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The purpose of this paper is to investigate the factors influencing Palestinian consumer attitudes toward artificial intelligence (AI)-powered online banking, focusing on performance expectancy, effort expectancy, social influence and facilitating conditions while considering the moderating role of trust in financial institutions.

To test the hypotheses, an empirical study with a questionnaire was carried out. The study was completed by 362 Palestinian customers who use online banking services.

The findings of this paper show that performance expectancy, effort expectancy, social influence and facilitating conditions significantly influence consumer attitudes toward AI-powered online banking. Furthermore, trust in financial institutions as a moderating variable strengthens the impact of performance expectancy, effort expectancy, social influence and facilitating conditions on consumer attitudes toward AI-powered online banking. Therefore, more studies should focus on certain fields and cultural contexts to get a more thorough grasp of the variables influencing adoption and acceptability.

The study's findings may be specific to the Palestinian context, limiting generalizability. The reliance on self-reported data and a cross-sectional design may constrain the establishment of causal relationships and the exploration of dynamic attitudes over time. In addition, external factors and technological advancements not captured in the study could influence Palestinian consumer attitudes toward AI-powered online banking.

Financial institutions can leverage the insights from this research to tailor their strategies for promoting AI-powered online banking, emphasizing factors like perceived security and ease of use. Efforts to build and maintain trust in financial institutions are crucial for fostering positive consumer attitudes toward AI technologies. Policymakers can use these findings to inform regulations and initiatives that support the responsible adoption of AI in the financial sector, ensuring a more widespread and effective implementation of these technologies.

This research delves into Palestinian consumer attitudes toward AI-powered online banking, focusing on trust in financial institutions. It aims to enrich literature by exploring this under-explored area with meticulous examination, robust methodology and insightful analysis. The study embarks on a novel journey into uncharted terrain, seeking to unearth unique insights that enrich the existing literature landscape. Its findings offer valuable insights for academia and practitioners, enhancing understanding of AI adoption in Palestine and guiding strategic decisions for financial institutions operating in the region.

With the great changes brought by information technology, there is also a challenge for the elderly's acceptance. This study aimed to determine the antecedents of elderly people's usage intention of financial artificial intelligent customer service (FAICS) and to examine the relationships between various factors and thus to help them better adapt to the digital age.

A mixed method, including the qualitative and quantitative study, was utilized to explore answers of the research questions. As the qualitative study, the authors used semi-structured interviews and data coding to uncover the influencing factors. As the quantitative study, the authors collected data through questionnaires and tested hypotheses using structural equation modeling.

The results of data analysis from interviews and questionnaires suggested that perceived anthropomorphism and virtual identity of elderly users have a positive impact on their perceived ease of use, and the perceived intelligence of elderly users positively influences their perceived ease of use, satisfaction and perceived usefulness. Additionally, the elderly's cognition age can moderate the effects of perceived usefulness and satisfaction on their usage intention of FAICS.

This study contributes to the literature by taking the elderly group as the research participants and combining those influencing factors with technology acceptance model and information systems success model. The findings provide a basis for accelerating the promotion of FAICS and help address the problem that the elderly have difficulty adapting to a new technology.

The automation of services is rapidly growing, led by sectors such as banking and financial investment. The growing number of investments managed by artificial intelligence (AI) suggests that this technology-based service will become increasingly popular. This study examines how customers' technology readiness and service awareness affect their intention to use analytical AI investment services.

Hypotheses were tested with a data set of 404 North American-based potential customers of robo-advisors. In addition to technology readiness dimensions, the potential customers' characteristics were included in the framework as moderating factors (age, gender and previous experience with financial investment services). A post-hoc analysis examined the roles of service awareness and the financial advisor's name (i.e., robo-advisor vs. AI-advisor).

