The American Journal of Analytics and Artificial Intelligence (AJAAI)
The American Journal of Analytics and Artificial Intelligence (AJAAI) is a peer-reviewed, open-access academic journal dedicated to advancing research in analytics, artificial intelligence, machine learning, and computational intelligence. The journal publishes original research articles, review papers, and case studies that address both theoretical foundations and practical applications across science, engineering, healthcare, finance, and social domains. AJAAI is committed to rigorous double-blind peer review and to making high-quality research freely accessible to scholars, practitioners, and policymakers worldwide. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, all content is available without subscription or access barriers.
AI-Driven Risk Analysis in DevOps-Based Cloud-Native Payment Systems
The increasing frequency, sophistication, and financial impact of cyberattacks against financial institutions demand a proactive instead of a reactive approach. One current direction consists of the adoption of Cloud-Native Architecture principles supported by DevOps practices driven by Automation and AIOps tools supplied as-a-Service by cloud providers. Taking into consideration the property of security for the deployment of Payment Systems is paramount, it is proposed a Security-as-a-Service solution using Generative Artificial Intelligence techniques to induce Risk Models. This approach applied within the Design-and-Build phases of the DevOps Automation might link the application code with the risk/security posture of the Payment System and support effective decision-making regarding the acceptance/rejection of the software deployment or update. Generative Artificial Intelligence techniques have been used for textual completion since the introduction of the Transformer architecture. It can be used to induce Risk Models since usually a Risk Model can be represented in a tabular structure summarized by its three fundamental components: Asset, Threat, and Vulnerability. Recent advances in confidential computing and secure enclaves enable the processing of sensitive information without exposing the input data.
Copyright (c) 2023 Vikram Boga (Author) https://creativecommons.org/licenses/by/4.0
Evolving Digital Health into AI-Powered Care
The rapid evolution of digital health technologies has laid the foundation for a transformative shift toward artificial intelligence (AI)–driven healthcare systems. While early digital health solutions primarily focused on data digitization, connectivity, and remote care delivery, recent advancements in AI have enabled more intelligent, adaptive, and predictive models of care. This transition represents a paradigm shift from reactive and standardized healthcare toward proactive, personalized, and precision-driven interventions. AI-driven care leverages machine learning algorithms, big data analytics, and real-time clinical decision support systems to enhance diagnostic accuracy, optimize treatment pathways, and improve patient outcomes. However, the integration of AI into healthcare ecosystems also introduces significant challenges related to data quality, interoperability, algorithmic bias, explainability, ethics, and governance. This paper examines the progression from traditional digital health technologies to AI-enabled care models, highlighting key technological enablers, clinical applications, and system-level impacts. Furthermore, it explores the implications for healthcare professionals, patients, and policymakers, emphasizing the need for robust regulatory frameworks and human-centered AI design. Understanding this transition is critical for ensuring that AI-driven care augments clinical expertise, enhances patient trust, and delivers equitable and sustainable healthcare solutions.
Copyright (c) 2026 Sophie Dubois (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Data Governance for Regulatory Reporting
Data governance strategies based on artificial intelligence are urgently needed to meet financial regulations that require authorities to track and control transactions and flows. Recent events have demonstrated the severe consequences when such regulations are not fulfilled. AI technologies can help close the gap between the high level of regulatory risk and the maturity of compliance and audit governance. Implementing AI techniques in data governance to improve regulatory-ready reporting, audit, and compliance can be mapped to various frameworks. The necessary components and connections can be expressed through the lenses of data lineage and provenance, metadata management and regulation language adapters, natural language processing systems for extracting governance policies from natural language documents, and machine-learning models for monitoring data quality. New regulatory imperatives are piling pressure on institutions that already run overloaded production systems. High levels of regulatory risk require institutions to secure and map their governance processes and controls. Most of the recent regulatory breaches can be traced to poor data quality, weak data flows, and insufficient controls. Data sources and flows are seldom documented, and no system manages data quality in an integrated way. Regulatory stress tests have also led supervisory authorities to seek holistic risk evaluations instead of using the usual isolated pillar assessments. The aim is to have a clear view of the institution's credit risk in order to assess capital needs in extreme scenarios. For many institutions, such assessments require a monumental amount of additional infrastructure and resources because the required reporting does not flow naturally from their business-as-usual processes.
