American Data Science Journal for Advanced Computations (ADSJAC)
The American Data Science Journal for Advanced Computations (ADSJAC) is a quarterly, peer-reviewed, open-access journal publishing original research, review articles, and case studies in data science, artificial intelligence, machine learning, computational intelligence, and advanced computing. The journal serves researchers, academics, and industry practitioners worldwide and uses a double-blind peer review process to evaluate every submission. ADSJAC charges no article processing fees and publishes all accepted content under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, ensuring immediate and permanent open access. The journal is published by American Data Science Journal for Advanced Computations and is committed to the publication ethics standards set out by the Committee on Publication Ethics (COPE).
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Artificial Intelligence (AI) is at the forefront of a new age of service intelligence, merging analytics, Machine Learning, and Natural Language Processing tools with wide-ranging data sources to enhance the customer experience throughout a service life cycle. AI enables data-centric approaches that supersede human-centric ones in many aspects of customer experience—from personalization at scale, through the proactive detection and resolution of potential issues, to automated provision of recalls. There are two significant needs in capitalizing on these opportunities. The first is the development of service intelligence indices, which define a maturity level for service intelligence by aggregating performance objectives that span trusted data and AI platforms, provisioning and support toolsets, and process governance. The second need is the adoption of appropriate deployment patterns, which guide practitioners in implementing service intelligence in incremental, actionable stages. The growing, strategic importance of customer experience has motivated the research. This work investigates how AI-driven service intelligence enables an enhanced customer experience and defines a structured approach to supporting practical deployment. Initial reflections from a large telecommunication uses case provide illustrative insights.
Copyright (c) 2024 Dileep Valiki (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Collateral Mesh for Multi-Party Derivatives
Automated, agent-based, enterprise orchestration of cloud-native financial systems according to regulatory frameworks, operational governance policies, business imperative, and risk considerations for enterprise accounts and transactions with multiple counterparties. The study lays the foundation for cloud-native, agentically-controlled, orchestration solutions focusing on the complex set of risk, collateral, and accounting subproblems emerging from the multi-counterparty nature of DvP transactions for traded derivatives portfolios. A design approach is proposed, enabling the configuration of the governing control and decision criteria, supported by a proof-of-concept architecture. The solution design is supported by commercial and open-source domain technology patterns with proof points in reducing technical debt. The traded derivatives portfolio serves as an example. Although not exhaustive, achieving the stated objectives, guided by the established principles, and responding to the defined requirements and questions for this set of problems advance the field of cloud-native financial systems toward an operational state. Incremental deployment is possible, enabling deployment and operational teams to identify milestones and roadmap the full range of engineering, operational, and organizational work required to realise the complete cloud-native DvP, trader, and transaction management solution.
Copyright (c) 2026 Nareddy Abhireddy (Author) https://creativecommons.org/licenses/by/4.0
Neural Graph Retrieval for Intelligent Enterprise Agents
Graph-Enhanced Retrieval-Augmented Generation (Graph-RAG) and contextual architectures for large language models (LLMs) deliver performance, reliability, and governance for enterprise applications. Graph-enhanced RAG addresses neural retrieval deficiencies within enterprise chatbots, question-answering systems, and service automation agents. A contextually aware agent-centric architecture combines knowledge graphs, domain ontologies, and personal models to ensure contextual accuracy and suitability for the use case. In contrast to standard RAG augmentation, application-specific data and reasoning mechanisms are connected through prompt engineering, plugins, and external tools. Evaluation methodologies for context-aware agents include dedicated benchmark suites and task-centric quality metrics. Enterprise deployments of RAG and large language models require performance, reliability, and governance. LLM generative capability is appealing for data indexing tasks like customer support chat interactions, service delivery, and knowledge management. However, the neural retrieval component of RAG suffers from novelty, specificity, and appropriateness issues that impact overall user experience. Scaling chatbots to handle repetitive queries created by less elaborate agents leads to a reduction in customer engagement. Operator confidence decreases further when these systems fail to escalate correctly when faced with complex problems.
