Research on Financial Field Integrating Artificial Intelligence: Application Basis, Case Analysis, and SVR Model-Based Overnight

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

The word “artificial intelligence” has appeared more and more frequently in our life in the past two years. More and more intelligent products have entered our ordinary people’s homes. The new technology represented by artificial intelligence technology has gradually become the key force driving China’s economic development. The integration of artificial intelligence and electronic information technology has become an inevitable trend, Electronic information engineering must be transformed, relying on artificial intelligence technology to walk in the forefront of upgrading and development. Based on the literature research method, After consulting a lot of literature about AI technology and electronic information engineering, and concludes that under the background of artificial AI technology, the scale of electronic information manufacturing industry is steadily expanding. It shows a steady growth trend. According to the big data platform, The trade volume of China’s electronic information industry is as high as 9.3 trillion, an increase of 11% Compared with the previous income scale, with the support of AI technology, the development of electronic information engineering will be even more powerful.

This study delves into the ethical implications of Artificial Intelligence (AI) in financial decision-making, exploring the transformative impact of AI technologies on the financial services sector. Through a comprehensive literature review, the research highlights the dual nature of AI's integration into finance, showcasing both its potential to enhance operational efficiency and decision accuracy and the ethical challenges it introduces. These challenges include concerns over data privacy, algorithmic bias, and the potential for systemic risks, underscoring the need for robust ethical frameworks and regulatory standards. The study emphasizes the importance of a multidisciplinary approach to AI development and deployment, advocating for collaboration among technologists, ethicists, policymakers, and end-users to ensure that AI technologies are aligned with societal values and ethical principles. Future directions for research are identified, focusing on the development of adaptive ethical guidelines, methodologies for embedding ethical principles into AI systems, and the investigation of AI's long-term impact on market dynamics and consumer behaviour. This research contributes valuable insights into the ethical integration of AI in finance, offering recommendations for ensuring that AI technologies are utilized in a manner that is both ethically sound and conducive to the advancement of the financial services industry. Keywords: Artificial Intelligence, Financial Decision-Making, Ethical Implications, Algorithmic Bias, Data Privacy, Regulatory Standards, Multidisciplinary Approach.

With the continuous progress of information technology, the integration of artificial intelligence and virtual simulation technologies into the teaching system is of great significance to promote the informatization reform of physics teaching and the cultivation of innovative talents. This paper first introduces the characteristics of the combination of artificial intelligence and virtual simulation technologies, then comprehensively analyzes the positive effects of the integration of artificial intelligence and virtual simulation technologies on physics teaching, and finally puts forward some thoughts and suggestions on the integration of artificial intelligence and virtual simulation technology into physics teaching.

The integration of Artificial Intelligence (AI) into the financial sector has catalyzed a transformative shift, enhancing operational efficiency, customer service, and risk management across the industry. This paper explores the evolving landscape of AI-driven financial innovation, focusing on contemporary trends, key challenges, and future opportunities. It reviews significant applications of AI, including algorithmic trading, personalized banking, fraud detection, and regulatory compliance. Emphasis is placed on how AI technologies are reshaping decision-making processes and the delivery of financial services. The analysis highlights the importance of addressing ethical concerns, such as data privacy and algorithmic bias, as well as technical hurdles like system integration and model interpretability. Furthermore, it underscores the role of collaboration between FinTech startups and traditional financial institutions in driving innovation. By synthesizing recent academic and industry research, this paper provides a comprehensive outlook on AI’s impact in finance and offers strategic insights for stakeholders aiming to leverage AI responsibly and effectively.

The integration of artificial intelligence (AI) within the financial market has ushered in an era of unprecedented innovation and disruption, redefining traditional paradigms and unveiling transformative opportunities. This study explores the multifaceted applications of AI in the financial sector, including algorithmic trading, risk management, fraud detection, and portfolio optimization. By analyzing cutting-edge advancements such as machine learning, natural language processing, and predictive analytics, the research highlights how AI enhances market efficiency, decision-making accuracy, and operational agility. Moreover, the paper delves into the challenges and ethical considerations surrounding AI adoption, including data privacy, regulatory compliance, and the potential for market destabilization. Drawing on empirical evidence and case studies, this work offers a comprehensive examination of the symbiotic relationship between AI technologies and financial systems, while proposing innovative frameworks to harness their full potential responsibly. By unraveling the transformative capabilities of AI, this article aims to provide valuable insights for academics, practitioners, and policymakers striving to navigate the rapidly evolving landscape of the financial market.