Copyright (c) 2024 Tanja Schultz (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Streaming for Financial Crime Compliance
Financial crime compliance continually evolves in response to regulatory, legal, and business drivers. The longstanding objective of effective risk coverage, delivering acceptable risk-to-reward ratios and limiting adverse operational impact, remains unchanged. Recent drivers, however, include the maturation of artificial intelligence technologies; sustained growth in financial crime rates, coupled with increasing private sector obligations; the increasing intertwining of compliance and governance, risk, and control; a shift toward resolution instead of prosecution; and shifting regulatory focus to effectiveness and impact indicators. Adoption patterns and the mainstreaming of capabilities reflect this evolution, shaping the interrelations between these drivers and compliance objectives. Examining these dynamics specifies the origins of the transition from rule-based to AI-enabled financial crime compliance: pre-existing momentum, the speed of adoption, and the industry impact of AI adoption. This understanding also clarifies the implications of financial crime compliance's evolution for operational governance, enabling the adoption of compliance-by-design principles that embed control, risk management, and governance into business processes.
Copyright (c) 2023 Michael Anderson (Author) https://creativecommons.org/licenses/by/4.0
Cloud-Native AI Systems for Industry-Specific Decision Optimization
Scalable cloud-based intelligent decision systems are investigated for industry-specific problems in different sectors that can be optimized by AI-assisted big-data analytics. Despite the growing popularity of cloud-based AI and big-data-driven decision systems, evolution in the required cloud infrastructure and the actual data pipelines have not yet been undertaken in detail. In addition, the current research has not gone into sufficient detail on cloud-based systems for specific high-scaling industry problems such as for the healthcare and life sciences sector or those supported by the manufacturing or supply chain domain. Cloud decision systems also have specific requirements for management and governance due to the nature of data belonging to different parties, and these aspects require further consideration. Consequently, the focus is on cloud systems for scalable manufacturing or supply chain operation decision management, with practical industry examples clearly described for evaluation and cross-validation of accuracies. The study serves teleological domain-focused decision-systems deployment in generic governance-integrity-demand-supporting environments. For exemplary industry-use domain-focused problems, demand forecasting, inventory/supply, production scheduling, logistics, or resiliency-enhancement of supply chains, patient-flow management, clinical-decision-support allocation of resources in healthcare, and integration of multiple-organization datasets for results comparison and research in life-sciences decision support are highlighted. The encoding and timing aspects of teleological risks are also emphasized at the decision-system level, while general privacy and security requirements specify the data-handling and incident-reaction measures needed for protection against unauthorized disclosure or service unavailability.
Copyright (c) 2026 Katarzyna Nowak (Author) https://creativecommons.org/licenses/by/4.0
Small Models as Control Planes for Enterprise GenAI
Generative AI applications such as chatbots and text-to-image systems create demand for GenAI models that address the common information needs of diverse stakeholders in a responsive and personalized manner. Yet the effort to train, host, and serve these models can incur substantial cost and complexity. Many large language models can answer a wide range of questions, but risk being underutilized for specific enterprise workloads. At the same time, enterprise-integrated GenAI services are supporting the digitalization of business processes at an unprecedented scale, revealing latent use cases for specialized models or adjusted configurations of the same model that reflect the cost profiles of these systems. These factors suggest that deploying smaller models to manage the GenAI orchestration layer across an enterprise might yield significant cost savings. A control plane design based on the concept of GenAI orchestration is proposed, along with a set of cost-efficiency principles for implementing this functionality. Control planes are responsible for decision-making and policy enforcement across a distributed system. Making cost-effectiveness an explicit design goal when architecting an orchestration layer introduces additional considerations beyond those that typically inform the design of control planes. Model size, sparsity, quantization, caching, and workload characterization shape the trade-offs governing the overall cost of model execution, create opportunities for realizing cost savings, and identify workload patterns that can further inform cost-saving measures.
