AI and FinTech adoption in Jordanian banking: Toward inclusive and culturally aligned innovation

We are glad to introduce you that the 2021 3rd International Conference on Artificial Intelligence Technologies and Applications (ICAITA 2021) was successfully held on September 10-12, 2021. In light of worldwide travel restriction and the impact of COVID-19, ICAITA 2021 was carried out in the form of virtual conference to avoid personnel gatherings. Because most participants were still highly enthusiastic about participating in this conference, we chose to carry out ICAITA 2021 via online platform according to the original schedule instead of postponing it.ICAITA 2021 is to bring together innovative academics and industrial experts in the field of Artificial Intelligence Technologies and Applications to a common forum. The primary goal of the conference is to promote research and developmental activities in Artificial Intelligence Technologies and Applications and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Artificial Intelligence Technologies and Applications and related areas.This scientific event brings together more than 100 national and international researchers in artificial intelligence technologies and applications. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.We were pleased to invite three distinguished experts to present their insightful speeches. Our first keynote speaker, Prof. Yau Kok Lim, from Sunway University, Malaysia. His research interests include Applied artificial intelligence, 5G networks, Cognitiveradio networks, Routing and clustering, Trust and reputation, Intelligent transportation system. And then we had Prof. Peter Sincak, from Technical University of Kosice, Slovakia. His research includes Artificial Intelligence and Intelligent Systems. Lastly, we were glad to invite Chinthaka Premachandra, from Shibaura Institute of Technology, Sri Lanka. His research interests include Artificial Intelligence, image processing and robotics. In the last part of the conference, all participants were invited to join in a WeChat group to discuss and explore the academic issues after the presentations. The online discussion was lasted for about 30-60 minutes. The first two parts were conducted via online collaboration tool, Zoom, while the online discussion was carried out through instant communication tool, WeChat. The online platform enabled all participants to join this grand academic event from their own home.We are glad to share with you that we still received lots of submissions from the conference during this special period. Hence, we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Artificial Intelligence Applications & Technologies, Computing and the Mind, Foundations of Artificial Intelligence and other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard.Lastly, we would like to express our sincere gratitude to the Chairman, the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. May the readers could enjoy the gain some valuable knowledge from the proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year.The Committee of ICAITA 2021List of titles Committee member, General Conference Chair, Technical Program Committee Chair, Academic Committee Chair, Technical Program Committee Member, Academic Committee Member are available in this Pdf.

Artificial Intelligence (AI) stands as a technological tool of transforming the traditional Indian banking sector into a modernized financial service provider. The emergence of artificial intelligence (AI) has made a paradigm shift in the banking sector that enhances customer satisfaction, decision-making and operational efficiency. The role of AI with data analytics helps financial institutions prevent cyber security issues, fraudulent transactions and foster compliance. Tools such as AI-powered chatbots, digital payment advisors, and biometric fraud detection systems have shown prominence in improving the quality of customer service in the banking sector. AI has paved way for the customers to be aware of financial options and make more informed financial decisions. AI in banking generally leads to increased customer satisfaction due to enhanced efficiency and personalized experiences, but also raises concerns about privacy and the role of human interaction. Therefore, the present study is an attempt to examine the customer perception and satisfaction towards the application of Artificial Intelligence (AI) in the Indian Banking sector. A survey methodology was conducted among 119 respondents who are customers of the private sector banks both men and women between the age group of 25 and 60 years. The structured questionnaire including the demographic profile and questions related to customer perception and satisfaction in using AI powered banking services is used to collect data. Structural equation Modeling (SEM) is employed to analyze the data. The findings of the study reveals the customer perception and satisfaction towards AI enabled services in the Indian banking sector and provides recommendation for banks to offer customer education and transparency to build trust and ensure secured transactions. The implications of the study underscore the significance of AI and its strategic implementation including digital access, and regulatory measures to enable customer-centric AI integration in the banking sector.

The advances in science and technology have benefited many industries. In recent years, we have witnessed the rapid development of financial technology. All of them worked hard in this area, such as Amazon, UPS, and Wal-Mart International. In China, leading e-commerce platforms such as Alibaba and Tencent actively provided services to SMEs in their ecosystems; Taiwan also make efforts to develop it. The emergence of networking account scientific and technological AMIS provides various payment companies, lending platform, financial robots. Although Taiwan’s innovation industry faces many restrictions on its development, it will still go through it. Therefore, Taiwan has continued to update laws and regulations related to financial technology. The latest rule “Financial Science and Technology Development and Innovation Experiments Regulations” regards the development of Taiwan’s financial technology. FinTech has gradually replaced the traditional financial service model. Through mobile payments, cloud platforms, and artificial intelligence, the technology industry has gradually penetrated into the financial industry. We are willing to make more progress in Taiwan’s financial technology to deepen the understanding of FinTech as a study.

