Journal of Next-Generation Research 5.0
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Formal Multi-Agent AI System Architecture for Regulated Insurers
Full text linkThis paper proposes a formal multi-agent architecture for implementing enterprise AI in regulated insurance firms,integrating economic theory with institutional design. The framework synthesises three core theoretical perspectives:Arrow’s risk pooling theory to formalise risk transformation under uncertainty, Nash equilibrium to model strategicinteractions between decision agents, and Principal-Agent theory to address incentive alignment under informationasymmetry. The insurer is modelled as a constrained optimisation entity operating under solvency, legal, ESG, andoperational boundaries, with specific focus on the regulatory contexts of Austria and Germany. The architecturedecomposes the firm into multiple specialised agents—each representing distinct functional domains such as capitalmanagement, underwriting, claims processing, compliance, fraud detection, and client interaction. Human-in-theloop agents are integrated through a tiered access control system, ensuring differentiated data visibility and decisioninfluence based on user roles. An orchestrator agent supervises inter-agent coordination, enforcing regulatory admissibility and institutional coherence under frameworks such as Solvency II, the AI Act, and the Insurance DistributionDirective. Protocol integration is based on asynchronous execution and dual-layer communication infrastructures,specifically the Model Context Protocol (MCP) and Agent-to-Agent (A2A) messaging. This structure enables thesystematic design of compliant, auditable multi-agent systems aligned with the institutional logic of financial firmsin Austria and Germany
Empirical Analysis of NIS2 Adoption in EU SMEs: Challenges for Critical Infrastructure in Germany
Full text linkThis research investigates the implementation of the NIS2 Directive in small and medium-sized enterprises (SMEs) categorised as part of critical infrastructure in Germany. The study examines regulatory requirements, compliance challenges, and the practical implications of cybersecurity obligations under NIS2, with particular emphasis on SMEs’ resource limitations and sector-specific vulnerabilities. A mixed-method approach was utilised, integrating qualitative analysis of legal frameworks, academic literature, and policy guidelines with quantitative survey data from SMEs operating in critical sectors. This methodological design facilitates a comprehensive assessment of both regulatory demands and real-world compliance barriers. The findings indicate that SMEs encounter substantial challenges in interpreting and implementing NIS2 requirements, with compliance scores exhibiting variation across company size and industry sector. While larger SMEs in telecommunications and energy demonstrate moderate preparedness (mean score 72.3), smaller enterprises in service-based sectors manifest lower compliance levels (mean score 48.5). Principal obstacles comprise financial constraints, limited cybersecurity expertise, and the complexity of mandatory risk management and reporting obligations. The study elucidates the disproportionate burden that NIS2 imposes on SMEs in comparison to larger enterprises. The absence of tailored cybersecurity frameworks and financial support mechanisms exacerbates compliance challenges, particularly in resource-limited sectors. Incident reporting obligations and supply chain security requirements introduce additional administrative and operational encumbrances, necessitating sector-specific guidance and targeted assistance. Ensuring SME compliance with NIS2 necessitates regulatory modifications, financial incentives, and pragmatic support measures. Policy recommendations encompass simplified compliance frameworks, government- supported cybersecurity advisory services, and enhanced funding for SME cybersecurity initiatives. The development of sector-specific guidelines, AI-driven compliance tools, and targeted training programmes could reduce administrative burdens while enhancing cybersecurity resilience. A risk-based approach, aligned with SMEs’ operational realities, is imperative to balance cybersecurity resilience with economic viability
The Role of Agile Digital Marketing in Achieving Marketing Excellence: A Study of Cellular Companies Customers in Syria
Full text linkThis study presents a field study based on the descriptive analytical approach, to determine the extent of achieving marketing excellence in cellular communications companies based on the application of agile digital marketing from the point of view of its customers to determine the dimensions of agile digital marketing that were positively reflected on customers to achieve a service that suits customers, by distributing (310) questionnaires to Cellular companies customers that were distributed to the company\u27s customers via the WhatsApp application, and aims to determine the role of agile digital marketing in achieving marketing excellence and its dimensions that have an important impact on that. The data were analyzed using the SPSS statistical program by performing a multiple regression test to achieve the research objectives. This study was enhanced by relying on previous studies on the subject so that the research completes what previous studies started to contribute to the academic and practical understanding of agile digital marketing in cellular telecommunications companies within an environment of uncertainty in which companies suffer from environmental threats as a result of emergency conditions of fluctuations in the surrounding environment. It sheds light on the dimensions of agile marketing that were positively and effectively reflected on the customers of the company under study, which may be the most appropriate solution for the company to continue and provide a service that satisfies the customer within the environment of uncertainty surrounding it
Generation Z: AI Affinity and Adoption in Competitive German Organisations