The results indicated that customers' technological optimism increases, and insecurity decreases, their intention to use robo-advisors. Surprisingly, feelings of technological discomfort positively influenced robo-advisor adoption. This interesting finding challenges previous insights into technology adoption and value co-creation as analytical AI puts customers into a very passive role and reduces barriers to technology adoption. The research also analyzes how consumers become aware of robo-advisors, and how this influences their acceptance.

This is the first study to analyze the role of customers' technology readiness in the adoption of analytical AI. The authors link the findings to previous technology adoption and automated services' literature and provide specific managerial implications and avenues for further research.

Given that managers play a crucial role in developing and deploying AI for marketing financial services, this study was aimed at better understanding their awareness regarding AI and the challenges they are facing in providing the attendant technologies, as well as highlighting key stakeholders and their collaborative efforts in providing financial services.

Exploratory, inductive research design. The data was gathered through semi-structured interviews with 47 bank managers in both developed and developing countries, including the United Kingdom, Canada, Nigeria and Vietnam.

Managers are aware of the prospects of AI and are making efforts to address AI as a business need but find that there often exist certain challenges in accelerating AI adoption. The study also presents a conceptual framework of AI in relation to financial service marketing, which captures and highlights the interactions among the customers, banks and external stakeholders, as well as the regulators.

Banks must understand their business objectives, the available resources and the needs of their customers. Managers should keep the ethical implications of their working relationships in mind when selecting a team or collaborating with partners. In addition, managers should be trained and assisted in comprehending AI in relation to financial services, while the regulators must be involved in the development of AI for financial service marketing. Finally, it is critical to communicate the prospects for AI to consumers.

This study provides empirical insight into the opportunities, prospects and challenges pertaining to the use of AI in the area of financial service marketing. It also specifically calls into question certain preconceptions regarding AI and its role in financial services, the chatbots adopted for financial service delivery and the role of marketing managers in developing AI.

The objective of this study is to provide a systematic review of the literature on artificial intelligence (AI) in customer-facing financial services, providing an overview of explored contexts and research foci, identifying gaps in the literature and setting a comprehensive agenda for future research.

Combining database (i.e. Scopus, Web of Science, EBSCO, ScienceDirect) and manual journal search, the authors identify 90 articles published in Australian Business Deans Council (ABDC) journals for investigation, using the TCCM (Theory, Context, Characteristics and Methodology) framework.

The results indicate a split between data-driven and theory-driven research, with most studies either adopting an experimental research design focused on testing the accuracy and performance of AI algorithms to assist with credit scoring or investigating AI consumer adoption behaviors in a banking context. The authors call for more research building overarching theories or extending existing theoretical perspectives, such as actor networks. More empirical research is required, especially focusing on consumers' financial behaviors as well as the role of regulation, ethics and policy concerned with AI in financial service contexts, such as insurance or pensions.

The review focuses on AI in customer-facing financial services. Future work may want to investigate back-office and operations contexts.

The authors are the first to systematically synthesize the literature on the use of AI in customer-facing financial services, offering a valuable agenda for future research.

This paper aims to analyze the impact of DARQ technologies (distributed ledger, artificial intelligence, extended reality, quantum computing) in the financial sector, focusing on artificial intelligence (AI) applications in personalized banking, which consists of treating every customer as a segment of one. The research has two main goals. First, providing a complete and organic analysis of the DARQ technologies framework currently missing in the literature. Because this research focuses on the financial sector, more attention is dedicated to DARQ technologies in this industry. Second, studying applications of one of the DARQ technologies, AI, in personalized banking, where it appears to have a great potential impact.

The research analyses both the supply side, collecting secondary data from documentation, reports and research studies to study the major trends and results obtained by leading banks, and on the demand side, collecting primary data through a dedicated survey and elaborating opinions and preferences of potential customers. Using this information, a detailed go-to-market plan based on the framework elaborated by Bain and Co. in 2012 is developed, considering the hypothesis of a well-known universal bank, operating globally, with an established brand and access to modern AI technologies, which decides to invest in this field as a priority.