Copyright (c) 2026 Carlos Mendoza (Author) https://creativecommons.org/licenses/by/4.0
Automated Feature Engineering for Large-Scale Healthcare Data
A rapidly proliferating corpus of clinical research harnessing the power of machine learning has substantial implications for healthcare feature engineering. As a broad umbrella encompassing data preprocessing, quality control, transformation, and generation, feature engineering addresses a major bottleneck in the production of predictive models. Automated feature engineering systems are increasingly deployed at scale to meet the challenges of generating the vast quantity of predictive features necessary for successful, generalizable, and clinically useful machine learning systems. Such clinical feature engineering systems produce features that are applied in a predictive setting after the fact and not explicitly linked to clinical care, but nevertheless involve substantial risk. A principled examination of a clinical feature engineering system can be framed in terms of six core components: data governance; compliance with privacy and regulatory constraints; appropriate validation and prospective evaluation; consideration of data biases; the use of effective data-quality and preprocessing pipelines; and sound candidate feature generation, scoring, and selection strategies. Health systems typically possess an assemblage of rich and diverse, yet underutilized, information with the potential to contribute meaningfully to clinical prediction problems. Electronic health record (EHR) data, comprising clinical notes, laboratory values, medication orders, and procedure codes; over a decade’s worth of length and width dataset and point-of-care laboratory test results; continuous biosensor measurements; DNA sequencing data; biomarkers derived from imaging; and drug compounds targeting genotypes provide raw material for hundreds of prediction problems in diverse specialties. However, machine learning in healthcare exhibits a stunning lack of reproducibility: many predictive models fail to retain their accuracy in different cohorts, and those that do are seldom incorporated into routine clinical care. A significant bottleneck underlying this failure lies with the feature engineering step.
Copyright (c) 2026 Tanja Schultz (Author) https://creativecommons.org/licenses/by/4.0
Predictive analytics leverages past occurrences to answer questions about future events, thereby enabling healthcare organizations to identify exceptional cases and optimize strategies and actions. Healthcare predictive analytics applies predictive analytics to clinical prediction, resource allocation, workflow optimization, risk stratification, treatment response prediction, and other tasks with different objectives and stakeholder perspectives. Successful solutions can be instrumental in improving healthcare outcomes, increasing operational efficiency, and achieving better return on investment, thereby stimulating interest among analysts and clinicians. However, the innovation gap in applying machine learning to healthcare is primarily associated with production-grade models rather than algorithmic novelty. Machine learning is maturing into a viable technology for many forms of prediction, but the surround systems and processes needed for real-world adoption remain largely unsolved. Production-grade pipelines fill this need by providing the end-to-end framework connecting clinicians’ predictive ideas to analytically-driven changes in healthcare delivery. They support experimentation by enabling analysts or researchers to quickly build and test pipelines on small-scale data using diverse models without requiring detailed technical expertise. For the clinical effort to yield substantial benefits, however, the pipelines must be production-grade, thereby allowing a solution to work reliably and continuously once analysts uncover an interesting prediction application.
Copyright (c) 2025 Olivia Johnson (Author) https://creativecommons.org/licenses/by/4.0
Machine learning operations (MLOps) comprises the practices, methods, and tooling that facilitate the deployment of reliable ML models in production environments. While many aspects of cloud data platforms are designed to enable reliability, only some managed ML services support the MLOps goals of continuous integration, continuous delivery, data lineage tracking, associated reproducibility, governance, and security. Furthermore, reliability encompasses not only the fulfillment of service-level objectives, but also systematic monitoring, alerting, and incident response automation. Architectural patterns are proposed to enable reliable deployment in cloud data platforms, focusing on the implementation of continuous integration and testing pipelines for ML models and the formulation of continuous delivery and rollout strategies. Continuous integration pipelines reduce the risk of regressions and ensure sufficient model performance at the time of deployment, while continuous delivery pipelines enable rapid updates to production models within acceptable risk profiles. The landscape of publicly available MLOps frameworks, tools, and services is also examined, emphasizing the pros and cons of established and rising solutions in containerization, orchestration, model serving, and inference. Containerization and orchestration contributes to the building of reliable deployment pipelines in cloud data platforms, whether general-purpose tools (e.g. Docker and Kubernetes) or solutions tailored for ML workloads. Containerized serving frameworks designed for high-throughput, low-latency inference can benefit a wide range of business applications, while auto-scaling and model versioning capabilities enhance the ease of use of cloud-native ML services.
Copyright (c) 2024 Ethan Williams (Author) https://creativecommons.org/licenses/by/4.0
Explainable Fraud Detection Hybrid Cloud-Native GenAI for Insurance
Using hybrid cloud-native architecture, machine learning (ML) and AIOps technologies, this study addresses the explainability challenges of insurance fraud identification models built on proprietary telecommunications data. Internal and external enterprise data, together with historical fraud incident records, power supervised classification models for fraud detection. Explainable Artificial Intelligence (XAI) technologies, which shed light on model decision-making processes and outcomes, are essential for business acceptance, regulatory compliance, and alignment with risk-mitigation objectives. The research thus fills a critical gap in the literature, encouraging organizations to move beyond accuracy-centric goals and embrace a more comprehensive view. The findings reveal that combining data from telecommunication systems with historical fraud event data leads to more accurate models and highlights the importance of class-imbalance calibration. A realistic simulation involving a high-tech anti-fraud operation showcases the potential of a hybrid cloud-native architecture with MLOps capabilities for supporting the whole ML life cycle. With appropriate scrutiny and evaluation, these models can serve business decision-making and be deployed within AIOps procedures built on hybrid cloud architectures. Looking ahead, the investigation points to fruitful future work in two areas: enhancing the explainable capabilities of fraud models through generative AI and establishing new methodological standards for the deployment of explainable fraud detection models within hybrid cloud environments.
