JDA Software (Canada)

The accelerating complexity of global regulatory frameworks, driven by rapid technological advancements, cross-border transactions, and shifting socio-economic priorities, has placed unprecedented demands on organizations to maintain continuous compliance. Traditional compliance management systems, often rule-based and manually updated, struggle to adapt to the dynamic and fragmented nature of these evolving landscapes. This paper proposes the design of intelligent compliance systems that leverage artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to automate regulatory monitoring, interpretation, and enforcement. By integrating real-time data streams from multiple jurisdictions, the proposed system employs semantic analysis to extract, classify, and map regulatory requirements to organizational policies, operational processes, and risk controls. A modular architecture is developed to ensure scalability, interoperability, and adaptability, enabling sector-specific customization and rapid incorporation of regulatory changes. The system incorporates predictive analytics to forecast regulatory trends, simulate compliance scenarios, and recommend proactive adjustments, thereby transforming compliance from a reactive obligation into a strategic advantage. Furthermore, explainable AI techniques are embedded to enhance transparency and trust, ensuring that automated decisions align with both legal mandates and ethical standards. Case studies across finance, healthcare, and energy sectors illustrate how intelligent compliance systems reduce operational risk, lower compliance costs, and improve audit readiness. The research underscores the importance of harmonizing technological innovation with robust governance frameworks to mitigate algorithmic bias, protect sensitive data, and meet jurisdiction-specific legal obligations such as GDPR, CCPA, and sectoral regulations. This work concludes that intelligent compliance systems represent a paradigm shift, enabling organizations to navigate the evolving global regulatory landscape with agility, accuracy, and strategic foresight, while fostering regulatory harmonization and operational resilience in an increasingly interconnected world. Keywords: Intelligent Compliance Systems, Artificial Intelligence, Machine Learning, Regulatory Technology, RegTech, Global Regulations, Natural Language Processing, Predictive Analytics, Explainable AI, Compliance Automation, Governance Frameworks, Operational Resilience, Risk Management, Legal Technology, Regulatory Harmonization.

The rapid evolution of digital ecosystems—characterized by multi-cloud infrastructures, IoT proliferation, and distributed data flows—has fundamentally altered the governance, risk, and compliance (GRC) landscape. Traditional GRC frameworks, rooted in periodic audits and reactive controls, are increasingly inadequate for addressing the scale, speed, and sophistication of modern cyber threats. This review paper examines the emergence of AI-driven continuous compliance and threat intelligence models as adaptive solutions for managing GRC in complex digital environments. It synthesizes existing literature on regulatory mapping, continuous auditing, and real-time threat intelligence integration, while identifying key limitations of siloed, manual approaches. The study highlights how artificial intelligence and machine learning can enable proactive risk identification, predictive analytics, and automated remediation, transforming GRC into a continuous and intelligent function. Furthermore, the paper explores technical methodologies such as natural language processing for regulatory interpretation, anomaly detection algorithms for compliance monitoring, and predictive modeling for risk forecasting. By analyzing current advancements, challenges, and research gaps, this review proposes a conceptual framework that positions AI as a catalyst for adaptive, resilient, and future-ready GRC architectures. The findings underscore the critical need for intelligent, real-time governance models to ensure organizational sustainability in the face of regulatory volatility and cyber risk escalation. Keywords: Continuous Compliance, Threat Intelligence, Adaptive GRC, Artificial Intelligence in Governance, Predictive Risk Analysis, Automated Remediation.

Since the application of entropy in financial economics has been growing extensively as a measure of volatility, in portfolio selection and to detect anomalies in markets. It’s really complicated to establish that increase in entropy is a source of the useful information for the financial markets that tantamount to mitigate risk, or it is in fact an indicator of disorder reflecting the growing risk scenario in the financial market. To explore the more effective application of entropy in the field of financial economics, this study evaluates entropy in both contexts, as a source of information to mitigate risk and as an indicator of disorder reflecting volatility. Twelve years daily data of 29 financial assets have been used to measure the intrinsic entropy in addition to other eight volatility estimators and three GARCH models-based volatilities. Various assessment techniques are used to test the role of entropy in both contexts including, Run Test, Mean, Variance and Coefficients of Variation, Mean Squared Errors, Proportional Bias and Efficiency Estimator, in addition to spearman rank-order correlation. Results emphasis that entropy is more suitable as a volatility measure rather a source of information in the financial market.