Copyright (c) 2025 Mallesham Goli (Author) https://creativecommons.org/licenses/by/4.0
Adaptive Intelligence Layers for Real-Time Derivative Exposure and System Cohesion
Many aspects of derivative-processing and risk modeling systems remain poorly integrated. In response to this gap, an architecture is developed that ingests real-time market data, cognitive and generative models, and neural decision engines to provide on-the-fly pricing, P&L, margining, credit risk, and VaR calculations. An extension determines the level of collateral to allocate against potential future exposure, optimally fulfilling both central clearing and regulatory liquidity mandates. Data controls ensure quality and trustworthiness, while risk-aware deployment provisions enable a seamless integration of cognitive technology into an existing ecosystem. Rapidly changing markets and an increase in extreme events require more responsive risk processes. While artificial intelligence (AI) methods facilitate faster data preprocessing, cognitive technologies allow operational decisions to be made at the same time as bank risk changes. Yet, in many cases, and especially at a bank-wide level, these developments are not being exploited: pseudo-real-time market and credit risk numbers are still the norm. A cognitive AI framework that supports real-time processing of derivatives and associated risk metrics meets this need.
Copyright (c) 2025 Dhanaraj Sathiri (Author) https://creativecommons.org/licenses/by/4.0
Cognitive Digital Fabric for Autonomous Healthcare and Financial Intelligence
Healthcare and financial ecosystems provide critical information for individuals, communities, and governments. The intelligent processing of this information is essential for improving efficiency, reducing costs, and creating value. Digital Fabric, an intelligent real-time Digital Fabric for autonomously learning and generative systems, is designed to support these ecosystems. As complex systems dealing with the virtually continuous flow of high-volume data, healthcare and financial environments are ideally suited to digital twins to develop AIs for standard decision frameworks that relate to clinical and financial conditions. The internal lifecycle of AIs requires adaptive engines which include the needs for new data, for checking, updating, retraining and deploying models, and convolution operations with other available models. As autonomous learning manages the phase shift of the digital twins, a process to aggregate detected problems of an AI from different users over time into a generative learning request improves multilingual understanding, so increasing the number of use cases and the cohort for language models for Kolmogorov-Zasanenko complexity. Data processing capable of real-time cloud native operations on the public-cloud infrastructure of any provider is supported. Indeed, processing capability in the Azure ecosystem has been developed on the two domains Digital Fabric services. These exploits of Microsoft Copilot functions are collaborated on these services.
Copyright (c) 2025 American Data Science Journal for Advanced Computations (ADSJAC)
Governed Agentic RAG for Secure Enterprise Automation
Automating enterprise operations offers the promise of substantially increased agility and competitive advantage. However, enterprises are still constrained in their ability to leverage such innovations for many operations beyond routine transaction processing. Security and oversight requirements ordinarily preclude the deployment of advanced AI techniques in data-rich enterprise context. Recently proposed retrieval-augmented AI designs provide a means to address some of these limitations, enabling formal institutions to enforce policy and mitigate risk, even when operations require complex reasoning and data synthesis. Yet no formal framework for retrieval-augmented multi-agent systems exists. Such a framework is necessary to support comprehensive validation and evaluation of emerging systems while also guiding practical implementations. Providing such an underpinning constitutes the principal contribution of this work. The contributions of this study formalize the design of retrieval-augmented multi-agent systems. A foundation architectural model is defined, linking agent modularity, open interfaces, and scalable operation to retrieval-augmented operation. Grounding the architecture for enterprise contexts supports a comprehensive theory of retrieval-augmented multi-agent systems. Security and governed operation are addressed via consideration of (1) agent role definitions; (2) data access and provenance management; (3) strategic behaviour, information source maintenance, and understandable queries. Together, these aspects provide the procedural scaffolding necessary for scientifically-grounded operational and performance evaluations, endorsing the continued development of novel retrieval-augmented multi-agent systems for enterprise automation.