Copyright (c) 2024 Pierre Moreau (Author) https://creativecommons.org/licenses/by/4.0
The Evolution of Next-Generation Wireless Networks
The evolution of wireless generations is illustrated to demonstrate that 6G is the sixth generation of wireless communications and not an extension of Fifth Generation technology. Defining concepts, technologies, use cases, and a roadmap is pursued to enable sound and consistent progress within the 6G community. Society Mobility, latency, reliability, and location-aware requirements demand connective solutions are met rapidly adopted. Global connectivity, ubiquitous sensing, ultra-high data rates, latency and energy-efficiency are paramount. Advanced waveform, multiple-access technology, Terahertz communications, and novel materials enable 6G evolution. Wireless connectivity is indispensable as humans and machines become increasingly mobile, dependent on connectivity, and converge in the Industrial Internet of Things. Communication, computation, and storage requirements are growing hierarchically, prompting demands for higher, lower latency, more reliable, and location-aware services. The long-term potential of Digital Twins and the nearness of the tactile Internet further shape 6G service definition, ushering in a period of global connectivity and a sensing ecosystem. Such requirements prompt the creation of dedicated solutions and associated application domains, as distinguished from service extensions. The vision of Digital Twins of the Environment, Technological Dressing, and Mind at Scale serves as decoupled benchmark.
Fraud poses ongoing operational and financial challenges to the insurance industry, which rely on expert knowledge to prepare for and react against ever-evolving attacks. Beyond discrimination of potential fraud scenarios, future detection systems must therefore support the synthesis of training data for evolving patterns, surface latent signals for unseen forms, propose tests for the detection of adaptative fraud patterns and scenarios for benchmarking, and automatically adapt to changes. Recent advancements in the fields of Generative AI and Autonomous Decision making propose highly complex agents able to reason around natural language, map textual to 3D spaces, and learn new object categories from a few examples. Such models can find application to these aspects, and a study plan explore the hybrid integration of Generative and Agentic AI. The research takes the shape of a Do-Observe-Learn cycle, with the first cycle concentrating on Detect, along with Detect processes for Synthesis, Adaptation, and Quality Layer, thus applying the original focus of fraud detection to the new context, identifying the critical elements from which new decisions can be made, concluding with the resulting TDCC basis for realisation. An analysis on how evolving patterns can influence the design is also performed, targeting integration path of the hybrid design, with the generative component directly involved in detection pipeline in layer supporting adaptation. One important instance of integration focuses on the Data Quality Layer, combining external knowledge with the concept of absolutely difficult-to-detect forms for fraud detection, thus synthesizing signals that should be present in the data for the adapted detector to generalise the aspect.
Dimitris Plexousakis Dimitris Plexousakis
Copyright (c) 2023 Dimitris Plexousakis (Author) https://creativecommons.org/licenses/by/4.0
Significance of Negative Matric Suction Pressures on Infiltration in the Swash Zone_Final
The swash zone's complex hydrodynamics can be influenced by infiltration and exfiltration processes, which are not yet fully understood. This study explores the role of negative matric suction pressures in swash-zone infiltration, focusing on seven naturally occurring beach media samples. Using a combination of tensiometer and in-contact filter paper methods, we examine the prevalence of matric suction pressures for beach media ranging from fine pebbles to fine sand. The results highlight the existence of matric suction pressures within the range 0-2 kPa for the examined media samples, with maximum values observed within the grain-size range 0.85-0.71 mm. Our results reveal a previously underexamined aspect of swash-zone infiltration, highlighting the potential significance of matric suction in influencing coastal processes. The findings suggest that omitting matric suction effects is shown to be an invalid assumption under certain conditions, as these pressures can exceed gravitational potential.