Objectives: This research aimed to empirically examine the transformative impacts of Artificial Intelligence (AI) adoption on financial reporting quality in Jordanian banking, with internal controls as a hypothesized mediation mechanism. Methodology: Quantitative survey data was collected from 130 bank personnel. Multi-item reflective measures assessed AI adoption, internal controls, and financial reporting quality—structural equation modelling analysis relationships between constructs. Findings: The research tested four hypotheses grounded in agency and contingency theories. Confirmatory factor analysis demonstrated sound measurement models. Structural equation modelling revealed that AI adoption significantly transformed financial reporting quality. The mediating effect of internal controls on the AI-quality relationship was supported. Specifically, the path from AI adoption to quality was significant, indicating a positive impact. Despite internal controls strongly predicting quality, its mediating effect significantly shaped the degree of transformation driven by AI adoption. The indirect effect of AI on quality through internal controls was also significant. Findings imply a growing diffusion of AI applications in core financial reporting systems. Practical implications: Increasing AI applications focus on holistically transforming systems, reflecting committing adoption. Jordanian banks selectively leverage controls to moderate AI-induced transformations. Originality/value: This study provides essential real-world insights into how AI is adopted and impacts the Jordanian banking sector, a key player in a fast-evolving developing economy. By examining the role of internal controls, it deepens our understanding of how AI works in practice and offers practical advice for integrating technology effectively and improving information quality. Its mixed methods, unique context, and focus on AI’s impact on organizations significantly enrich academic literature. Recommendations: Banks should invest in integrated AI architectures, strategically strengthen critical controls to steer transformations, and incrementally translate AI innovations into core processes.

Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines.

Focus issue: Artificial intelligence in medical physics.

Purpose: The purpose of this study is to investigate and illuminate the transformative potential of artificial intelligence (AI) in the context of enhancing financial services within Jordanian commercial banks, with a specific focus on credit risk management. By researching into the applications of AI within this sector, the study aims to provide insights into how AI technologies can reshape traditional banking practices and improve the overall efficiency and effectiveness of credit risk management processes. Theoretical framework: The study is grounded in the theoretical framework of technological innovation and strategic management. It draws from the literature on AI adoption in the financial industry and its implications for operational efficiency, risk assessment, and customer experience. Additionally, the study incorporates concepts related to data analysis, machine learning, and predictive modeling as key components of AI-driven transformation within the banking sector. Method/design/approach: To achieve the research objectives, a systematic research design is employed, utilizing survey methods as the primary data collection tool. A sample of 143 employees from major banks located in Amman, Jordan, is selected for participation. The survey encompasses questions designed to gather information about the current state of AI integration, challenges faced, and potential benefits within credit risk management and other financial services. This quantitative approach allows for the collection of structured data that can be statistically analyzed to uncover trends and patterns. Results and conclusion: The findings of the study highlight the substantial potential of AI integration in revolutionizing the operations of Jordanian commercial banks. AI technologies enable more accurate credit assessment, precise analysis of market risks, enhanced financial forecasting capabilities, robust validation of risk models, and advanced evaluation of creditworthiness. Furthermore, the study reveals that AI offers the opportunity for personalized customer service solutions, thereby improving the user experience and guiding customers toward suitable financial services. In conclusion, the study underscores the positive impact of leveraging AI-driven innovation on financial performance and profitability within Jordan's banking sector. Research implications: This study has implications for academia and the banking industry, contributing to knowledge about AI's strategic use in financial innovation and its application in Jordanian commercial banks for credit risk management and customer service enhancement. Originality/value: This research stands out by focusing on Jordanian banks' AI adoption, providing distinct insights into challenges and opportunities in a specific context. Its value lies in guiding banks to effectively integrate AI, enhancing credit risk management and financial services for improved performance and innovation.

Financial technology (FinTech) has revolutionized the banking sector, transforming traditional financial transactions through digital platforms. This study investigates the key factors influencing FinTech adoption among banking customers in Nepal, focusing on financial literacy, government support, perceived ease of use, perceived usefulness, and user innovativeness. Additionally, the study examines the mediating role of user innovativeness in the relationship between financial literacy, government support, and FinTech adoption. Employing a quantitative approach, data were collected from 208 banking customers using a structured questionnaire. PLS-SEM was utilized for analysis. The findings reveal that financial literacy and government support do not have a direct influence on FinTech adoption. However, when mediated by user innovativeness, their indirect effect becomes significant. Perceived ease of use and perceived usefulness exhibit a strong positive relationship with FinTech adoption, highlighting the importance of user-friendly platforms and clear functional benefits. The study’s findings have significant implications for financial institutions, policymakers, and technology developers. While financial literacy and government support alone may not drive adoption, they contribute indirectly by fostering user innovativeness. This underscores the need for strategies that encourage digital adaptability and openness to technology. Future research should explore FinTech adoption across various industries, assess long-term behavioral changes, and examine regional differences in adoption patterns. By understanding these determinants, stakeholders can develop targeted strategies to enhance digital financial inclusion and promote sustainable FinTech growth.