In addition to giving a detailed overview of DARQ technologies from a technical and a business perspective, the results related to the hypothetical case of the study help to understand which would be the most suitable target for the launch phase, which value proposition should be offered and how to deliver it, but also how to evolve the project to attract more customers and strengthen the relationship with the existing ones. Nevertheless, this research could be a starting point for future studies and updates, considering related evolutions, investigating more representative demand samples or analyzing how the combination of more DARQ technologies could be applied to the financial sector.

Some limitations affect this work. First, the topics studied are evolving rapidly and partially dependent on other innovations under development; therefore, they may become obsolete and less significant in the next years. As regard the data collection, the supply-side analysis involves strategical information kept private by companies; therefore, the collected data probably miss some useful details. As concerns primary data, the sample could have been larger and more heterogeneous and biases and misinterpretations could have affected the answers. A compromise has been found between the time and resources available and the qualitative and quantitative characteristics of the sample.

This research could be a tool for financial companies interested in investing in AI for personalized banking, but it also provides useful insights about the whole DARQ framework, which could be interesting for all the financial and nonfinancial firms. Applying AI effectively and efficiently could offer great benefits, both economic and noneconomic, to financial firms but also to their customers, who could benefit from hyper-tailored services at a reasonable and affordable price, whereas in the past, they were reserved only for very important person clients. This win-win situation could lead the way to further investments and consequent innovations in the future.

Some issues still exist, mainly about data security and privacy, but also the social risk linked to the labor market due to the AI substitution for some tasks and the related shift in professionals required by employers, which could negatively affect the salary gap among workers with different levels of educations, tightening up existing inequality problems. An effort by public and private subjects will be required to make this transition inside the labor market smoother. Despite this, the research shows that AI applications in personalized m-banking could mutually benefit both the demand and the supply of the market.

Apart from the organic overview offered on DARQ technologies and their related business applications, currently missing in the literature, which could be useful for a better comprehension of the topic and could also give interesting insights to firms, this research presents an original and concrete roadmap to follow for financial companies interested in delivering a personalized mobile banking service leveraging on AI. Every step presented in the output of this work is based on an in-depth analysis of past, and present actions carried out and result obtained by competitive firms on the market and on needs and preferences observed among potential customers.

The financial technology (FinTech) era has brought a revolutionary change in the financial sector’s customer experiences at the national and global levels. The importance of artificial intelligence (AI) in the context of FinTech services for enriching customer experiences has become a new norm in this modern era of technological advancement. So, it becomes crucial to understand the customer’s perspective. The current research ranks the factors and sub-factors influencing customers’ perceptions of AI-based FinTech services.

The sample size for this study was decided to be 970 respondents from four Indian cities: Mumbai, Delhi, Kolkata and Chennai. The Fuzzy-AHP technique was used to identify the primary factors and sub-factors influencing customers’ experiences with AI-enabled finance services. The factors considered in the study were service quality, trust commitment, personalization, perceived convenience, relationship commitment, perceived sacrifice, subjective norms, perceived usefulness, attitude and vulnerability. The current research is both empirical and descriptive.

The study’s three top factors are service quality, perceived usefulness and perceived convenience, all of which have a significant impact on customers’ experience with AI-enabled FinTech services discussing sub-criteria three primary criteria for customers’ experience for FinTech services include: “Using FinTech would increase my effectiveness in managing a portfolio (A2)”, “My peer groups and friends have an impact on using FinTech services (SN3)” and “Using FinTech would increase my efficacy in administering portfolio (PU2)”.

The current study is limited to four Indian cities, with 10 factors to understand customers’ preferences in FinTech. Further research can focus on other dimensions like perceived ease of use, familiarity, etc. Future studies can have a broader view of different geographical locations and consider new tech to understand customer perceptions better.

The study’s findings will significantly assist businesses in determining the primary aspects influencing customers’ experiences with AI-enabled financial services. As a result, they will develop strategies and policies to entice clients to use AI-powered FinTech services.

