Journal of Next-Generation Research 5.0
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Generative AI Unlocking Adaptive Workflow Design
Full text linkThis paper introduces a novel application of generative AI models to enterprise workflow automation, emphasizing adaptive process design and continuous improvement. By utilizing transformer-based models like GPT for real-time decision-making, the framework empowers workflows to self-optimize based on operational data and evolving business needs. The proposed system integrates Robotic Process Automation (RPA) with generative AI to dynamically suggest process improvements, reducing design time and human intervention. A case study in the e-commerce sector showcases the system\u27s ability to adapt order fulfillment workflows, achieving a 35% reduction in processing time while enhancing customer satisfaction. This research establishes generative AI as a transformative tool for intelligent and adaptive workflow automation, offering unprecedented flexibility and efficiency in enterprise environments
Evaluation of AI-Driven Learning Strategies and Business Innovation for SDG Dissemination in Meta Colombia
Full text linkThe problem encountered is the deficient knowledge about the Sustainable Development Goals (SDG) adopted by the United Nations as part of the 2030 agenda in Mesetas and Lejanías, in Meta, Colombia. The problem was solved by sharing at the local level a learning strategy with Artificial Intelligence (AI) and emerging technologies for sustainable innovation with the participation of undergraduate students levels 10 and 11, small business agricultural entrepreneurs and rural producers from the localities of study, useful for the dissemination of the SDGs in the rural sector of the Ariari in Colombia using as a model a successful sustainable business that obtained inherent results to the evaluation of the effectiveness of techniques for the dissemination of knowledge, attitudes and practices as well as metacognitive strategies and AI at the local level useful for the dissemination of the SDGs in Meta Colombia. The data obtained in the research imply effective support strategies for self-assessment and practice for consolidation of learning about the SDG for rural communities using AI. The findings show that AI-powered systems increase learner motivation, engagement, and knowledge retention while providing scalable solutions for various educational scenarios. Limitations include the scope of the implementation and the necessity for additional quantitative study. The paper continues by identifying areas for further research and practical implementation tactics and establishing significant contribution from AI in rural education about the SDGs
Insider Trading Challenges in the Digital Era: Legal and Ethical Considerations for U.S. Financial Market Regulation
Full text linkDigital technologies have profoundly transformed financial markets, introducing both opportunities and challenges for regulatory frameworks. This paper critically explores the complexities of regulating insider trading in the U.S. financial markets, focusing on emerging technologies such as blockchain, smart contracts, and artificial intelligence. While these innovations promise enhanced transparency and efficiency, they simultaneously obscure accountability, complicate enforcement, and expand the interpretative scope of insider trading laws. The transition to decentralized platforms and automated trading systems has disrupted jurisdictional boundaries and amplified regulatory gaps. This study examines these disruptions through the lens of key legal principles and case law, highlighting the urgent need for adaptive legal reforms to maintain market integrity in the digital age
The Evolution of Digital Financial Architecture: Artificial Intelligence-Driven Agility and Scalability in Enterprise Solutions & Customer Excellence
Full text linkDigital Financial Architecture (DFA) has been invented to alter enterprise financial systems that offer agility, scalability, and operational efficiency through advanced technologies like Cloud Computing, Artificial Intelligence (AI), Blockchain, and API-driven platforms. The fact that this transformation has brought together traditional and rigid financial structures with modular and decentralized platforms to create the often real-time decision-making and compliance. Empirical evidence reveals that digital payment is positively related to financial inclusion, and 0.018% of the operational costs will be decreased with a 1% increase in digital transactions. Practices of use of AI and Cloud Computing have reduced the time that is required for making decisions while DevOps practice has decreased the time required for development cycles and deployment efficiency. It enhances the protection of transactions and supports the development of big platforms in finance while allowing data openness. By connecting ESB with EA, users experience better interconnection between systems while making their operations expandable. Numbers show exactly how dynamic resource management works and performance results from our continuous delivery methods. They will look at ways to broaden the previous systems, investigate the security issues surrounding data dissemination, and explore merging technology types with modern digital banking networks
AI-Powered Financial Digital Twins: The Next Frontier in Hyper-Personalized, Customer-Centric Financial Services
Full text linkThe financial services industry is going through a fundamental transformation as artificial intelligence (AI) converges with digital twin technology to create unprecedented capabilities in customer experience optimization. This paper presents a comprehensive examination of financial digital twins (FDTs) as smart virtual counterparts that can effectively replicate and predict customer financial behaviors in real-time. Unlike fundamental analytics approaches, FDTs incorporate multi-dimensional data streams, advanced behavioral modeling, and autonomous simulation capabilities to deliver hyper-personalized financial services at scale. We analyze the architectural foundations of enterprise-grade FDT implementations, detailing their five critical layers: data fabric, behavioral modeling, simulation environment, decision intelligence, and experience orchestration. Then, we discuss the need for sophisticated computational requirements including edge-AI hybrid architectures, quantized simulations, and confidential computing frameworks for secure, real-time financial twin operations at scale. Through an evolutionary analysis of deployment patterns across banking, wealth management, and insurance sectors, we demonstrate how FDTs have progressed from basic data mirrors to autonomous cognitive systems capable of anticipatory financial guardianship. The paper also provides an examination of ethical and regulatory considerations, proposing a robust algorithmic accountability framework that addresses bias auditing, explainability mandates, and human oversight protocols. Our analysis reveals that mature FDT implementations can simultaneously achieve 30-40% improvements in customer experience metrics while reducing operational risk exposure. Looking ahead, we explore next-generation innovations including decentralized identity integration, and biometric behavioral models that will further transform financial services operations. The conclusion presents a strategic execution roadmap for financial institutions seeking to harness FDT technology while maintaining regulatory compliance and ethical standards
