tracing the evolution of AI from its early skepticism to its current widespread use, particularly in sectors like office work, production, and transportation. The chapter classifies robots into industrial, professional service, and personal service categories, each with distinct applications. It also discusses the different types of AI—mechanical, analytical, intuitive, and empathetic—and their potential in service industries. The text explores how AI and robotics are redefining service interactions, making them more efficient and effective, but also raising concerns about job displacement. It emphasizes the importance of human-AI collaboration, showcasing examples like chatbots and robot advisors that enhance customer engagement and service provision. The chapter further examines the impact of AI on both employees and customers, discussing the need for restructuring work and the potential for increased efficiency and job satisfaction. It concludes by noting the need for further research on the ethical implications and sector-specific effects of AI and robotics in the service industry.AI Generated
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AbstractArtificial Intelligence (AI) is increasingly posing a threat to human service jobs in several sectors. According to projections, AI will put a remarkable percent of service sector employment at risk, including a wide range of jobs. Since AI offers chances to improve service provision efficacy and enhance consumer engagement, it is expected that artificial intelligence and service robots will become widespread in many countries. It is anticipated that service provision tasks will be affected by automation and artificial intelligence in different ways. Regarding the replacement of jobs, it is envisaged that job designs will transform on a task-based rather than top-down basis, with simple cognitive and analytical tasks being performed first by service robots, and then complex emotional and social tasks are likely to be supported by robot-human collaboration. As a result, it is critical to assess artificial intelligence as it penetrates every aspect of our life, particularly in terms of consumer acceptance & use and the service industry, where it is perceived as a significant threat to service jobs. Depending on the predictions regarding the widespread applications of AI shortly and the change of job designs in the service sector, the evaluation of the current research in the area is critical for reducing the gap between practitioners and academic studies. Until far, most AI for service research has classified the service tasks and attempted to explain how the transformation of jobs can take place on a task basis. On the other hand, some research has conceptually discussed the potential benefits and drawbacks of AIs in the service industry. A stream of research has empirically measured user acceptance of different AI applications, their antecedents, and consequences. Hence, this chapter aims to synthesize and discuss the previous literature findings to have a broad understanding of the current research output. Some literature gaps, particularly in terms of human-robot interaction, have been identified, and avenues for future research have been emphasized. Finally, a roadmap for future research is presented.
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Backmatter
- Title
- The Impact of Artificial Intelligence on Governance, Economics and Finance, Volume 2
- Editor
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Sezer Bozkuş Kahyaoğlu
- Copyright Year
- 2022
- Publisher
- Springer Nature Singapore
- Electronic ISBN
- 978-981-16-8997-0
- Print ISBN
- 978-981-16-8996-3
- DOI
- https://doi.org/10.1007/978-981-16-8997-0
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