Copyright (c) 2026 American Data Science Journal for Advanced Computations (ADSJAC)
Reimagining Financial Systems Through Adaptive Automation
Next-generation financial Cloud systems must meet the automation, operational pacing, and rapid-market-change demands of the digital economy, which cannot be supported by traditional automation paradigms. Intelligent automation, which employs appropriate AI technologies for different automation decision categories and business processes, is a concept with unprecedented potential. Seen as an enabler for next-generation financial Cloud systems, three critical aspects need serious consideration to seize this opportunity: the intelligent automation of internal operations, the governance and compliance of intelligent automation at a system level, and how companies can incrementally build intelligent automation capabilities. The first two aspects interact because the business case for intelligent automation is built on stakeholder demand for cost reduction. Filling gaps in these two areas provides a foundation for a roadmap of maturity phases or increments. Primary examples of intelligent automation in financial operations research illustrate its industrial relevance.
Copyright (c) 2024 American Data Science Journal for Advanced Computations (ADSJAC)
Latent Risk Discovery in FHIR Networks Using Graph Clustering
Graph-driven risk modeling for healthcare interoperability is framed, enabling damage- and cost-related risk-indicator estimation through currently-available FHIR data. Risk scenarios focus on the patient’s exchange of health information across systems and the harmful consequences of improperly managed sensitive data during transfer. An extended FHIR interoperability-oriented usage graph serves as a primary substrate supporting the risk model. The introduced concepts are assessed with a combination of unsupervised learning and risk-modeling techniques across multiple stages. Results confirm the applicability and implications of the risk analysis. Risk indicators facilitate a strong-data-weak-graph notion adding to traditional strong-graph-weak-data arguments. Unexpected or undesired events—such as system failures, sensitive-data exposure, or privacy violations—occur during the exchange of electronic health information and are subsumed under the notion of risk. Risk is a practical issue in healthcare interoperability that relates to safety, quality, cost, and patient trust. A graph-driven framework extends the two-tier determinism for interoperability analysis and supports the identification and quantification of risks even with scarce or unbalanced data. The approach enables the modeling of graphs that exploit currently-available Fast Healthcare Interoperability Resources data and provide risk indicators related to data-exchange tasks. Concepts are put into practice in two scenarios. The first analyzes the readiness of U.S. healthcare system Electronic Health Record vendors to facilitate the realization of the 21st Century Cures Act. The second investigates risks related to illicit use of health information during data transfer.
Privacy-Preserving Federated AI for Customer Intelligence
Cross-enterprise customer intelligence analytics seek to discover valuable insights from secure collaboration between multiple enterprises, although sensitive business data and competing interests often restrict data sharing. Federated AI enables distributed machine learning without sharing models and supporting inferencing or prediction tasks involving the aggregated intelligence of isolated, possibly heterogeneously governed datasets. The applications are crucial for risk control analytics or enriching data-driven decision systems, while cooperation can be organized in a formal consortium or on an ad-hoc basis. Federated AI offers several potential benefits: it minimizes the issues associated with cross-enterprise data sharing and overcomes the limitations of local analysis. Nevertheless, it also introduces new privacy, security and system-level threats, such as data leakage via model sharing or model inversion and membership inference attacks against sensitive model information. Exploring the federated AI paradigm for cross-enterprise customer intelligence analytics is essential for determining its applicability, identifying missing privacy-preserving mechanisms and addressing latent security risks. These aspects can thus be analysed and adapted into a privacy-preserving security-enhanced federated AI platform proposed through design science research. Realizing and endorsing the potential advantages in a production environment, however, requires further exploration.