Copyright (c) 2025 Anton Ali, Damian Alexander (Author) https://creativecommons.org/licenses/by/4.0
Scalable Cloud Systems for Automated Precision Medicine
The paper discusses the critical importance of data ecosystems within cloud-native environments designed for predictive and automated decision-making systems in healthcare. The evolving requirements for healthcare service delivery necessitate an alternative architectural paradigm that goes beyond traditional monolithic systems and monolithic software applications hosted in dedicated private data centers or single public cloud infrastructures. Service-oriented deployments are best modeled using microservices and service meshes, data contracts, and independent deployments. Precision healthcare addresses disease prevention, detection, forecasting, diagnosis, management, and outcome prediction for individuals or specific population sets. It leverages underlying compute, storage, and networking services designed for traditional enterprise workloads with strict recoverability and business continuity requirements. Frameworks for scalability, resilience, and disaster recovery translate elasticity features from the public cloud domain, while supporting cloud-native solutions in hybrid or multi-cloud environments. The success of such an approach is entirely dependent on effective data protection techniques, access controls, and the ability to demonstrate compliance with multiple regulations.
Copyright (c) 2025 Michael Anderson (Author) https://creativecommons.org/licenses/by/4.0
Integrated Governance Model for Industry 4.0 Data Centers
Digital transitions are revolutionising industrial processes and services offered. The new arrangements introduce new risks across the entire value chain domain from planning to supply, production and delivery of services. State-of-the-art Enterprise Risk Management (ERM) provides the necessary elements to cover risk domains associated to cyber-physical systems interconnected by the operating technology in concert with Information Technology under Industry 4.0 paradigms Control mechanisms from ERM needs new support structure provided by the Data Centre Governance principles, in particular those linked to data quality, security, privacy and stewardship, for a coherent data preparation and management able to deliver the correct and timely analytics supporting the decision making. The effort synthesises the integrated Enterprise Risk and the Data Centre Governance Framework, integrated with data quality requirements, applying the two best practice domains to the Industry 4.0 era. The underlying concept defined a layer governance structure able to cover day-to-day operate in the production/service delivery sites and the strategic management levels for the application to any organisation supporting the integrated risk management approach. The set of guiding principles is outlined consequently. Grounding elements are drawn from literature sources, the originals support description from the Centre for Internet Security, the Industrial Internet Consortium guiding principles and recommendations, five case studies of Industry 4.0 environments for Manufacturing and the integration of cloud technologies in data centres. The resulting work delivers an integrated structure capable to capture coherent data from the production/service area and to deliver the correct analytics behind the ERM risk management for any Industry 4.0 context.
Copyright (c) 2024 Anumandla Mukesh (Author) https://creativecommons.org/licenses/by/4.0
Fusing Generative and Agentic AI for Insurance Fraud Resilience
Copyright (c) 2026 Ghatoth mishra (Author) https://creativecommons.org/licenses/by/4.0
Predictive Data Systems for Sustainable Farming and Hospitality
Scalable Big Data Processing and Predictive Decision Systems for Sustainable AgriTech and Smart Hospitality align with principles of growing Big Data by maximising processing throughput while minimising completion time and resource consumption. Decision systems in the background support immediate predictions, long-term forecasts, and model evaluations, providing predictive analytics for Smart Hospitality and sustainable AgriTech. Artificial intelligence models for predictive irrigation have a data-driven architecture with large-scale data from public repositories and real-time weather feeds. Smart Hospitality applies a multi-timescale approach for resource and operational cost management via demand forecasting and optimal capacity allocation. Environmental and operational impact narratives form an integrated life-cycle perspective of practice in AgriTech and Smart Hospitality. Growing volumes of data and information sourcing within hospitality operations fuel the need for predictive analytics. Predictive systems supported by environmental and mobility information are important drivers for Smart Tourism and visible within Smart Cities.The capacity and flow constraints of hospitality environments become decision system use cases for demand forecasting and resource allocation. Data-driven decision systems predict demand streams and architecture support management of hospitality processes.
