American Journal of Analytics and Artificial Intelligence (ajaai) with ISSN 3067-283X
Journal Summary What is AJAAI? The American Journal of Analytics and Artificial Intelligence (AJAAI) is a peer-reviewed, open-access international journal dedicated to advancing high-impact scholarship in analytics, artificial intelligence, data science, machine learning, and computational intelligence. The journal serves as a rigorous scholarly platform for researchers, practitioners, academicians, and innovators to disseminate original theories, methodologies, experimental findings, and technological developments that address real-world challenges. AJAAI is committed to global knowledge equity through unrestricted open access, ensuring that cutting-edge research is freely available to the international scientific community via the official portal at https://ajaai.com/index.php/ajaai/. Indexing & Journal Metrics AJAAI is actively pursuing indexing in major international bibliographic databases to maximize the visibility, discoverability, and citation impact of all published research. Authors are encouraged to visit the journal website for the most current indexing status, CiteScore, SCImago Journal Rank (SJR), and Source Normalized Impact per Paper (SNIP) metrics. Note: Scopus-indexed journals prominently display metrics such as CiteScore, SJR, SNIP, and Quartile (Q1–Q4). Once indexed, this section should reflect live metrics such as: CiteScore: [e.g., 4.2] SJR: [e.g., 0.75] SNIP: [e.g., 1.10] Scopus Quartile: [e.g., Q2 — Computer Science] Scopus Source ID: [ID] Open Access & Publication Model AJAAI follows a fully open-access publishing model, ensuring all articles are immediately and permanently accessible upon publication. This model aligns with international open science principles and maximizes research impact through: Global visibility and discoverability across international research communities Higher citation potential through unrestricted access Rapid dissemination of innovations in analytics and AI research Equitable knowledge sharing across academia, industry, and society Article Processing Charges (APCs) are levied only upon manuscript acceptance, with no upfront submission fees. Detailed APC information and any available fee waivers are published transparently on the journal website. Aims & Scope AJAAI publishes original research articles, systematic review papers, technical reports, case studies, and short communications spanning the full spectrum of analytical and AI-focused disciplines. The journal particularly welcomes interdisciplinary work that combines analytics, AI, computing, engineering, and applied sciences. Primary subject areas include: Analytics & Data Science Predictive and prescriptive analytics; statistical and quantitative modeling; business analytics and decision intelligence; big data engineering; data mining and pattern discovery. Machine Learning & Deep Learning Neural networks and deep learning architectures; reinforcement learning; ensemble and transfer learning; meta-learning; explainable and interpretable AI (XAI). Artificial Intelligence Intelligent systems and automation; knowledge representation and reasoning; expert systems; human–AI collaboration; algorithmic decision-making. Natural Language & Speech Technologies Natural language processing (NLP); text mining and classification; conversational AI and chatbots; speech recognition and synthesis. Computer Vision & Image Analytics Image classification and segmentation; object detection; video analytics; medical image intelligence; vision-based automation systems. Advanced Computational Methods High-performance and cloud computing; distributed systems; computational simulation and optimization; mathematical and statistical computing. Applications of Analytics & AI Healthcare informatics; financial and economic modeling; industrial automation; smart cities and IoT; environmental and agricultural analytics; educational technology and learning analytics. Peer Review & Editorial Standards AJAAI employs a rigorous double-blind peer review process in accordance with internationally recognized ethical publishing standards, including the Committee on Publication Ethics (COPE) guidelines. Review Workflow: Initial Screening — Manuscripts are assessed for scope alignment, structural completeness, and originality. Double-Blind Peer Review — Independent subject-matter experts evaluate scientific merit, methodological rigor, and contribution to the field without disclosure of author or reviewer identities. Editorial Decision — Acceptance is contingent upon demonstrated scientific accuracy, analytical validity, and meaningful scholarly contribution. AJAAI strictly prohibits plagiarism, data fabrication or falsification, duplicate submission, undisclosed conflicts of interest, and any misrepresentation of authorship roles. All manuscripts are screened using industry-standard plagiarism detection tools. Publication Ethics AJAAI adheres to the ethical publishing standards of the Committee on Publication Ethics (COPE) and aligns with the ICMJE and COPE Core Practices. All submissions must represent original, unpublished work not currently under review elsewhere. Authors, reviewers, and editors are expected to observe the highest levels of integrity throughout the publication process. Retraction & Correction Policy To safeguard the integrity of the scientific record, AJAAI issues: Corrections — For minor factual errors that do not affect core findings or conclusions. Expressions of Concern — When substantive questions arise and a formal investigation is underway. Retractions — In cases of proven research misconduct, ethical violations, unreliable results, plagiarism, or duplicate publication. All retraction notices are permanently archived and publicly accessible in accordance with COPE