Full text linkGeneration Z, having matured in an entirely digital environment, plays a central role in the adoption of AI within organisations. AI presents potential advantages such as enhanced productivity, process optimisation, and novel employment sectors, while simultaneously posing risks including job displacement, algorithmic biases, and ethical dilemmas. This paper examines the opportunities and challenges associated with this development. The study incorporates a literature review and two surveys conducted among LinkedIn users across diverse industries to assess the role of Generation Z in AI implementation and the relevance of AI-based systems for competitiveness. Data was collected over a seven-day period in December 2024. The first survey, comprising 202 participants (n = 202), focused on the role of Generation Z in the integration and use of AI in companies. The second survey, involving 345 respondents (n = 345), explored whether companies can remain competitive in the next three to five years without the use of AI-supported systems. A target function was developed to formalise business success in the context of AI integration, considering key factors such as technology acceptance, training intensity, and workplace design. The findings indicate that 58.42% of respondents consider Generation Z as central contributors to the integration of AI in organisations. A total of 69.57% of respondents indicated that they believe German companies can maintain their competitiveness without AI, whereas 30.43% regarded AI-supported systems as critical for maintaining competitiveness. While Generation Z exhibits a high level of technological affinity, older generations demonstrate a more cautious approach to adoption. The target function elucidates that business success is contingent upon a balance between technology acceptance and supportive measures such as training and transparent system design. The results indicate that Generation Z plays an important role in AI implementation within organisations. To address social and psychological concerns, such as job insecurity and cognitive strain, companies should adopt structured training, mentoring programmes, and change management measures to support responsible integration. The formal model implies that flexible workplace design and an organisational culture that support innovation contribute to successful AI implementation
Exploring the Fusion of SAP S/4HANA and Machine Learning for Intelligent Financial Operations
Full text linkThe article analyses combining SAP S/4HANA with machine learning to automate and evaluate real-time data and enhance decision-making in financial processes. SAP S/4HANA, a complex ERP system, uses machine learning technologies. These mathematical models attempt to change financial operations by improving forecasts, identifying irregularities, and automating transactions. Artificial Intelligence-driven automation has improved financial forecast accuracy, employee involvement reduction, and fraud detection. Management teams at the bank put financial systems in place that made transactions flow better and helped them make smart choices, saving money and time. The system works at top quality despite the Cloud service provider’s changes in capacity to match demand. The system integration leads to better financial tracking while managing resources effectively and generating useful analysis from data. The future project plan includes strengthening database administration technologies, making AI modules available in SAP S/4HANA, and developing advanced models to identify problems. Future research will check system interconnection and data control methods to help enhance financial processes. The paper examines how joining SAP S/4HANA with machine learning creates fresh ways to automate difficult work and generate better financial operation forecasts. The article minimized workflow slowdowns while improving both financial decision-output times and how spending proceeds are tracked. By analysing financial data efficiently, the system supported financial corporations to monitor resources better while dodging errors. By including AI and Cloud technologies, the system gained proper scalability and used resources effectively as businesses grew without hurting system speed
Emotional Drivers of Financial Decision-Making: Unveiling the Link Between Emotions and Stock Market Behavior (Part 3)
Full text linkThis study is the third part of a research project involving analyzing written documents to assess emotional charges. Those documents have been written following a three-day trading experience. The first part focused on the textual analysis of these documents using several measurement scales. The second part consisted of a comparative analysis of the results using three Artificial Intelligences (ChatGPT, Gemini, and DeepSeek), working through several queries designed to clarify the emotional fields under analysis as well as the general context that led to the documents being written. Our results demonstrated the ability of Artificial Intelligence to identify a general emotional context but revealed difficulties in approaching some emotional nuances. In this third study, we refined the queries addressed to the three Artificial Intelligences, particularly about the emotions to be taken into consideration, making a distinction between positive anticipation and negative anticipation, as well as a distinction between a good surprise and a bad surprise. The emotional typology used prevented us from considering how the two aspects could differ. Finally, the Artificial Intelligences were informed that a reward would be granted to the best-performing portfolio at the end of the experiment. Our results show that DeepSeek gives great importance to this parameter and generates many positive emotions, which neither ChatGPT nor Gemini do. Our results once again demonstrate a consistent ability of Artificial Intelligence to clarify the prevailing general emotional context, but difficulties in identifying consistently and uniformly the emotional nuances associated with the experiment. Concluding these three articles, it seems clear that Artificial Intelligences could be used to begin the process of understanding qualitative data, but it must be complemented by a human dimension in order to capture the emotional nuances that Artificial Intelligences analysis cannot