This study investigates the impact of FinTech adoption on sustainable mineral management policies in Australia within the context of Industry 4.0, using quarterly data from 1990Q1 to 2022Q4. Employing the ARDL-Bounds testing approach, Granger causality analysis, and innovation accounting matrix, the research finds a short-term positive association between FinTech adoption, technological readiness, and green mineral extraction. However, both in the short and long run, investment in sustainable mining technologies, government support for FinTech in mining, and environmental compliance exhibit a negative relationship with resource management. Bidirectional causality is observed between regulatory support for mining FinTech, technological finance solutions, and environmentally conscious mineral practices, while unidirectional causality exists from FinTech adoption to sustainable mining practices. Impulse response functions offer insights into the future impact of variables on eco-conscious mining policies, indicating positive influences from FinTech adoption, government support for FinTech in mining, and technological adaptability over the next decade. Conversely, eco-friendly mining investments, environmental conformity, and social license to operate will impact sustainable mineral utilization. Variance decomposition analysis highlights the most significant shocks on eco-friendly resource management over the next ten years, emphasizing the role of sustainable mining technologies, FinTech adoption, and public support for mining endeavours. In the transition to Industry 4.0, this research provides crucial insights for responsibly utilizing Australia's natural resources by leveraging financial technology and technological readiness.

Purpose This study aims to investigate the disclosure practices of Jordanian banks regarding their digital transformation strategies and financial technology (FinTech) innovations. It focuses on how banks communicate strategic achievements, innovations and challenges through qualitative and quantitative disclosures, emphasizing transparency and alignment with stakeholder expectations. Design/methodology/approach This study uses a descriptive content analysis approach to examine the annual reports of 15 Jordanian banks (2015–2022). It investigates FinTech disclosures and digital transformation strategies, focusing on the nature (quantitative vs. qualitative), scope (past achievements vs. future goals) and tone (positive, neutral or negative) of these disclosures. A structured analytical framework ensures systematic categorization and analysis, incorporating contextual insights, descriptive statistics and trend analysis. Findings The study identifies significant variability in FinTech disclosures and digital transformation strategies among Jordanian banks, with qualitative narratives dominating over quantitative metrics. Disclosures have grown notably between 2015 and 2022, reflecting increasing attention to innovation and customer-centric improvements. Key benefits, such as operational efficiency and customer satisfaction, are emphasized more than challenges like cybersecurity and regulatory compliance. Strong correlations highlight that transparency fosters innovation, and overcoming challenges yields substantial benefits, underscoring the strategic importance of consistent FinTech adoption and digital strategies for enhanced banking performance. Originality/value This study offers a novel contribution by positioning disclosure of FinTech and digital transformation strategies as a strategic governance mechanism rather than a passive reporting task. It introduces an integrated analytical framework that combines qualitative content analysis with quantitative trend mapping to examine the tone, scope and evolution of disclosures. By bridging conceptual and methodological gaps in the literature, the study provides fresh insights into how digital narratives shape transparency, accountability and innovation in contemporary banking.

Research Question: This paper reviews different artificial intelligence (AI) techniques application in financial risk management. Motivation: Financial technology has significantly changed the business operations which required transformation of financial industry. The financial risk management needs to be restructured because the methods that have been used in the past became low effective. The artificial intelligence techniques proved their efficiency and contributed to fast, low–cost and improved financial risk management in both financial institutions and companies. Idea: The aim of this paper is to present a state of AI techniques application in financial risk management, as well as to point out the direction in which further application and development could be expected. Data: The analysis was conducted by reviewing various papers, books and reports on AI applications in financial risk management. Tools: The relevant literature systematization was used to provide answers to the question to what extent AI techniques (especially machine learning) could be used in managing financial risk management. Findings: Artificial intelligence largely improved the market risk and credit risk management through data preparation, modelling risk, stress testing and model validation. Artificial intelligence techniques can be useful in data quality assurance, text-mining for data augmentation and fraud detection. The financial technology will continue to affect the financial sector through requiring the adaption to new environment and new business models. Because of that, it could be expected that artificial intelligence will become part of the financial risk management framework. Contribution: This paper provides a review of artificial intelligence applications in market risk management, credit risk management and operational risk management. The paper identified the key AI techniques that could be used for financial risk management improvement because of financial industry transformation.