retraction guidelines. Digital Preservation & Archiving AJAAI ensures the long-term preservation and accessibility of all published content through secure digital archiving infrastructure, including redundant backup systems and permanent digital repositories. These mechanisms guarantee uninterrupted access for present and future researchers. Privacy & Data Handling Personal data collected through the manuscript submission system is processed solely for editorial and publication correspondence. All data is securely stored, accessible only to authorized journal personnel, and is not disclosed to third parties without explicit consent. Authors may request access to, amendment of, or deletion of their personal data at any time in accordance with applicable data protection regulations. Submission & Contact Information Journal Website: www.ajaai.com Editorial Contact: info@ajaai.com
Intelligent Pipelines for the Speed-of-Money Era
The emergence of Generative AI (GenAI) unlocks new capabilities in software development and operations. A related trend calls for the adoption of DevOps principles and practices. Furthermore, organizations across industries seek to process data in real time. In financial services, that imperative applies to transaction pipelines, where volumes peak at Black Swan events, yet the average workload is relatively modest. Ideas from GenAI-enabled DevOps can accelerate the design, implementation, and monitoring of such pipelines, but tensions remain. The landscape of existing work is therefore reviewed, gaps identified, Generalized Enterprise Architecture Theory summoned, and research questions posed. Addressing them will yield candidate solutions that reconcile GenAI-enabled DevOps principles with the requirements of real-time transaction processing. A foundation for that exploration is laid by: specifying the functional and nonfunctional requirements for such pipelines; providing an overview of GenAI-driven DevOps concepts, architectures, and operational paradigms amenable to financial Pipelines; and articulating suitable patterns, together with the associated challenges and trade-offs. Real-time transaction processing pipelines in financial services must support Black Swan event volumes while meeting operational requirements at lower loads. Ideas from GenAI-enabled DevOps can accelerate the design, implementation, and monitoring of such pipelines, but tensions remain. Existing work is reviewed, gaps identified, Generalized Enterprise Architecture Theory summoned, and research questions posed. Addressing them will yield candidate solutions reconciling GenAI-enabled DevOps principles with the requirements of real-time financial transaction processing. A foundation for that exploration is laid by specifying the functional and nonfunctional requirements for such pipelines; providing an overview of GenAI-driven DevOps concepts, architectures, and operational paradigms amenable to financial pipelines; and articulating suitable patterns, together with the associated challenges and trade-offs.
Generative Artificial Intelligence, GenAI Enabled DevOps, Real Time Transaction Processing, Financial Services Pipelines, Black Swan Event Handling, Enterprise Architecture Theory, Cloud Native Architectures, Transaction Pipeline Scalability, DevOps Automation Practices, Real Time Data Processing, Operational Resilience Engineering, Non Functional Requirements Analysis, Intelligent Pipeline Monitoring, Adaptive System Design, Financial Infrastructure Modernization, Event Driven Architectures, High Volume Transaction Systems, AI Assisted Software Operations, Architectural Patterns And Trade Offs, Enterprise Grade Transaction Systems.
Generative Artificial Intelligence; DevOps; Real-Time Transaction Processing; Financial Services; Fraud Detection; Pipeline Architecture; Cloud Computing
Computer Science; Information Systems; Financial Technology (FinTech); Software Engineering; Data Engineering
Copyright (c) 2026 American Journal of Analytics and Artificial Intelligence (ajaai) with ISSN 3067-283X
DevOps-Enabled Agentic Deep Learning for Insurance Fraud Intelligence
The study investigates DevOps-driven data engineering for agentic AI in insurance fraud detection with a focus on cloud-native automations. Data are centrally secured, accessible, governed, of reproducible quality, properly profiled, and efficiently prepared. Design, development, and deployment encompass extensive safeguards for risk-sensitive decision-making. A superset of continuous integration/continuous deployment tailored to machine learning as well as data governance underlie the approach, demystifying the technology stack and enabling success at operational scale. The investigation is motivated by regulatory, economic, and ethical pressures on the industry, demand for predictive solutions, and pain points of legacy monitoring systems. Automation of machine-learning model development and deployment mitigates issues of scalability, reproducibility, and security while incorporating risk assessment. Organizations worldwide are increasingly turning to artificial intelligence in a bid to detect fraudulent activity in all its forms. Such systems, however, generally stem from one-off proof-of-concept initiatives, operate in isolation, and lack sufficient controls for operational deployment in support of everyday decision-making. Nevertheless, agentic artificial intelligence—actant solutions capable of executing decisions without direct human intervention—promises a transformative competitive advantage for business leaders. Integrating governance and control ensures that these automated systems operate within an acceptable risk tolerance, align with organizational values, and minimize unintended consequences. Insurance fraud detection solutions typically fall short of these requirements.