Analysis of Fraud Detection Solutions Using Machine Learning (DSR Approach)
Full text linkThe research aims to identify the problems and details of fraud detection methods in bank transactions using machine algorithms and to provide solutions in this field. Qualitative research is conducted for this aim, and the main problems are identified by reviewing previous research. Then, solutions are presented using the Design Science Research Methodology (DSR). The main topics identified from previous research include Data limitation, labeled data, Discovering new fraud patterns, Bias and Costs, and responsibility for false prediction. The proposed research model has been designed using results from previous studies and experts\u27 opinions in this field. Using both supervised and unsupervised algorithms in the transaction registration process, labeling data based on discovered patterns, obtaining customer confirmation in cases where the system detects fraud, training, and continuous improvement of learning models using the generated data online are among the solutions of the suggested model. Also, it is suggested that the issue of reducing the error of false harmful data in the fraud detection process be investigated in future research
Artificial Intelligence in the Context of the Labour Market
Full text linkThe presented research results show that the impact of artificial intelligence (AI) on the labour market is multi-dimensional: on the one hand, it automates and displaces some professions (lawyers, analysts, financial specialists), on the other - it creates new positions in the space of technology development, robotics, data analysis. This phenomenon may lead to increased social and property antagonisms in the future. Important challenges concern the digital education of employees and the need to introduce appropriate ethical and legal regulations. Own research conducted in the Piła district confirms that artificial intelligence is most often perceived as a tool for a personal assistant, as well as in the form of document analysis and offer personalization. At the same time, significant gaps in the knowledge of AI functioning mechanisms among employees were demonstrated - education and training in this area could accelerate the teaching process. Among the organizational and ethical problems, the most frequently indicated were the security of using AI, privacy protection, and transparency
A Day in the Life of an MSL Powered by AI: Combining AI Technologies to Transform Training
Full text linkThe pharmaceutical industry is at a pivotal moment, where Medical Science Liaisons (MSLs) must navigate increasingly complex scientific landscapes and deliver precise, timely insights to healthcare professionals. Traditional training approaches – particularly standard e-Learning modules – fall short in equipping MSLs to meet these evolving demands. This review introduces a revolutionary concept: an AI-powered training platform that reimagines MSL development through the seamless integration of multiple advanced technologies. Using a “day in the life” narrative, the proposed platform demonstrates how the convergence of Generative AI, Retrieval-Augmented Generation, Multimodal AI, and AI Agents creates a dynamic learning ecosystem tailored to individual MSL needs. Blockchain technology ensures secure progress tracking, Federated Learning enables privacy-compliant, regionalized training delivery, while Explainable AI fosters trust through transparent, AI-driven recommendations. This analysis highlights the platform’s ability to address fundamental limitations in current training methodologies by offering MSLs personalized learning pathways, real-time decision support, and interactive engagement tools. Furthermore, the scalability of this innovative approach is explored, extending its potential to other pharmaceutical roles, such as sales representatives and compliance professionals. By redefining professional development, this conceptual platform provides a blueprint for leveraging AI to transform training in life sciences
Regulatory and Compliance Requirements for SMEs Operating AI Systems through Data Centers in the EU, with a Focus on Data Protection Challenges in Germany
Full text linkThis research examines the regulatory challenges encountered by small and medium-sized enterprises (SMEs) operating artificial intelligence (AI) systems through data centres in the European Union (EU), with a particular focus on data protection issues in Germany. The study analyses the interaction between the General Data Protection Regulation (GDPR) and the proposed EU AI Act, emphasising the compliance barriers faced by SMEs. Methods: A mixed-method approach was employed, combining qualitative analysis of regulatory frameworks and scholarly literature with quantitative survey data from SMEs across key industries. This methodology ensured a comprehensive examination of both regulatory requirements and their practical implications. The findings indicate that SMEs demonstrate high familiarity with GDPR (mean score 82.24) but lower awareness of the AI Act (mean score 56.24), with significant intersectoral variation. Challenges include resource limitations, ambiguous ”high-risk” AI classifications, and the complexity of dual compliance. Notably, government and healthcare sectors reported substantial regulatory burdens, while energy and finance sectors exhibited lower preparedness for AI Act requirements. The study reveals the fragmented implementation of GDPR across member states, complicating compliance for cross-border SMEs. The dual demands of GDPR and the AI Act necessitate streamlined regulatory processes and tailored support mechanisms, such as simplified guidelines and financial assistance. Explainability and transparency obligations, while essential for trust, introduce additional administrative burdens that may impede innovation. Harmonising GDPR and AI Act requirements is crucial to enabling SMEs to comply without inhibiting innovation. Policy recommendations include regulatory sandboxes, targeted training, and increased financial support for SMEs to foster legally compliant yet innovative AI applications