Copyright (c) 2024 Shashikala Valiki (Author) https://creativecommons.org/licenses/by/4.0
A Unified Big Data Infrastructure for AI-Driven Therapeutic Personalization
AI—driven precision medicine requires access to large amounts of clinical data together with detailed clinical annotations. Clinical data platforms support this need by integrating patient records, diagnostic data, and clinical research data within a collect—once, use—many approach. Rich patient records combined with scalable analytics platforms enable the reuse of clinical data for novel analyses. Such platforms create new clinical learning loops, allowing the delivery of genomic—guided precision therapeutics to more patients. State—of—the—art clinical data platforms support dynamic, integrated clinical records, simplify cross—modality data collection, and facilitate the clinical adoption of AI technologies. The democratization of clinical data, imaging, and genomic data into open foundations allows the global research community to build foundation models that can later be adapted for clinic—specific contexts or for other clinical functions. Such foundation models offer rich transfer learning opportunities that reduce the cost of model training while increasing performance and addressing clinical bias. Emerging architectures—modelling techniques and privacy—preserving technologies address data interoperability challenges, supporting the creation of true population datasets at scale and at equal cost for all communities and demographics.
Copyright (c) 2026 Dasari Vinay (Author) https://creativecommons.org/licenses/by/4.0
Reimagining Clinical Data Architecture for Personalized Care
Copyright (c) 2024 Bhasker Katta (Author) https://creativecommons.org/licenses/by/4.0
Smart Cloud Ecosystems for Precision Healthcare Decision Systems
Copyright (c) 2023 The American Journal of Analytics and Artificial Intelligence (AJAAI) https://creativecommons.org/licenses/by/4.0
Cognitive Cloud Ecosystems for Industrial Foresight
Industrial organizations are rapidly adopting cloud technologies to reduce infrastructure management costs and increase operational flexibility. Nonetheless, existing cloud deployment uses are mainly focused on infrastructure as a service and data storage. In parallel, the demand for data-driven predictive modeling has grown and continues to evolve beyond standard business intelligence. As a result, cloud-native software development and deployment models have attracted significant attention from academia and industry. However, related research into the cloud-native paradigm remains limited and tends to offer design and development support for a single application or service without addressing the complete data ecosystem. The data processing and predictive modeling lifecycle requires a cloud-native architecture that supports large-scale predictive modeling operations and the construction of a data-oriented infrastructure. Building intelligent capabilities involves orchestrating all machine learning operations in the cloud, integrating advanced machine learning lifecycle frameworks, incorporating feature stores, and enabling real-time scoring orchestration and execution algorithms. A predictive decision-making data ecosystem focuses on the complete data processing pipeline in relation to predictive models, integrating model data processing and inference pipelines into orchestration and scheduling frameworks while supporting event-driven workflows.
Copyright (c) 2025 The American Journal of Analytics and Artificial Intelligence (AJAAI)
Real-Time AI Compliance at the Speed of Financial Fraud
Smart integration of generative AI with event-driven streaming architectures offers a promising avenue for enhancing Financial Crime Compliance. Presenting an approach for Intelligent Financial Crime Compliance (IFCC), this paper defines, delineates, and discusses the concept before illustrating its application in the context of strengthening detection of money laundering (ML) and terrorist financing (TF) activities. Generative techniques for detection, data risk/scoring, and anomaly identification provide accurate, explainable, actionable, and adaptive insights into potential compliance breaches. Evaluation frameworks tailored to the streaming regimes of compliance workflows address the urgency and operational nuances associated with real-time AML/CFT surveillance. Fed by diverse continuous data feeds and often governing timely responses to regulatory penalties, Financial Crime Compliance (FCC) processes traditionally viewed through a descriptive lens comprise a rich target for AI-enabled automation within a data-driven paradigm. Gaps in rule-based detection technologies—designed in hindsight for known patterns of illicit behavior or anomalies and lacking continual risk calibration—emphasize the need for a data-centric streaming architecture. The intensifying real-time nature of ML and counter-terrorism financing activities further motivates broad streaming detection. At the same time, the availability of such vast data resources in near real-time offers potential to improve compliance decision-making, including for risk scoring, alert generation, and near real-time data lineage.
Cross-Sector Model Deployment Intelligence
MLOps-enabled intelligent systems automate the deployment of machine learning pipelines that can exploit machine learning capabilities and pre-existing data across data platforms in multiple sectors, enabling automated decision-making with a known (soft) risk or loss function. MLOps concepts enable new forms of machine learning model lifecycle management that may leverage autoML and human-in-the-loop approaches for model development, training, and tuning; they facilitate the continuous operation of pipelines on a 24/7 basis; they address concerns such as privacy, security, model bias, transparency, explainability, interpretability, and inclusivity; and they provide mechanisms for automatic scaling, fault-tolerance, and infrastructure cost optimisation. Automated model deployment represents a major feature of MLOps. Automated deployment refers to the management of machine learning models and pipelines so that the entire lifecycle from development to deployment and operation may be governed from an MLOps perspective. When system performance is measured within a known range and against a soft loss or risk function, virtual data platforms that encompass sectors as diverse as finance, healthcare, retail, and telecommunications possess feature sets supporting such automations. Such automations focus on Machine Learning as a Service (MLaaS) deployments on a 24/7 basis, with the objective of enabling decision making within these soft constraints and scaling the deployment of autoML pipelines in tandem with demand for untrained services.