The integration of artificial intelligence (AI) in financial decision-making processes has significantly enhanced the efficiency and scope of services in the finance sector. However, the rapid adoption of AI technologies raises complex ethical questions that need thorough examination. This paper explores the ethical challenges posed by AI in finance, including issues related to bias and fairness, transparency and explainability, accountability, and privacy. These challenges are scrutinized within the framework of current regulatory and ethical guidelines such as the General Data Protection Regulation (GDPR) and the Fair Lending Laws in the United States. Despite these frameworks, gaps remain that could potentially compromise the equity and integrity of financial services. The paper proposes enhancements to existing ethical frameworks and introduces new recommendations for ensuring that AI technologies foster ethical financial practices. By emphasizing a proactive approach to ethical considerations, this study aims to contribute to the ongoing discourse on maintaining trust and integrity in AI-driven financial decisions, ultimately proposing a pathway towards more robust and ethical AI applications in finance.

In today's rapidly evolving business landscape, the integration of Artificial Intelligence (AI technology within Management Information Systems (MIS) has gained significant attention as a transformative approach to enhancing decision-making processes. The aim of this research was to assess the extent of integration of Artificial Intelligence(AI) technology within Management Information Systems (MIS) of County Governments in Kenya. The findings show that Artificial intelligence integration within MIS in County Governments in Kenya were partially Integrated. These findings are anticipated to contribute to the growing discourse surrounding the level of AI integration within MIS in enhancing decision-making processes, while offering practical recommendations for organizations seeking to harness the potential of these technologies to drive improved decision-making outcomes in the Kenyan context

The integration of artificial intelligence (AI) in rural healthcare systems holds great promise for improving access to quality medical care and addressing healthcare disparities. However, numerous barriers must be overcome to successfully implement AI technologies in these settings. This paper explores the key challenges and potential solutions for implementing AI in rural healthcare systems, focusing on infrastructure, data privacy, internet connectivity, and workforce readiness. One of the primary barriers is the lack of infrastructure in rural areas. Many rural healthcare facilities are under-resourced, lacking the necessary hardware, software, and technical support to adopt AI solutions. Addressing this issue requires significant investment in healthcare infrastructure, including upgrading existing facilities and ensuring they are equipped with modern technology. Data privacy and security present another significant challenge. AI systems rely on vast amounts of patient data to function effectively, raising concerns about data confidentiality and protection. Implementing robust cybersecurity measures and developing clear data governance policies are essential to safeguard patient information and build trust in AI technologies. Internet connectivity is a crucial enabler for AI-driven healthcare, yet rural areas often suffer from unreliable or limited internet access. Solutions to this challenge include investing in broadband infrastructure, exploring alternative connectivity options such as satellite internet, and leveraging offline AI capabilities that can operate with minimal connectivity. Workforce readiness and acceptance of AI technology are also critical for successful implementation. Many rural healthcare providers may lack the training and expertise needed to utilize AI tools effectively. Comprehensive training programs, ongoing education, and support from technology providers can help bridge this knowledge gap and encourage the adoption of AI. In conclusion, while the integration of AI in rural healthcare systems faces significant barriers, targeted solutions can address these challenges. Investment in infrastructure, robust data privacy measures, improved internet connectivity, and comprehensive training programs are essential steps to enable the successful implementation of AI in rural healthcare. By overcoming these barriers, AI has the potential to transform rural healthcare delivery, improve patient outcomes, and reduce healthcare disparities. Collaborative efforts among government entities, healthcare providers, and technology companies will be crucial in realizing this potential and ensuring equitable access to advanced healthcare technologies in rural areas.

The integration of artificial intelligence (AI) technologies into education systems has been a transformative force across multiple disciplines, with chemistry education proving particularly fertile ground for innovation. As we consider the prospects of AI in chemistry education, it is important to recognize the unique opportunities and challenges that this integration brings. AI technologies, ranging from machine learning algorithms to advanced simulation tools, offer unprecedented opportunities to improve the way chemistry is taught and learned. By introducing interactive content, personalized learning experiences, and improved visualization techniques, these technologies not only enable deeper understanding of complex chemical concepts but also transform traditional teaching methods. This article examines the current state of AI applications in chemistry education and highlights the significant benefits and potential future developments that could further transform this field. As AI continues to advance, its role in educational contexts, particularly in chemistry, promises to be both transformative and far-reaching, paving the way for more effective educational technologies. Keywords: chemistry, artificial intelligence, chemistry education, modern education trends, methods of teaching chemistry.