DevOps Driven Data Engineering, Agentic Artificial Intelligence, Insurance Fraud Detection, Cloud Native Automation, Machine Learning Operations, Continuous Integration Continuous Deployment, Data Governance Frameworks, Risk Sensitive Decision Making, Automated Model Deployment, Predictive Fraud Analytics, Scalable AI Systems, Reproducible Data Pipelines, Secure Data Architectures, Regulatory Compliance In Insurance, Ethical AI Governance, Legacy System Modernization, Autonomous Decision Systems, Operational AI Control, Model Lifecycle Management, Enterprise AI Adoption
Insurance Fraud Detection; Machine Learning; Deep Learning; DevOps; Agentic AI; Cloud Computing; Data Engineering
Computer Science; Artificial Intelligence; Machine Learning; Information Systems; Data Engineering; Software Engineering; Insurance/Actuarial Science
The Pre-AI Landscape of Data-Enabled Healthcare Support
Data-driven decision support enables the consolidation, organization, and analysis of amassed health data for more effective decision-making. It complements AI solutions by directly addressing core user needs prior to the availability of clinical AI. Decision-theoretic models provide a normative framework for qualitative and quantitative models. Methods for evidence synthesis underpin clinical decisions supported by data without requiring AI. Technologies that enable these capabilities comprise database systems that collect and store data, tools that facilitate querying and exploration, statistical approaches that extract patterns and structure, and predictive models that summarize risk. The ability to perform medical decision-making has always been constrained by the complexity and uncertainty of the clinical environment. Data-driven decision support refers to a set of processes, methods, and supporting technologies that consolidate, organize, and analyze the accumulated clinical data to make decision-making easier and more reliable. Data-driven decision support does not rely on Artificial Intelligence (AI) methods, although it can help pave the way for them. Recent advancements in Natural Language Processing and Convolutional Neural Networks have made AI tools seem right around the corner, but it is important to remember that these technological trends might not deliver AI solutions for all clinical tasks any time soon. Data-driven decision support can provide answers when AI methods are either not available or cannot be trusted, such as in the early phase of development for a given medical task.
Data-Driven; Clinical Decision Support; Decision Theory; Evidence Synthesis; Statistical Methods; Health Informatics; Clinical Decision Support Systems (CDSS); Evidence-Based Medicine; Health Informatics; Medical Data Analytics; Rule-Based Expert Systems; Electronic Health Records (EHR); Statistical Decision-Making; Knowledge-Based Systems; Clinical Guidelines and Protocols; Data Quality and Standardization.
The paper covers data-driven clinical decision support systems (CDSS) in healthcare, specifically the infrastructure, methodologies, and technologies that enabled healthcare decision-making prior to the emergence of AI.
The paper spans Health Informatics, Clinical Decision Support, Decision Theory, Evidence Synthesis, Statistical Methods, Evidence-Based Medicine, Medical Data Analytics, Rule-Based Expert Systems, Electronic Health Records (EHR), Statistical Decision-Making, Knowledge-Based Systems, Clinical Guidelines and Protocols, and Data Quality and Standardization.
Copyright (c) 2024 American Journal of Analytics and Artificial Intelligence (ajaai) with ISSN 3067-283X
Structural Readiness in the Era of Machine Intelligence
Artificial Intelligence (AI) is positioned to transform healthcare services and operations into intelligent systems, delivering innovative solutions to improve the quality of care while containing rising costs. However, AI readiness at all levels of the healthcare ecosystem must be examined in combination with real-world implementations that deliver actual transformation. Readiness is commonly defined as the state of being fully prepared for something. In a healthcare management context, readiness refers to the state of a healthcare organization in determining whether the required capabilities exist for the successful adoption of a new technology or concept. Towards AI integration in healthcare, the concept applies at three levels—technological, data, and skills readiness—and can be assessed using maturity frameworks that provide an indication of progress and direction, as well as stakeholder analysis. While Technology Adoption Models assess potential uptake of AI solutions by health practitioners, these validations assess preparedness to adopt and/or innovate in AI solutions within the healthcare ecosystem. Emerging AI applications covering clinical needs from radiology to mental health support, or from product development to operational management, present recognition and acceptance challenges requiring stakeholder engagement. Stakeholder impact on the success of AI solutions can be assessed using readiness levels, grouping stakeholders into three categories: IDPC leaders responsible for implementation, users at the point of care who interact directly with patients, and patients, the users of the healthcare system and the ultimate beneficiaries or sufferers from the role out of the AI solution.
