The Challenge of European AI Risk Management: An Iron Cage for Water?

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.

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.

This paper focuses on the prudential regulation and supervision of UK re-insurance undertakings, in relation to Artificial Intelligence (AI) considerations. Specifically, it presents a critical analysis of the prudential provisions of the European Artificial Intelligence (AI) Act which could be adjusted and adopted in the UK regulatory and supervisory regime, in line with the Prudential Regulation Authority (PRA)’s approach to insurance supervision. Building on the gaps identified regarding the supervisory approach to AI applications within the insurance value chain, it presents proposed developments based on the EU AI Act. The purpose of this paper is to present a critique on the learnings from the EU AI Act in relation to risk management systems and risk management for UK financial regulators regarding the prudential supervision of re-insurers. These are linked to the assessment performed by the European Insurance and Occupational Pensions Authority (EIOPA) in relation to the governance and risk management of AI to ensure the appropriate regulation and supervision of the risks linked to re-insurance activities. Effectively capturing how this approach towards the prudent AI governance and risk management framework could be adopted by the PRA, and ultimately how prudential supervision should be adjusted to monitor AI applications and uses. Beyond the EU AI Act, the principles from the International Association of Insurance Supervisors (IAIS) in relation to risk management systems from a prudential angle are also discussed to complement the recommendations for UK regulators, in relation to risk management practices and the prudential regulatory expectations based on the PRA’s Rulebook, in combination with the Lloyd’s of London Principles for the London market. The focus is placed on the AI considerations within the Own Risk and Solvency Assessment, model risk management and stress testing, all interlinked core prudential components of Solvency II and Delegated Acts. This doctrinal legal research adopts a socio-legal methodology combined with economic theory in analysing the prudential regulatory frameworks underpinning AI. The economic analysis of law and regulation constitutes the methodological approach adopted to critically examine the prudential provisions of the EU AI Act applicable to re-insurers. The contribution of this paper is twofold, providing insights for advances to the (a) regulation and (b) supervision of AI applications within the insurance sector for the UK, based on the EU AI Act and EIOPA’s approach. Regulating and supervising AI applications within the UK insurance industry is of high importance, linked to AI uses and the inherent purpose of insurance. In particular referring to the growth and capacity of the insurance market, with wider applications of AI, and the insurability of risks, with the case of under-insurance and protection gap, towards affordability via increased accuracy of risks and improved underwriting, both outcomes of prudential activities. Overall, this research adds to the growing literature about regulatory implications from AI, using the UK insurance industry as a case study, commenting on the EU regulatory regime, from a prudential lens, on how this could be utilised to shape UK practice and policy.

This study investigates the predictive roles of customer-centric and risk management artificial intelligence (AI) applications in optimizing banking operations in Nigeria. The objective was to determine how AI-driven solutions enhance operational efficiency, service quality, and regulatory compliance within the Nigerian banking sector. A predictive research design was adopted, using multiple regression analysis to examine the influence of AI applications, including automation, data-driven decision-making, customer-focused innovations, and risk/compliance management, on banking efficiency and process optimization. Data were collected from 444 purposively sampled banking professionals across top-tier commercial banks in Nigeria, using a validated Likert-scale questionnaire. Findings reveal that customer-centric AI significantly improves service delivery, query resolution, and operational responsiveness (β = 0.200, p = 0.002), while AI in risk and compliance management strengthens fraud detection, transparency, and regulatory adherence (β = 0.169, p = 0.013). Both applications demonstrate statistically significant predictive roles in enhancing operational performance, customer satisfaction, and process optimization. Based on these results, it is recommended that Nigerian banks invest in AI infrastructure, provide staff training, implement ethical AI practices, and encourage regulatory support to maximize AI’s predictive potential for sustainable operational excellence and improved customer trust.

Focus issue: Artificial intelligence in medical physics.

This paper explores the intersection of artificial intelligence (AI) and the financial sector, showcasing their transformative synergy. The integration of AI into finance has led to pioneering advancements like robo-advisors and AI-driven risk assessment methods. These innovations reshape investment strategies and risk management, ushering in a new era of financial operations. The study's focal question examines how AI recalibrates investment management, risk assessment, and fraud prevention in finance. The paper comprises sections on AI's impact on investment management, risk assessment, and fraud detection, detailing how robo-advisors provide personalized portfolio recommendations, AI aids risk identification and management, and transaction surveillance benefits from AI-powered fraud detection. Ethical, regulatory, and accountability considerations are discussed, reflecting AI's transformative influence on traditional financial paradigms. The application of AI in transaction detection and its role in enhancing portfolio recommendations, risk management, and automated trading are examined. While AI holds potential, its limitations such as data quality, model risks, and ethical concerns must be addressed. Regulatory oversight is crucial to ensure responsible AI implementation, fostering a balance between technological progress and financial stability. This paper underscores the intricate relationship between AI and finance, portraying AI's capacity to reshape the financial landscape and drive innovation

Outline for a German Strategy for Artificial Intelligence

As risk analysis and risk management get increasingly caught up in political debates, a new way of looking at and defining the risks of modern technologies becomes necessary

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.