Copyright (c) 2023 The American Journal of Analytics and Artificial Intelligence (AJAAI)
Edge Intelligence for Predictive Civil Asset Management
Innovations in artificial intelligence (AI) and predictive analytics are driving the convergence of big data, sensor networks, the Internet of Things, and cyber-physical systems into environments known as smart cities. By augmenting conventional city information systems with predictive modeling capabilities that exploit the wealth of high-frequency operational data generated by sensor-equipped urban infrastructure assets, it is possible to implement proactive preventive maintenance strategies that promise to enhance infrastructure robustness, safety, performance, sustainability, and cost-effectiveness. Such strategies, termed predictive maintenance, exploit predictive models of asset health (measured via condition indicators), typically learned using supervised learning techniques, and create actionable information to optimise maintenance scheduling and better allocate limited maintenance budgets. Despite considerable interest in predictive maintenance research, most efforts have focused on specific domains (e.g., buildings, roads, bridges, tunnels) in isolation. Thus, a big-data framework architecture that provides for the integrated prediction of asset health indicators across different urban infrastructure systems and their underlying sub-systems is developed. In the proposed framework, an architecture for the ingestion and integration of diverse high-dimensional data types is presented, together with a scalable ensemble-learning-based approach for feature engineering that exploits the multi-variate time-series nature of the data generated by real-world monitoring systems. By providing a generic architecture for the predictive maintenance of infrastructure assets, the framework lays the foundations for effective predictive maintenance strategies in smart cities.
Copyright (c) 2024 The American Journal of Analytics and Artificial Intelligence (AJAAI)
Self-Evolving Data Ecosystems for Predictive Enterprise Clarity
Two significant developments have converged: the emergence of cloud-based data lakes, conducive to cost-effective data storage and processing for intelligent enterprise applications, and the application of artificial intelligence (AI) to greatly improve the various processes required to build and maintain a data lake. Intelligent enterprise applications can be categorized as descriptive analytics and dashboards; advanced analytics and predictive modeling; and real-time analytics and streaming data. These categories map to typical application areas such as customer analytics and personalization; operational intelligence and asset monitoring; and supply chain optimization and risk management. The primary issues in building a cloud-based data lake include data privacy and sovereignty; fairness, bias and explainability in AI; performance management and cost optimization.The abstract now conforms with the rest of the paper. Data governance and cataloging; security and compliance; architecture and components are addressed. Emerging AI technologies significantly enhance the intelligent enterprise applications in a cloud-based data lake, particularly data ingestion and feature engineering; automated metadata enrichment and lineage; and data quality and cleansing. Supporting both the descriptive and advanced analytics categories of intelligent enterprise applications, the Intelligent Enterprise Analytics and Reporting Framework comprises descriptive analytics, advanced analytics, real-time analytics and streaming data; integrating the domains of customer analytics and personalization; operational intelligence and asset monitoring; supply chain optimization and risk management.
Smart Agriculture Climate Adaptation via AI Big Data
Climate change is threatening the future of agriculture worldwide, and significant adaptation efforts will be needed to mitigate its consequences. Although many adaptation strategies have been proposed, a comprehensive climate-adaptation framework for smart agriculture based on AI-enabled big data are still lacking. To bridge this gap, a cloud-based Cyber-Physical Environment for Smart Agricultural Climate Adaptation was developed by integrating big-data sources needed for climate-adaptation simulation, prediction, management, and decision-making. The resulting Information, Data, Process, Software, and Model (IDPSM) ecosystem integrates supervised and unsupervised predictive-analytics models, downscaling and microclimate-assimilation Modeling, and weather-and-microclimate-scenario-planning tools, and supports AI-enabled decision-making. Several representative Climate Adaptation Case Studies were deployed in various global contexts to demonstrate the use of the IDPSM ecosystem and its associated AI methods for climate-adaptation simulations, impact assessments, and/or decision making. Lessons learned from these case studies illustrate the potential of deploying such an AI-enabled big-data ecosystem to drive data-centric climate adaptation for agriculture.