Artificial intelligence (AI)-enabled technologies in the MedTech sector hold the promise to transform healthcare delivery by improving access, quality, and outcomes. As the regulatory contours of these technologies are being defined, there is a notable lack of literature on the key stakeholders such as the organizations and interest groups that have a significant input in shaping the regulatory framework. This article explores the perspectives and contributions of these stakeholders in shaping the regulatory paradigm of AI-enabled medical technologies. The formation of an AI regulatory framework requires the convergence of ethical, regulatory, technical, societal, and practical considerations. These multiple perspectives contribute to the various dimensions of an evolving regulatory paradigm. From the global governance guidelines set by the World Health Organization (WHO) to national regulations, the article sheds light not just on these multiple perspectives but also on their interconnectedness in shaping the regulatory landscape of AI.

Engagement, or rather lack thereof has become a major issue because of its negative impact on productivity. Recently, gamification has successfully been implemented into corporate technological interfaces to increase engagement of employees. This paper proposes a theory-driven experiment that examines the impact a gamified interface has on engagement and performance of workers in a warehouse-management task. Specifically, the experiment proposed in this paper compares how the integration of two different types of goal-setting (self-set goals or assigned goals) into a warehouse-employee interface will affect engagement and performance.

In order to achieve effective support for software development, the transition between an informal and provisional mode of tool operation, which is conducive to design exploration, and a formal mechanistic mode required for computer-based design capture is crucial. This contribution proposes a smooth transition for designing class models starting from informal, sketchy object models. We propose a lenient development approach and discuss the possibilities and problems of a transformation from object diagrams to class diagrams. While classes describe abstract concepts, objects are representations of what can be seen in the real world, so it might be easier to start modeling with objects instead of classes. An object diagram can however not describe a whole system, it is only used as the first step of an iterative process to create a complete model. During this process, our object and class diagrams provide a notation for highlighting missing or conflicting parts. Based on these imperfect object diagrams, educated guesses can be made for resulting, imperfect class diagrams, which can then be refined to a complete, formal description of the modeled system.

Service compositions are a means to create value-added services for service provider, as well as for service requester. From the consumer-centric point of view services that meet the predefined preferences of service requester add both economic value and satisfaction. In order to achieve consumer satisfaction in a market place it is necessary for service providers to design fair-algorithms for service composition and ranking of services, so that consumers will have less difficulty in selecting their most preferred services without much difficulty. This consumer-oriented approach has the potential to add value-added trust for business, leading to profitable business and healthier consumer relation. Motivated by this theme this paper proposes a fair ranking algorithm for composite services.

(Scimago Q1, ATIEF A+)

The Ada language has for long provided support for the development of reliable real-time systems, with a model of computation amenable for real-time analysis. To complement the already existent multiprocessor support in the language, an ongoing effort is underway to extend Ada with a fine-grained parallel programming model also suitable for real-time systems. This paper overviews the model which is being proposed, pointing out the main issues still open and road ahead.

This chapter examines eleven technology issues faced by human rights organizations. These issues are critical if they are to use technology effectively. Although solutions are not proposed, the chapter highlights topics that increasingly require research and concrete solutions in order for human rights organizations to integrate technology tools into their work. The key issues addressed are: rapid technology changes; national security, personal information, and human rights; web security; data relationships; controlled vocabulary (a common language); Semantic Web and human interactivity; search - finding the information; assumptions and ease of use; trust; open source; and social media. These issues, chosen from the human rights and technology backgrounds of the authors, provide a frontier to human rights work that may seem daunting to the newly initiated. Human interaction must remain the key element. Technology must be a servant to the human rights needs and demands.