AI readiness assessment, Digital health infrastructure,Clinical workflow integration,Health data interoperability,Electronic health record (EHR) maturity,Data governance frameworks,Ethical AI in healthcare,Workforce AI competency,Change management in healthcare,Clinical decision support systems,Health information security,Regulatory compliance for AI,Algorithm transparency,Patient data quality,Organizational readiness for AI,AI adoption barriers,Health system innovation capacity,Human–AI collaboration,AI-enabled care delivery,Implementation science for AI in healthcare.
Pulse and Protocol The Nascent Influence of AI on Care Delivery Rhythms
Early artificial intelligence (AI) adoption and a weakness in organizational execution represent two elements that shape practical examples of AI-assisted clinical workflow optimization. Some institutions are realizing operational efficiencies, short-term reductions in clinical error rates, and enhanced patient experience even amid talent shortages. Initial improvements align with the actual diagnostic or treatment pathway being executed and the real-world clinical and technical capabilities enabled by the AI initiative. Data-driven medicine on the other hand, with its call for evidence-based, structured data-driven access to clinical decision support tools at the time and place of need, as well as automated alerts and reminders, remains a vision yet to be fully realized. Institutions with the right elements in place can expect workflow enhancements provided clinicians are willing and able to trust their clinical judgment more than the underlying models and embrace delegation. While some hospitals have implemented data-mature and user-centered data-driven medicine for the first three years for diagnostic support and decision-making, AI-supported routing and triage, multispecialty treatment planning, and high-urgency multidisciplinary approval have remained aspirational. In other words, the implementation readiness of AI-supported workflow optimization in these hospitals remains in flux, with elements switching between enablers and barriers. Expectation alignment, however, has improved markedly during the same period. Work that focuses on the evolution of clinical governance, data infrastructure, training needs across clinician groups, user-centered design, and operating model readiness is therefore directly relevant to translating AI ambition into execution.
Artificial intelligence in healthcare,Clinical workflow optimization,AI adoption in clinical settings,Early-stage AI implementation,Healthcare process automation,Clinical decision support systems,Machine learning in healthcare operations,Workflow efficiency improvement,AI-driven clinical productivity,Health information systems integration,Clinical operations management,Digital transformation in healthcare,Care delivery optimization,Provider workload reduction,AI impact on clinical practice.
The paper lists these explicitly as keywords: Artificial intelligence in healthcare, Clinical workflow optimization, AI adoption in clinical settings, Early-stage AI implementation, Healthcare process automation, Clinical decision support systems, Machine learning in healthcare operations, Workflow efficiency improvement, AI-driven clinical productivity, Health information systems integration, Clinical operations management, Digital transformation in healthcare, Care delivery optimization, Provider workload reduction, AI impact on clinical practice.
Navigating Accountability and Oversight in AI-Integrated Clinical Systems
AI holds the potential to transform healthcare delivery by improving decision-making and operational efficiency. However, it is important to address the ethical, governance, and operational issues associated with AI-enabled applications before they can be safely and effectively deployed. AI-enabled healthcare presents unique challenges for beneficence, non-maleficence, and patient autonomy. The AI development lifecycle is often not under the control of healthcare institutions, nor are the outputs of AI systems properly understood. Consequently, the true impact of AI on patient outcomes, equity, and justice cannot be adequately evaluated. Governance frameworks play an essential role in establishing an initial level of assurance. A well-conceived but imperfectly implemented implementation governance framework can help reduce harm and increase public trust. IRBs, in combination with government-sponsored risk management and safety assurance measures, can address most of the requirements of the Safe and Effective Product Regulations. These agencies are best placed to prevent harm emanating from the use of AI-enabled interventions. The next operational development steps focus on building the evidence needed to inform and guide healthcare AI. Proactive information sharing, together with proper documentation and knowledge capes, can mitigate some of the consequences of working without feedback or clinical validation.
Artificial intelligence, health, healthcare ethics, ethical theory, beneficence, non-maleficence, patient autonomy.
The paper covers AI in healthcare, covering ethical governance, patient safety, clinical decision-making, and accountability in AI-integrated clinical systems.
Healthcare/Medicine, Artificial Intelligence, Biomedical Ethics, Health Informatics, and Regulatory Science.
The Evolutionary Leap Toward AI-Integrated Clinical 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.
Digital health, Artificial intelligence in healthcare,AI-driven care,Clinical decision support systems,Machine learning,Predictive analytics,Personalized medicine,Precision healthcare,Health informatics,Electronic health records (EHRs),Big data in healthcare,Remote patient monitoring,Telemedicine,Automation in healthcare,Ethical AI,Explainable AI,Healthcare transformation,Patient-centered care,Interoperability,Data governance.