Purpose – The purpose of this paper is to give an overview of current risk management approaches and outline their commonalities and differences, evaluate current risk management approaches regarding their capability of supporting cost-efficient decisions without unnecessary security trade-offs, outline current fundamental problems in risk management based on industrial feedback and academic literature and provide potential solutions and research directions to address the identified problems. Despite decades of research, the information security risk management domain still faces numerous challenges which hinder risk managers to come up with sound risk management results. Design/methodology/approach – To identify the challenges in information security risk management, existing approaches are compared against each other, and as a result, an abstracted methodology is derived to align the problem and solution identification to its generic phases. The challenges have been identified based on literature surveys and industry feedback. Findings – As common problems at implementing information security risk management approaches, we identified the fields of asset and countermeasure inventory, asset value assignment, risk prediction, the overconfidence effect, knowledge sharing and risk vs. cost trade-offs. The reviewed risk management approaches do not explicitly provide mechanisms to support decision makers in making an appropriate risk versus cost trade-offs, but we identified academic approaches which fulfill this need. Originality/value – The paper provides a reference point for professionals and researchers by summing up the current challenges in the field of information security risk management. Therefore, the findings enable researchers to focus their work on the identified real-world challenges and thereby contribute to advance the information security risk management domain in a structured way. Practitioners can use the research results to identify common weaknesses and potential solutions in information security risk management programs.

Response to M. Trengove & coll regarding "Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine".

Technological progress, especially in the field of information, has led to reducing the gap between science, information and knowledge. This accelerating change has forced all educational institutions to comply with this new mission by using information technology in the teaching process, as artificial intelligence has become an integral part of the educational and learning process, and a means of Effective in the teaching process, in addition to being research tools in obtaining information and knowledge, the research problem focused on (what are the attitudes of the kindergarten teacher towards using artificial intelligence applications in the effective teaching process in the early education stage in the third Rusafa Education Directorate?), where the importance of The research is considered one of the researches that deals with an important and vital topic (artificial intelligence), as it gains its importance in that it represents the role that kindergarten teachers play in the early education stage in developing the skills and abilities and refining the information of the child through their use of artificial intelligence methods and applications, as well as The positive role of such research in stimulating kindergarten teachers’ motivation to benefit from artificial intelligence applications, which increases the efficiency and effectiveness of their teaching. The aim of the research is to reveal kindergarten teachers’ attitudes towards using artificial intelligence in the effective teaching process in the early education stage, as well as to reveal the most important The challenges facing kindergarten teachers using artificial intelligence in the effective teaching process, and are there any statistically significant differences between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables? The research reached several conclusions, perhaps the most important of which is the existence of differences. There is statistical significance between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables. The research recommends holding many workshops and training courses for kindergarten teachers on how to use artificial intelligence in effective teaching, and including programs and rehabilitation plans for kindergarten teachers. About the use of artificial intelligence according to the plans of the Ministry of Education, which are consistent with its vision, and encouraging teachers to proceed and benefit from applications and websites for safe and available artificial intelligence applications in the teaching process

The construction industry is undergoing a significant transformation with the integration of Artificial Intelligence (AI). The growth of the construction industry is severely limited by the myriad complex challenges it faces such as cost and time overruns, health and safety, productivity and labor shortages. Also, construction industry is one the least digitized industries in the world, which has made it difficult for it to tackle the problems it currently faces. An advanced digital technology, Artificial Intelligence (AI), is currently revolutionizing industries such as manufacturing, retail, and telecommunications. The subfields of AI such as machine learning, knowledge-based systems, computer vision, robotics and optimization have successfully been applied in other industries to achieve increased profitability, efficiency, safety and security. While acknowledging the benefits of AI applications, numerous challenges which are relevant to AI still exist in the construction industry. This study aims to unravel AI applications, examine AI techniques being used and identify opportunities and challenges for AI applications in the construction industry. A critical review of available literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimization was conducted. Furthermore, the opportunities and challenges of AI applications in construction were identified and presented in this study. This paper explores the applications of AI in construction, highlighting its potential to improve efficiency, productivity, and safety. We discuss AI-powered technologies such as Building Information Modeling (BIM), predictive analytics, machine learning, and robotics. Case studies demonstrate the benefits of AI adoption, including reduced costs, enhanced decision-making, and improved project outcomes. This study provides insights into key AI applications as it applies to construction-specific challenges, as well as the pathway to realize the accruable benefits of AI in the construction industry.

This paper presents a comprehensive review of the application and impact of Artificial Intelligence (AI) in risk management within the banking sectors of the United States and Nigeria, emphasizing a comparative analysis. The objective is to assess how AI technologies are adopted and implemented in risk management practices across these diverse banking environments, identifying the benefits achieved and the challenges faced. The review synthesizes existing literature, including case studies, industry reports, and academic research, to outline the current state of AI in risk management. It delves into various risk types such as credit, market, operational, and compliance risks, exploring the specific AI tools and techniques employed to address these risks in each country. Key findings suggest that U.S. banks have a more mature implementation of AI in risk management, characterized by the adoption of advanced analytics, machine learning models, and natural language processing for enhanced decision-making, fraud detection, and compliance monitoring. In contrast, the Nigerian banking sector is at a nascent stage of AI adoption, with efforts hampered by challenges like inadequate technological infrastructure, regulatory hurdles, and a lack of skilled professionals in AI. Despite these differences, the paper identifies a strong interest and potential for growth in AI applications within the Nigerian banking sector, spurred by an increasing recognition of AI's value in enhancing competitiveness and meeting regulatory demands. Conclusively, the review underscores the critical role of supportive regulatory policies, investment in technological infrastructure, and capacity building in human capital as pivotal elements for fostering the effective integration of AI in risk management. The comparative analysis reveals both the disparities and potential areas of collaboration between the U.S. and Nigerian banking sectors, advocating for a global dialogue on best practices and strategies for AI adoption in risk management.