AI-Powered Fraud Detection and Risk Intelligence in Banking and Insurance
AI/ML technologies are exploited to detect, mitigate, control, and minimize risks in banking activities as they create, transmit, lend, invest, insure, and secure value. AI/ML paradigms and technologies based on microservice architecture support dynamic delivery of probabilistic native support of banking actions and transactions. Notable categories of AI/ML technologies are based on risk intelligence, fraud detection and prevention, and fraud control. Risk intelligence examines the risk area of the banking ecosystem. Risk intelligence warns participants about the risks involved in their actions or transactions, predicts a credit card operation's logical behaviour in order to set dedicated limits, and determines the best approach to set a limit for a credit score. Fraud detection identifies fraud in real time using analysis of users’ attributes and account history, monitor behaviour changes or sudden changes of an attribute, and classify whether an operation will be done by a legit user or not. Fraud prevention detects fraud before it occurs using knowledge of historical cases to find the relationship between data in order to alert when a similar case may happen and detect unusual patterns to create rules for risky operations. Fraud control deals with the management of fraudulent approaches, actions or transactions for the banking sector. Practices for fraud control address identifying tendencies in the banking sector for fraud detection—vent consumers’ intolerance for crime, minimize social disparity, increase competence to minimize price of fraud.
Copyright (c) 2026 The American Journal of Analytics and Artificial Intelligence (AJAAI)
Agentic AI for Demand, Credit, and Inventory Intelligence in Food Supply Chains
A multi-cloud big-data architecture delivers predictive services for demand forecasting, credit risk modeling, and supply chain inventory financing. Strategic partnerships provide a comprehensive range of data for a national food service domain, supported by data governance and management protocols. Predictive services facilitate collaborative demand reconciliation across heterogeneous cloud environments, deepening data interaction among competing firms, while optimizing Steering Committee members’ inventory levels and exposure to credit risk from suppliers, distributors, and retailers. Agentic Artificial Intelligence, integrating the principles of Autonomous Analytics and a multi-agency governanc­e framework, performs the groundwork for decision signals regarding all three services. While agentic artificial intelligence (AI) enables autonomous data preparation, predictive analytics, and deep learning, it does not remove humans from the loop. Instead, agentic AI facilitates deeper human-AI collaboration across the multi-cloud big-data architecture, with the a­gentic AI agents charged with examining results and providing interpretable explanations. Although solutions are focused on a national food service supply chain formed by the collaboration of food manufacturers, distributors, and both commercial and governmental businesses, the framework is readily applicable to other large supply chains requiring data preparation, predictive demand signals for collaborative reconciliation, risk assessment, and inventory financing facilitation.
Real-Time AI Risk Management for Multi-Party Derivatives Trading A Cloud Compliance Framework
The problem of real-time risk for enterprise-scale derivatives, on the sell-side business, and the orchestration of workflows across multiple counterparties, such as for FX, are well understood; indeed, gap analysis templates exist. Yet no solution is in production. Hedge proposals, whether for market-making services or for wholesale multi-country liquidity coverage fundamentals in bankruptcy planning, are self-evidently Rule 63 costs for which risk-based decision engines at enterprise scale should generate a valid counterparty profile with risk-adjusted positions, identifying the best bid/offer patch for execution and settlement. Furthermore, no architecture, systemic or architectural, meets the needs of all key stakeholders and disciplines. Leveraging the capabilities of a cloud-native foundation and embedding an AI-driven decision engine within the distinction between decision and execution engine on observed latencies supports risk-aware automation and real-time orchestration. It also strengthens the integration of risk management, regulatory compliance, and risk governance into a coherent whole across the previously examined views of risk types, risk data, the control plane, and the regulatory requirements of the system.
Real-Time Decision Intelligence Using Adaptive Deep Learning
Adaptive deep learning models are needed to support real-time operations in dynamic business environments characterized by fast state changes. Streaming data processing architectures, model adaptation mechanisms, and decision-trust capabilities form the building blocks for adaptive architectures. Adaptation relies on a spectrum of techniques, from online learning and continual learning through meta-learning to lightweight and edge-enabled neural networks. Evaluation is also challenging: approaches must be benchmarked in dynamic non-stationary settings using appropriate real-time metrics. In addition, vigilance is needed to ensure robustness, fairness, and compliance in controlled areas, such as financial services and healthcare. Real-time adaptive support has been achieved in practical scenarios involving supply chains, healthcare resource allocation, and financial-services operations.