The paper covers the transition from Digital Health Technologies (DHTs) to AI-driven clinical care, examining technological enablers, clinical applications, ethical considerations, and governance frameworks.
Health Informatics, Biomedical Engineering, Artificial Intelligence/Machine Learning, Clinical Medicine, Medical Ethics, Health Policy, and Data Science.
Copyright (c) 2023 American Journal of Analytics and Artificial Intelligence (ajaai) with ISSN 3067-283X
Sovereign Data, Shared Intelligence A Polycloud Federated Learning Architecture for Privacy-Conserving Demand and Distribution Optimization in National Food Wholesale Networks
National level supply chain optimization demands federated analytics across multiple sovereign clouds to respect regulatory needs and avoid privacy concerns. In traditional centralized models, neither such aspects nor the sensitivity to data latency can be suitably considered. A real-world wholesaler of the food service sector engaged in the development of demand forecasting, inventory optimization, and transportation planning models from different data domains and sources, routed through AWS, Azure, and GCP. Data sharing agreements enforced mandatory storage duration and data sharing policies to satisfy ownership rules and a business partnering model was used to coordinate application developments. State-of-the-art algorithms were applied for the federated learning building blocks, and the communication overheads associated with model exchanges were assessed. Today's business world suffers from the lack of information about critical events that occur far away and could have a significant positive or negative influence on business outcomes. Big data opens up the possibility of having more information for analysis but brings with it new challenges and costs, especially when dealing with processing and scripting these big data analytics. The need for specialized know-how and costs are important factors that dictate the success of an analytics process and the return on investment. However, successful and careful analysis of data has its rewards. Developing scalable models on three major public clouds (AWS, Azure, and GCP) for national-level supply chain optimization and representing data and models accurately under privacy and security regulations are still in their infancy.
Federated learning; federated analytics; big-data analytics; supply chain; privacy-preserving computation; cross-cloud; data governance; Government of Canada; transport and warehousing; food services; wholesale trade; national capital region; Amazon Web Services; Microsoft Azure; Google Cloud Platform.
he paper does not list a formal "Subjects" field. Based on the keywords provided, the subjects are: federated learning, federated analytics, big-data analytics, supply chain, privacy-preserving computation, cross-cloud, data governance, transport and warehousing, food services, and wholesale trade.
the work spans computer science, supply chain management, and data engineering.
Copyright (c) 2025 American Journal of Analytics and Artificial Intelligence (ajaai) with ISSN 3067-283X
Cognitive Production Lines Reinforcement Learning and Adaptive Intelligence for Real-Time Vehicle Assembly Reconfiguration in Industry 5.0
Smart Vehicle Manufacturing (SVM) plays a vital role in the fast-growing market of electric vehicles. A distinctive feature of these New-Generation Intelligent Connected Vehicles (NGICVs) is their software architecture. SVM uses a traditional Mass Customization assembly-line production mode. Unlike simple electric vehicle architectures, NGICVs require more diverse components and parts like traditional internal combustion engine vehicles, but these part shortages lead to serious line-stopping production issues. Building SVM systems based on traditional Mass Customization assembly-line modes cannot solve this problem. To overcome these issues, Smart Vehicle Manufacturing paradigms that adopt Adaptive Assembly Lines (AALs) are proposed. An Adaptive Assembly Line (AAL) is a fundamentally new assembly-mode paradigm that enables real-time assembly-line adaptation and mass customization of various part configurations according to real-time market demands in Smart Vehicle Manufacturing. AAL architecture drastically changes each stage of the Smart Vehicle Manufacturing assembly process. SVM systems have deployed data architecture supporting these Smart Vehicle Manufacturing paradigms. The developed data governance framework with in-depth data perception enables high-quality data generation for AI-driven Adaptive Assembly Line system performance improvement.
Adaptive assembly line, AI data infrastructure, machine learning, next-generation vehicle manufacturing, perceptual systems, optimal scheduling, predictive maintenance, operator support, human–machine collaboration, threat modeling.
Smart Vehicle Manufacturing, Adaptive Assembly Lines, Reinforcement Learning, Industry 5.0, Real-Time Scheduling, Predictive Maintenance, Human–Machine Collaboration, Cyber-Physical Security.
Manufacturing Engineering, Artificial Intelligence, Computer Science, Industrial Engineering, Cyber-Physical Systems.