Multi-Agent Decision Automation in Enterprise Systems
Enterprise processes encompass non-obvious repetitive decisions that affect key performance indicators, and should therefore be analysed for automation using formal or semi-formal representation and reasoning methods. The focus is on high-level workflows comprising consistent high volumes of low-level anti-patterns, and not on semantic business process definitions. Their automated multi-agent realisation relies on specialised agents implementing the Anti-Pattern Detection System and Anti-Pattern Automation and Resilience System patterns. A generic multi-layer orchestration architecture integrates probability-based risk assessment with service-state representation in a hybrid Service-Oriented Architecture. Empirical evaluation examines the applicability for massive deployment in enterprise systems. Recent developments in autonomous AI and Computer Games research have demonstrated the capability of state-of-the-art techniques to realise common, low-level responses to non-obvious enterprise decisions. However, such solutions are yet to achieve autopilot orchestration-free deployment, increasing their potential for scalability and leverage of private or non-sensitive data. Automated task execution for highly-frequent, low-cost actions may not be needed but can create additional business value through risk-weighted selection. Artificial Agents can provide consistently reliable success rates for these Anti-Patterns in Enterprise Business Operations, enabling formalised real-world testing and risk quantification in production environments.
Copyright (c) 2023 American Data Science Journal for Advanced Computations (ADSJAC)
Elastic Cloud Reservoirs in Industrial Analytics
Cloud-integrated data lake architectures that enable exploratory production data analytics offer an efficient and flexible approach to mining data for new patterns and insights that can drive improvement. Such discoveries can enhance production quality and performance, facilitate predictive maintenance, or even support decision-making automation through AI and machine learning. Concrete ideas for supporting production analytics in a smart factory environment are provided, along with a cloud-enabled data lake reference architecture for manufacturing environments. The core components, data ingestion and integration pipelines, storage strategies, and modeling considerations are considered in the context of exploratory production analytics use cases focused the mining of production data for anomalies and failure patterns. Together, these considerations can help practitioners realize new value from cloud-integrated data lake architectures and services. However, moving production data to the cloud introduces new challenges that must be faced to make the analytics-ready data lake a successful reality. Enabling real-time analytics requires consideration of end-to-end latency, data freshness, and supported analytics features. From a data governance perspective, novel approaches to policy definition, access control, and proof-of-compliance must be developed. Even with the right design, the sensitivity and business-critical nature of production data mean that its movement must be carefully monitored, including the prevention of AET loss and potential data inconsistency during sync processes. For mission-critical decision-making, the automated orchestration of the entire pipeline chain is also essential.
Compression Strategies for Nation-Scale Mobility Data Vaults
Most national organizations are establishing national platforms to collect, analyze, and share transportation data. Although data storage is a critical component, it often does not receive the same level of attention as other aspects. This paper addresses big data storage optimization techniques for national transportation data platforms. It examines the data ecosystem of a hypothetical national platform and details the types of anticipated data storage optimization. The research identifies relevant techniques, summarizes representative configurations, and discusses quantifiable effects in existing platforms. National platforms enable data-driven insights for public and private sectors by offering a unified environment for transportation analytic applications based on data fusion—combining multiple data sources and types into a single data set for analysis. Big data technologies are essential for realizing these platforms, as they require significant computing and storage facilities to process large volumes of heterogeneous data that arrive at high velocities with the potential for high veracity. Even though storage systems are a key component, comparing large-scale storage architecture and optimization techniques remains largely unexplored. Such analysis is particularly important for national platforms because transport constitutes a basic national infrastructure, and disruption and degradation will incur heavy economic losses. Without cheap access costs, deployment design decisions cannot be met.
Generative AI systems, capable of producing coherent, contextually relevant text, images, and other media from prompt inputs, have become increasingly accessible. The potential for Generative AI to improve patient care and clinician efficiency has generated considerable interest in the healthcare sector, focusing on the enhancement of clinical documentation and patient engagement processes. The use of Generative AI in these domains is discussed with a focus on the underlying technology, implementation considerations for healthcare organizations, and case studies demonstrating the effectiveness of Generative AI in real-world deployments. Automated generation of clinical notes based on free-text summaries, unstructured summaries of patient examinations and assessments, or conversational inputs is explored, along with the code-based structuring of free-text notes and the application of standardization templates to ensure compliance. The generation of patient education materials appropriate for health literacy levels and cultural backgrounds, the scheduling of appointments, and the triaging of patient queries using Generative AI are also covered. Ethical considerations—especially with respect to data governance and the potential for biased, adversarial, or inaccurate output—are flagged throughout, along with the importance of establishing and maintaining high-quality workflows for the use of Generative AI services.