Existing AI research investigated several vital topics in the context of FinTech services. On the other hand, the current study ranked the criteria in understanding customer experiences. The research will substantially assist marketers, business houses, academicians and practitioners in understanding essential facets influencing customer experience and contribute significantly to the literature.

Integrating artificial intelligence (AI) into various industries, including the financial sector, has transformed them. This paper aims to examine the influence of integrating AI, including machine learning, process automation, predictive analytics and chatbots, on financial institutions and explores its various aspects and areas. The study aims to determine the impact of AI integration on financial services, products and customer experience.

The research study uses quantitative and qualitative methods, as well as secondary data analysis. It investigates four AI subfields: machine learning, process automation, predictive analytics and chatbots.

The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions. These subfields can contribute significantly to enhancing financial services and customer experience. However, the significance of predictive analytics integration and process automation is relatively lower. Although these subfields retain their usefulness, they might necessitate alternative workflows and tools that incorporate human involvement. Overall, AI integration minimizes human interactions and errors in financial institutions.

The research study contributes original insights by exploring the specific subfields of AI within the financial industry and assessing their strategic significance. It provides recommendations for financial institutions to adopt AI integration partially in multiple phases, measure and evaluate the impact of the transformation and structure internal units and expertise to strategize adoption and change.

Artificial intelligence (AI) has already changed the financial industry by increasing the accessibility and inclusiveness of financial services. While acknowledging the challenges posed by AI, this chapter provides insights into the positive impact of AI in promoting financial inclusion. AI has greatly enhanced credit scoring and risk assessment through the use of non-traditional data sources, enabling individuals with limited credit histories and low incomes to access loans and financial products. In addition, the implementation of AI-powered customer identification and verification systems has enhanced security measures while reducing the risk of fraudulent activity. However, the digital divide still remains a challenge to achieve wide financial inclusion. Limited access to technology and digital skills keeps some people from fully benefiting from AI-powered financial services. Access to loans through AI systems may seem convenient, but it also raises concerns about excess borrowing and the resulting unsustainable debt levels. In the age of digital finance, privacy and data security are still key issues. The chapter concludes by highlighting that more research is needed to address these challenges. By fully understanding the potential of AI, as well as its limitations, the power of technology can be harnessed to create more inclusive economic opportunities for everyone, especially those living in poorer areas.

The purpose of this study is to investigate the relationships that influence the value co-creation process and lead to consumer comfort with artificial intelligence (AI) and mobile banking (AIMB) service platforms.

A conceptual model was developed to investigate the value-in-use perceptions of AI-based mobile banking applications via five antecedents: baseline perceptions of current bank service delivery; service delivery configuration benefits; general data security; safety perceptions of specific mobile banking services; and perceptions of AI service delivery. Data were collected from 218 respondents and analyzed using structural equation modeling.

This study highlights the role and importance of the sequential relationships that impact the assessment of AIMB. The findings suggest that service delivery and the customer’s role in value co-creation change as AI is introduced into a digital self-service technology channel. Furthermore, AIMB offers transaction-oriented (utilitarian) value propositions more so than relationship-oriented (hedonic) value propositions.

The sample consisted on digital natives. Additional age cohorts are needed.

As financial institutions redirect their business models toward digital self-service technology channels, the need for customers to feel comfortable while interacting with an AI agent will be critical for enhancing the customer experience and firm performance.

The authors extend the service-dominant logic (SDL) literature by showing that value co-creation is a function of both firms’ technologies and consumers’ value-in-use, a finding that appears to be unique in the literature. The authors advance the digital transformation literature by evaluating AIMB as an interactive process that requires an understanding of key technology constructs, including perceptions of baseline service relationships, desired service configurations, security and safety issues and whether AI is useful for value co-creation. To the best of the authors’ knowledge, this is the first SDL framework that investigates interactive and structural relationships to explain value-in-use perceptions of AIMB.