Synthesizing Foresight: A Generative AI Framework for Financial Risk Anticipation in Retail and Decorative Coatings Supply Networks
Financial risk is an increasingly complex challenge in supply chains. Dynamic changes in the market affect multiple stakeholders across different tiers of the supply chain. Accurately defining and estimating these risks is crucial for optimizing working capital utilization and planning. Generative AI has been identified as a technology that can play a significant role in operational processes. Research on its application has focused primarily on operational processes, while the potential benefits for predictive modelling, particularly in supply chain financial risk management, remain unexplored. This study attempts to address this gap by proposing a framework for simulating various financial risk exposures in a retail supply chain and cross-tide testing the resulting specials within the supply chain of a paint manufacturer. The proposed framework comprises three main components: (a) establishing a data architecture with the required data elements, sources, and flows for preparing the data, (b) implementing generative AI models to simulate financial risk exposures, and (c) defining metrics for validating the output of the simulated models. These components are examined in detail, and their implementation is illustrated through a retail use case and a cross-supply chain application involving a paint manufacturer. In the retail case, financial risks related to stock levels, turnover, working capital, and liquidity stress under demand volatility are explored. The second case involves a paint manufacturer faced with a potential price increase of key raw material. It investigates the impact of sourcing decisions underprice uncertainty on raw material costs and financial exposure.
Financial Risk, Generative AI, Predictive Modelling, Supply Chain Finance, Supply Chain Management, Retail Business, Paint Industry, Chain Vertical Integration, Time of Day Effect.
Financial Risk, Generative AI, Predictive Modelling, Supply Chain Finance, Supply Chain Management, Retail Business, Paint Industry, Chain Vertical Integration, and Time of Day Effect (as listed in the Keywords section of the abstract).
Intelligent Real-Time Audit Analytics Framework for Automated Manufacturing Finance Operations
Smart manufacturing offers an increasingly integrated and data-driven approach to production, benefitting from the latest technologies like Industry 4.0, Internet of Things (IoT), cloud computing, and Big Data. Real-time artificial intelligence (AI)-enabled auditing and compliance can exploit these technologies to manage compliance in real-time. The advantages of continuous auditing compared with batch auditing are obvious. AI supports the automation of many audit and compliance functions. Although companies have adopted artificial intelligence for various operations, the AI model development process requires considerable resources, time, and expertise. Therefore, the AI-supported real-time audit and compliance framework is within smart manufacturing finance. Key definitions are clarified: Real-Time Audit (RTA), Continuous Controls Monitoring (CCM), AI components (models, data preparation), data lineage, and governance terms. A Continuous Auditing and Continuous Controls Monitoring (CACM) model supports more efficient compliance management and verification. Information Technology Controls and General Application Controls aligned with common regulations can now be automated. Real-time AI-driven event detection, triggered event correlation, preventive controls, and data-driven tests enhance the framework. Control objectives aligned with mother regulations support validation and assurance. Updated AI model governance principles allow support for production without expertise, acquiring test data during the normal course of business. As datasets accumulate, AI can evolve to operate with minimal specification. With present advances in AI and data-and-compute-storage cloud solutions, the necessary compute and storage resources are available to combine operation and audit seamlessly.
Compliance, smart manufacturing, governance, explainability, data privacy, data lineage, risk mitigation, model risk, continuous controls monitoring, anomaly detection, event correlation, supervision, testing, validation, assurance, mapping, pattern design, and validation cycle.
The paper does not list formal subject tags, but based on its content, the subjects covered are: AI-driven auditing, continuous controls monitoring, anomaly detection, financial compliance, smart manufacturing finance, event correlation, data governance, and regulatory mapping.
Precision at Scale: Intelligent Incident Tagging and Routing in Enterprise Service Management
In a typical IT service desk, the volume of incoming service tickets can exceed several thousand per week. Handling these tickets relies on an underlying classification or routing process that maps each ticket to qualified expert resources for resolution. The classification can be based on a predefined taxonomy, which provides a multi-level hierarchy of various types of IT service requests and distinguishes between incidents, problems, changes, queries, and other service requests. A traditional approach for ticket classification is based on manually crafted rules. However, such a rules-based approach is not always feasible, and classifications can frequently need to account for very rare or less frequently seen tickets. Even a rules-based classifier cannot always guarantee the effective usage of resources. Machine learning has increasingly been used for automating the classification operation and shifting the IT service desk to a model-driven setup, where the classical rules-based approach is enhanced by a learning-based operation that learns the mapping function. AI-driven intelligent classifiers can cater to frequently seen tickets and can learn either a one-vs-each approach for every type of service request or process the ticket in a multi-task learning setup.
Enterprise IT Service Management, ITIL, Ticket Classification, Supervised Learning, Semi-Supervised Learning, Self-Supervised Learning, Data Foundations for Classification.
Inferred from the keywords and content: The paper covers Enterprise IT Service Management, ITIL, Ticket Classification, Supervised Learning, Semi-Supervised Learning, Self-Supervised Learning, and Data Foundations for Classification. The discipline spans Computer Science, Information Systems, and Artificial Intelligence.