Accountable Intelligence in Tax Administration
Artificial intelligence (AI), mixed with advanced big data analytics methods, has the potential to play a pivotal role in enhancing efficiency and accuracy in multiple tax processes, from data management and fraud detection to pricing optimisation and performance assessments. However, the use of these new technologies must respect the ethical principles of transparency, accountability, and explainability in order to secure their acceptance by both internal and external stakeholders, ensure compliance with increasingly stringent financial regulations and well-founded tax decisions, and ultimately facilitate a more equitable tax regime. A Responsible AI framework is articulated for an area typically overlooked in the discussion of the ethical use of AI in other fields: its application in government analytics and compliance systems for tax agencies, natural and legal persons subjected to taxation, and internationally coordinated data-sharing agreements. These institutions frequently combine their own datasets with information sourced externally through digital interceptions, companies and organisations mandated to carry out withholdings or disseminate internationally belonging to third parties, data of a fiscal nature of a different nature that guarantees compatibility in the absence of a tax treaty, and other types of financial data. Given the sensitive nature of tax-administrative data and the legal obligations in force for tax agencies, the AI systems developed should respond adequately to the three dimensions of Responsible AI: securing ownership of data and results; avoiding biases during the design stage and in the response stage; and guaranteeing control and security of the systems' outputs.
Real-Time Compliance Pattern Intelligence
Research focusing on tax compliance investigations is often anecdotal. In this study, data from the Thai Revenue Department's electronic tax filing system are used with pattern recognition techniques to examine the compliance behavior of both businesses and individual income tax filers. The empirical investigation seeks to identify patterns of behavior and any other anomalies. Importantly, the investigation is not undertaken to explain causes and consequences of tax compliance but simply to provide a framework for investigation and a database based around a pattern recognition analysis. The analysis reveals clear patterns of compliance behavior within the two data sets, although the data from businesses is more consistent. Anomalies and irregularities are also evident, often recommending further and deeper investigation. Such an approach to tax compliance investigations provides broad and objective insight into typical behavior, while highlighting specific sets of records that require further investigation. Although these insights into the compliance behavior of Thai tax filers are somewhat limited, they nonetheless have potential value for the country's tax administrators. The framework can, however, be deployed in more general research into electronic tax filing compliance and enables the recognition of behavior that is not only anomalous but also worthy of further scrutiny. This enables, and possibly improves, resource allocation by tax authorities seeking to maximize the benefits from compliance investigations while minimizing the costs.
Over the past decade, claims processing has emerged as a primary area of interest for insurers exploring how artificial intelligence (AI) can reduce costs or enhance service quality. Several factors motivate this focus. First, processing claims represents a major operational expense—one that, if reduced, is likely to translate into greater profitability. Second, claims processing often significantly influences the customer experience and may even determine clients’ future purchase decisions. Finally, AI technologies tailored for claims processing have demonstrated the ability to deliver faster settlement of smaller claims without sacrificing accuracy. Provided that AI is applied within a carefully monitored, ethically governed framework, its integration into claims processing represents a genuine win-win opportunity. But increasing the efficiency of claims processing is only half the story. Unless these savings translate into improved customer experience and satisfaction, the long-term gains may be illusory. Customers today demand – and expect – faster claims settlements. Organisations that can consistently meet these expectations are likely to enjoy a competitive edge, gaining not only customer satisfaction but also long-term loyalty. Conversely, companies that fail to deliver speedy settlements will, over time, see an increased rate of customer attrition, particularly among their high-value clients. Insurers that can harness new AI capabilities to manage these twin objectives – faster settlement with greater consistency – stand to capture both the operating and customer experience benefits.