The article explores the role of digitalization in the development of public finance, highlighting its key advantages, challenges, and directions for implementing digital technologies in financial resource management. It is determined that digitalization is a key factor in enhancing the transparency, efficiency, and accountability of budgetary processes. The integration of big data analytics, artificial intelligence, and blockchain technologies contributes to process automation, improves revenue and expenditure forecasting, and enables the timely detection of deviations or violations.The study examines the implementation of digital platforms such as ProZorro and “Diia,” which have significantly increased the transparency and accountability of budget expenditures while strengthening public trust in government authorities. It has been established that expanding their functionality through the integration of artificial intelligence and automation of financial operations opens new opportunities for improving public finance management efficiency.At the same time, the digitalization process faces challenges, including the insufficient level of digital literacy among civil servants, uneven access to digital services across regions, high modernization costs for infrastructure, and the complexity of integrating new technologies with existing systems. Overcoming these barriers requires a systematic approach that includes developing digital infrastructure, reforming legislation, fostering financial technologies, and strengthening cybersecurity.The study analyzes international experience in the digitalization of public finance across various countries. Using China as an example, which demonstrates a high level of integration of information and communication technologies into financial management, it is shown that their application enhances transparency, reduces costs, detects deviations, improves imperative planning, and increases the accountability of public officials. Moreover, digitalization contributes to strengthening a country’s financial sovereignty. In particular, the use of automated control and monitoring systems significantly enhances the accountability of government agencies. China’s successful experience demonstrates the potential for effectively integrating digital technologies with public financial policy. For Ukraine, this opens up prospects for introducing a mixed economic system that combines market mechanisms with effective state regulation. The implementation of such approaches, coupled with profound social transformations, will contribute not only to the digitalization of public finance but also to strengthening social security and ensuring the long-term development of the country.The research findings have both theoretical and practical significance for the development of a national strategy for the digitalization of public finance. A set of measures is proposed to enhance public trust in government institutions, reduce financial losses, optimize expenditures, and improve Ukraine’s international competitiveness. The integration of digital technologies will not only enhance the transparency of budgetary processes but also ensure the long-term stability of the country’s financial system. Further research should focus on improving digital ecosystems, adapting international experience, and assessing the socio-economic impact of digitalization.

This study explores the transformative role of artificial intelligence (AI) in environmental health and public safety within the USA, focusing on pollution monitoring, emergency response, and sustainable practices for public. With the growing challenges posed by climate change, pollution, and emerging public health threats, the integration of Artificial Intelligence (AI) in environmental health and public safety strategies has become imperative. This comprehensive review explores the diverse array of AI applications implemented in the United States to address environmental issues and enhance public safety measures. The paper analyzes the multifaceted role of AI across various domains, including air and water quality monitoring, disease surveillance, disaster response, and infrastructure resilience. The advancements in AI technologies that have revolutionized data collection, analysis, and prediction in environmental health are examined. Machine learning algorithms, sensor networks, and satellite imagery are examined as tools for real-time monitoring and early detection of environmental hazards. Additionally, the paper investigates the integration of AI in public health surveillance systems, showcasing how predictive analytics and data-driven models contribute to the identification and containment of infectious diseases. Furthermore, the study sheds light on the incorporation of AI in disaster management, emphasizing the role of predictive modeling and risk assessment in optimizing emergency response strategies. The implementation of smart city technologies and intelligent infrastructure systems is discussed, highlighting how AI contributes to enhancing public safety and minimizing the impact of natural disasters. The review also critically evaluates the ethical, legal, and privacy considerations associated with the widespread adoption of AI in environmental health and public safety initiatives. It addresses concerns related to data security, algorithmic biases, and the need for transparent and accountable governance frameworks. Through an in-depth analysis of case studies, policies, and initiatives, this review provides insights into the successes and challenges of AI implementation in the USA. It concludes with recommendations for future research directions and policy considerations to ensure the responsible and effective integration of AI technologies in safeguarding environmental health and public safety. The findings presented in this review contribute to the broader discourse on leveraging AI for sustainable and resilient communities in the face of evolving environmental and public health challenges.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.