With the rapid development of modern information electronic technology, artificial intelligence information technology has gradually penetrated into all fields of human society. This paper aims to improve the clinical assistant nurses’ correct understanding of medical artificial intelligence, and to provide an important reference for further promoting the wide application and development of clinical artificial machine intelligence in the field of clinical nursing in China. In this paper, through the comparison of intelligent nursing and traditional nursing effect monitoring, as well as the analysis of medical staff and patients’ satisfaction with artificial intelligence treatment time, treatment effect and treatment scheme, and the results were discussed and analyzed. The problems that should be paid attention to in the application of artificial intelligence in clinical nursing were put forward, which provided guarantee for the development of clinical nursing. The research in this paper has important practical significance for the further development of the two.KeywordsIn the information ageArtificial intelligenceClinical nursingSmart devices

BackgroundResearch on technologies based on artificial intelligence in healthcare has increased during the last decade, with applications showing great potential in assisting and improving care. However, introducing these technologies into nursing can raise concerns related to data bias in the context of training algorithms and potential implications for certain populations. Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare. ObjectivesTo synthesize currently available state-of the-art research in artificial intelligence -based technologies applied in nursing practice. DesignScoping review MethodsPubMed, CINAHL, Web of Science and IEEE Xplore were searched for relevant articles with queries that combine names and terms related to nursing, artificial intelligence and machine learning methods. Included studies focused on developing or validating artificial intelligence -based technologies with a clear description of their impacts on nursing. We excluded non-experimental studies and research targeted at robotics, nursing management and technologies used in nursing research and education. ResultsA total of 7610 articles published between January 2010 and March 2021 were revealed, with 93 articles included in this review. Most studies explored the technology development (n = 55, 59.1%) and formation (testing) (n = 28, 30.1%) phases, followed by implementation (n = 9, 9.7%) and operational (n = 1, 1.1%) phases. The vast majority (73.1%) of studies provided evidence with a descriptive design (level VI) while only a small portion (4.3%) were randomised controlled trials (level II). The study aims, settings and methods were poorly described in the articles, and discussion of ethical considerations were lacking in 36.6% of studies. Additionally, one-third of papers (33.3%) were reported without the involvement of nurses. ConclusionsContemporary research on applications of artificial intelligence -based technologies in nursing mainly cover the earlier stages of technology development, leaving scarce evidence of the impact of these technologies and implementation aspects into practice. The content of research reported is varied. Therefore, guidelines on research reporting and implementing artificial intelligence -based technologies in nursing are needed. Furthermore, integrating basic knowledge of artificial intelligence -related technologies and their applications in nursing education is imperative, and interventions to increase the inclusion of nurses throughout the technology research and development process is needed.

Outline for a German Strategy for Artificial Intelligence

PurposeThis study explored the different artificial intelligence (AI) applications used in academic libraries and the key factors and impediments related to their implementation.Design/methodology/approachThe author applied quantitative research methods in the form of a questionnaire, using both open and closed questions. A total of 472 valid questionnaires were received from academic librarians.FindingsThe author sought responses from librarians who had implemented AI applications and those who had not, identifying the types of AI applications implemented, key factors relating to their implementation, and impediments to promoting AI. Gaps were identified between the level of support for AI applications and the negative effect of the impediments. Furthermore, the more extensive the individual and organizational knowledge activities performed by the librarians and libraries held, the more positive the attitude was librarians' attitude toward AI applications in their libraries. However, librarians recognized that AI applications are inevitable, but indicated that the difficulties of in execution have hampered the adoption of AI.Research limitations/implicationsThe sample data were collected in Taiwan; therefore, the data may only represent the views of Taiwanese academic librarians on AI applications. The results of this study may not apply to librarians worldwide; however, they may provide a useful reference.Practical implicationsThe results revealed the top four AI applications that libraries would most likely implement in the near future. Therefore, AI application developers and suppliers can prioritize the promotion of these products for to academic libraries. This study revealed that funding and costs related to AI implementation were discovered to be key factors relating to implementing AI applications. Some impediments to the implementation of AI applications relate to technological problems. Several librarians suggested that managers should invest more resources at an early stage rather than reducing cutting back on human resources initially. Although worries regarding privacy and ethics were mentioned expressed by some respondents, most academic librarians did not regard these to be major concerns.Originality/valueThis study provides the perspectives of librarians who have implemented AI applications and of those who have not. In addition, it explores the advantages and disadvantages of AI applications, and the level of support for and impact of AI applications and promotions. This study also included a gap analysis. Moreover, individual and organizational knowledge activity scales were adopted to examine AI awareness and the perceptions of academic librarians.