Cloud-Native AI Frameworks for Proactive Cybersecurity Threat Detection
Overreliance on signature-based sensor data, automation, and information sharing often enables threat actors to perpetrate cyberattacks such as ransomware, cryptojacking, and other undetected activities for months. Even without prior knowledge of an attack, security teams have the opportunity to locate advanced persistent threats that have already evaded existing security controls. Organizations must thus embrace threat hunting—proactively seeking active adversaries in their environments using tailored detection capabilities. Information technology teams operating in public cloud environments can now empower hunting by observing attack paths, developing detection capabilities in a serverless, on-demand, and cost-effective manner, and storing massive amounts of data in a cloud-native architecture. Threat hunting is defined as the hypothesis-driven exploration of an enterprise's environ­ ment to detect and isolate undiscovered malicious activity. The process relies on advanced knowledge of attackers and their techniques to create detection-focused intelligence that is then fed back into observability solutions. Conducting efficacious threat hunting involves defining clear objectives; selecting appropriate data sources, collection pipelines, and storage solutions; and developing detection capabilities that specifically target areas of interest. Despite the widespread adoption of threat hunting in traditional enterprise networks, cloud operating models introduce significant new characteristics and considerations that warrant development of a corresponding enterprise cloud threat-hunting framework.
Not formally listed. Based on the content, the subjects include: Cybersecurity, Cloud Computing, AI/ML-based Threat Detection, Anomaly Detection, Federated Learning, Serverless Architectures, and Threat Hunting.
Intelligent Predictive Decision-Making Framework for Autonomous Vehicle Navigation Using AI
Following a probabilistically-driven design philosophy, the proposed framework aims to address a pivotal aspect of real-time navigation for autonomous vehicles while constantly handling uncertainty—making predictive decisions in an uncertain environment. To this end, given a mapping of the environment augmented with statistical information for all key perturbations impacting performance during online navigation, the questions being tackled are: at each instant during online navigation (substantially constrained by latency requirements), what would be the preferred manoeuvre leading to the most favourable consequence (in terms of an appropriately defined cost function) within a defined disrupted neighbourhood of the current state? Would the definable manoeuvre, coupled with a meaningful understand of the environment at large within an horizon, guarantee that a reliable trajectory through the horizon could be sustained while dynamically adapting to key unexpected changes? To emphasise the persistent uncertainty within real-time navigation, performance is considered within a continuously dynamically evolving local environmental neighbourhood. The objective is to remain in a safe zone—away from obstacles, poor friction and slim clearance port holes—adapting the predictive decision at the end of a navigation latency to lead reliably through the next required safe region within the horizon while still reacting towards key determined perturbations. In probabilistic terms, this framework thus focuses on a novel predictive-decision scheme capable of overcoming: a unidimensional predictive approach under noise; noise constraints within a partial-observable and dynamic environment. A novel predictive-decision system bridging a low-cost inference engine and a key sensor redundancy has exhibited the capability to reliably adapt to a few sudden—and thus not directly observable—perturbational shifts.
Probabilistic Autonomous Navigation, Predictive Decision-Making Under Uncertainty, Real-Time Trajectory Planning, Latency-Constrained Navigation, Stochastic Environment Modeling, Cost-Optimized Manoeuvre Selection, Dynamic Safe-Zone Enforcement, Partial Observability Handling, Sensor Redundancy Integration, Low-Cost Inference Engines, Adaptive Horizon-Based Control, Uncertain Environment Mapping, Disrupted Neighborhood Modeling, Noise-Robust Predictive Control, Autonomous Vehicle Safety, Probabilistic Trajectory Reliability, Online Navigation Optimization, Reactive Perturbation Adaptation, Predictive Control Architectures.
The paper covers real-time autonomous vehicle navigation, predictive decision-making under uncertainty, probabilistic control, sensor integration, and data fusion.
Artificial Intelligence, Autonomous Systems, Robotics, Control Engineering, and Intelligent Transportation Systems.