AI-Driven Threat Intelligence for Enterprise Cybersecurity
Full text linkThe research investigates how artificial intelligence technologies operate within enterprise cybersecurity frameworks by studying threat intelligence automation and advanced detection techniques. The research uses extensive literature analysis to show that machine learning algorithms achieve detection accuracies above 95% and deep learning approaches enhance F1-scores by up to 33% above traditional methods. Real-time data integration with behavioral analytics boosts threat identification abilities, allowing systems to detect 150,000 threats per minute and preventing 8 out of 10 attacks from causing system compromise. The current implementations primarily use centralized architectures, but distributed approaches show benefits for particular deployment situations. The research identifies essential challenges, which include privacy concerns, transparency limitations, algorithmic bias, data quality issues, and integration complexity. The research demonstrates that effective countermeasures against advanced threats require security innovations governed by comprehensive frameworks that balance technological capabilities with ethical considerations through continuous evaluation processes
Preventing Financial Fraud in the Public Sector: A Structural Approach Using the FAST™ Framework
Full text linkThe evolving complexity of public finance systems presents significant challenges in preventing financial fraud, ensuring compliance, and maintaining operational transparency. With cloud ERP adoption on the rise—especially SAP S/4HANA Public Cloud—there is an urgent need for structured, strategy-led frameworks that embed control, visibility, and audit readiness into financial architecture. This article introduces the FAST™ Framework as a structural model to enable fraud-resistant digital finance systems. By aligning architecture, governance, and intelligent automation, FAST™ empowers public sector organizations to proactively mitigate fraud and build scalable, secure finance operations
Emotional Drivers of Financial Decision-Making: Unveiling the Link between Emotions and Stock Market Behavior (Part 2)
Full text linkThis study is the second part of a three-part analysis (if it meets the review requirements) of emotions carried out by written documents. These documents were collected from eight students who took part in a three-day stock market experiment in January 2025. In the first part of this research (1), which was previously published, a lexical approach was used to analyze the words\u27 emotional weight in the passages of the written documents. In this study, we considered all the passages in order to analyze a single written document for each student. To analyze the emotional charges in the documents, three Artificial Intelligences (ChatGPT, Gemini, and DeepSeek) have been used, and six queries have been selected. Using emotional couples, our results suggest different analytical processes depending on the Artificial Intelligence requested and a lack of uniformity in the emotional couples generated, according to the queries selected. At least, Artificial Intelligences are able to identify a strong primary and basic emotional trend but seem to have trouble capturing more nuanced emotional levels in a consistent manner. Using multiple queries does not improve the consistency of the results, and, indeed, the most generic query leads to the most uniform results among the selected artificial intelligences
Empowering Remote Healthcare with On-Premises Solar-Powered AI Units: Design and Implementation
Full text linkRural healthcare systems face considerable obstacles such as unreliable electricity, limited internet access, and shortages of healthcare professionals, all of which impede timely medical documentation and diagnostics. This study aims to design and evaluate a solar-powered AI unit equipped with fine-tuned Large Language Models for remote clinics, enabling offline medical transcription, clinical note generation, and diagnostic support in regions with limited infrastructure. Employing a mixed-methods approach, the research combines qualitative user experience assessments with quantitative performance metrics. Four TinyLLaMA models with 1.1 billion parameters were fine-tuned to generate Subjective, Objective, Assessment, and Plan (SOAP) notes using a synthetic dataset comprising thousands of patient records and transcriptions. These models were deployed on a Raspberry Pi 5, powered by solar panels, batteries, and a Wi-Fi antenna. System performance was simulated using mockup data, with plans for validation through real-world deployment. The fine-tuned models achieved high transcription accuracy, rapid note generation, and substantial diagnostic precision on mockup data, with a balanced demographic distribution. Qualitative feedback emphasized usability while highlighting challenges such as setup costs and the need for digital literacy. The solar-powered design ensures reliable offline operation, consuming roughly 480Wh daily. These solar-powered AI units and fine-tuned models present a sustainable solution to enhance documentation and diagnostics in remote healthcare settings. Real-world trials are crucial to validate system performance, complemented by strategic investments in training, infrastructure, and ethical governance to support scalability. This work has resulted in two provisional patent applications, further advancing its potential for practical deployment
The Theory of AI-Powered Legal Transformation (AILT): A New Paradigm in Judicial Systems
Full text linkThis study investigates the transformational potential of artificial intelligence (AI) in legal systems by developing and empirically testing the AI-Powered Legal Transformation (AILT) theory. The study looks into how AI technologies, such as natural language processing (NLP), machine learning (ML), and AI-powered decision support systems, can improve operational efficiency, judicial correctness, and ethical safeguards in legal processes. The findings confirm the theory\u27s significant constructs: AI Capabilities, Operational Efficiency, Judicial Accuracy, Ethical Safeguards, Bias Mitigation, and human collaboration, using a qualitative study design that included semi-structured interviews, case studies, and document analysis. The findings reveal that AI dramatically increases efficiency by automating mundane operations and improving the accuracy of legal decisions through data-driven insights. However, the study underlines the significance of ethical safeguards and human monitoring in preventing biases and ensuring transparency in AI-driven judicial systems. While the study provides valuable information, it needs to be improved by its small sample size and emphasis on mature legal systems. Future studies should broaden its scope to cover other jurisdictions and investigate AI\u27s evolving position in legal education and policy. This research adds to the expanding knowledge of AI\u27s integration into law by proposing a theoretical framework for its responsible adoption