AI-Powered Risk Classification and Pricing Optimization in Auto and Property Insurance
A unified perspective on risk stratification in insurance through the lenses of AI and premiums facilitates optimal risk-based pricing. Risk stratification improves upon traditional segmentation, notably via machine learning, in three areas: identifying and quantifying drivers of claims frequency or severity; reallocating risk among closed groups such that all members share risk characteristics; and estimating risk scores on individual customers based solely on their attribute values. Empirical studies show the added value for stratification of (1) modelling claims as a two-dimensional distribution, (2) incorporating policyholders’ driving behaviour, and (3) considering spatial factors, although data quality may determine the feasibility of particular enhancements. Whereas risk stratification is central to allocating premiums properly, premium optimisation considers the entire pricing and underwriting framework: rate-setting, discount/surcharge application, customer lifetime value, and churn mitigation. Joint consideration also allows dynamic pricing—adjusting premiums in line with changing risk over time—while taking precautions against unfair pricing. All formulation dimensions are amenable to algorithmic solutions. Unifying the two perspectives reveals a clear schematic of one’s research contribution, underlining that empirical applications in auto and property insurance harness AI-driven risk stratification within broader premium-optimisation frameworks. Successful projects yield novel insights and valuable lessons for future efforts. AI has opened exciting avenues for stratifying risk and optimally calibrating premiums in auto and property insurance. Risk stratification assesses underwriting risk for different groups and the entire portfolio, steering loss reserves and capital allocation. AI enhances traditional segmentation by identifying the main drivers of frequency and severity, reallocating risk among closed groups for equitable sharing of risk characteristics, and estimating risk scores for individual consumers—each with wider applicability. Empirical studies highlight the potential for two-dimensional frequency-severity modelling, incorporation of policyholders’ driving behaviour, and consideration of spatial factors, while underscoring the paramount importance of data quality. Promoting stratification typically directs research attention towards the technology employed, whether neural networks, external data enrichment, or other areas.
AI-Driven Risk Stratification, Premium Optimization, Risk-Based Pricing, Machine Learning in Insurance, Claims Frequency–Severity Modeling, Individual Risk Scoring, Closed-Group Risk Reallocation, Dynamic Pricing Strategies, Underwriting Analytics, Customer Lifetime Value, Churn Mitigation, Behavioral Telematics, Spatial Risk Modeling, Data Quality in Insurance Analytics, Algorithmic Pricing Frameworks, Fairness in Automated Pricing, Auto and Property Insurance Analytics, Portfolio Risk Management, Capital Allocation Optimization, Intelligent Underwriting Systems.
AI-driven risk stratification, premium optimization, machine learning in insurance, claims frequency-severity modeling, dynamic pricing, underwriting analytics, behavioral telematics, spatial risk modeling, and customer lifetime value.
Actuarial Science, Insurance Analytics, Artificial Intelligence / Machine Learning, and Financial Risk Management.
AI and Data Engineering Architectures Supporting Intelligent Public Sector Decision-Making
The demand for real-time evidence-based policy decision-making and management is driving governments to enhance their analytical capabilities through AI. However, actual AI uptake in the public sector remains limited. Current government ML models and innovations rely primarily on internal IT infrastructure and cloud-based platforms. This research outlines the AI and data engineering frameworks required to execute a future-ready government analytical agenda for smart decision-making. The analysis identifies three types of data pipeline architectures and the foundations of an integrated data ecosystem tailored to the specific characteristics of public sector data. These are combined with the essential requirements for data quality and governance, and different AI deployment models for PLG predictive and prescriptive analytics applications. Finally, seven use areas for healthcare, social services, urban planning, transport, crime and disaster response are examined. The resulting design delivers a comprehensive, objective, and evidence-based perspective on AI frameworks for real-time smart government. Despite practical implementation challenges, the recommendations align both with state-of-the-art AI developments and with the ML and AI strategies of important public and commercial institutions. The rapid development of analytics and AI technologies, combined with the capacity to harness the massive amount of data generated by public sector operations, unquestionably represents a significant opportunity for governments to transform their traditional ways of working. More than ever, there is a strong demand for real-time, objective, and evidence-based analysis to support the challenging decision-making environments created by the COVID-19 pandemic and other current global crises. However, in practice, only a limited number of governments have adopted a formal AI framework. Most AI innovations in the public sector remain isolated. Advanced ML models, especially deep-learning techniques, are mainly developed for specific applications, while broader ML initiatives are becoming more common, primarily driven by cloud-based platforms. At the same time, private organizations are increasingly offering prescriptive or predictive services to governments, filling in the gaps in their analytics capabilities.
AI for Smart Government, Public Sector Analytics, Evidence-Based Policy Making, Real-Time Government Decision Support, Government Data Engineering Frameworks, Public Sector Data Pipelines, Integrated Government Data Ecosystems, Data Quality and Governance
The paper's own keywords cover its subject matter: AI for Smart Government, Public Sector Analytics, Evidence-Based Policy Making, Real-Time Government Decision Support, Government Data Engineering Frameworks, Public Sector Data Pipelines, Integrated Government Data Ecosystems, Data Quality and Governance, Machine Learning in Government, Predictive and Prescriptive Analytics, Cloud-Based Government AI, Digital Government Transformation, AI Deployment Models, Health and Social Services Analytics, Urban Planning Intelligence, Smart Transport Analytics, Crime and Disaster Response Systems, and Government AI Strategy.