Predicting Loan Defaults with Machine Learning
The growing importance of machine learning in decision-support systems has spurred substantial research into applying machine learning models to data from multiple sectors. Available open-source data on loan defaults has been used to train a variety of machine learning models from statistical models to complex neural networks. The performance of the models is evaluated using various information-theoretic and statistical measures. In addition to a detailed evaluation of the models, several elements associated with their practical adoption in a bank have been presented. Machine learning algorithms are now widely used and external advances in these methods have come, in large part, from academic research. That said, the focus for these models is now shifting to practical elements such as deployment, interpretability, regulatory compliance and operational aspects. To this end, data publicly available from the Lending Club, a leading US provider of loans to individuals and small businesses, has been used to develop different machine learning algorithms for prediction of the default of loans. The deployed solution uses tree-based methods both for interpretability as well as compliance with regulations.
AI-Driven Behavioral Analytics for Smart Payment Authorization in Cloud Finance
Cloud-based platforms are increasingly incorporating Payment as a Service (PaaS) that empowers various organizations to swiftly offer payment services to their end users—most in a win-win manner. However, the growing violence and higher money flowing into crime severs the win-win situation and reduces the reception by the organizations. Payment transactions can never be completely anonymized. To keep the cloud-based transaction services flourish in the ecosystem, a technology that tracks criminals’ abnormal behaviors and predicts potential attacks before they happen is essential for sensitive, high-value, and risky transactions. Protecting the merchants and users by incorporating intelligent, machine learning (ML)-powered behavioral analytics for real-time authorization of sensitive transactions is a crucial and hot research topic; and their integrated receiving and sending decision policies should be taken care of in a balanced way. To meet these needs and offer intelligent risk sensing and scoring services, the integration of denser behavioral signals in addition to the transactional and contextual ones is necessary. Various machine-learning-anomaly-detection algorithms are compared to select the most suitable model. Moreover, denser behavioral signals are searched and experimented with to attain ML models of higher accuracy. The normal classes are defined by both supervised learning for benign actions of authorized users and by semi-supervised clustering with a few labeled instances only for malicious actions of non-users. The models are label-free in the testing phase. Enriching high-risk monetary transfer authorization with intelligent decision-making is accomplished by ML-based risk scoring, where pleasing sensitivity and specificity are achieved through careful calibration of the risk scoring threshold.
The growing volumes of payment transactions have placed significant burden on payment service providers or financial institutions to evaluate the risk profiles of customers swiftly. Various risk-related metrics of customers can be obtained from the data related to their payment transactions, and the recent development of machine learning-based modeling approaches allows these metrics to be modeled as a risk score. However, the shoe has on the other foot as providing such services has become a competitive game between players leading to the demand for near-native response time in serving customer requests. The services, therefore, require a cloud-native big data architecture where the data generation, ingestion, processing and modeling operations can be carried out at huge scales and low response and lead times while integrating the required principles of data governance and privacy. Cloud-native IoT systems collect vast volumes of data daily that forms the data lake for processing. Data processing includes both batch and event-driven processing. Batch processing is used for generating risk-related metrics where as event-driven processing is used for carrying out risk scoring. The ML models behind risk metrics are provided as services and lifelong learning is done by using CI/CD pipelines. Firewall and observability adopt a zero-trust policy to promote not just security but also privacy and governance compliance. Robust architecture has enormous readability and scalability features for tackling such diverse services. The entire architecture is illustrated with an example in the domain of payment service providers using a risk scoring system proposed in the previous work.
Historical Data as the Foundation of Clinical Foresight
Clinical Decision Support systems based on historical patient data represent a model for investigation of a category of Clinical Decision Support (CDS) applications that have recently gained traction — consumption of historical data to support clinical decision making. An overview of the category is provided and addressed based on five research questions: What are the properties of historical data that provide insights? How are these insights used in Analytical Methods for Clinical Decision Support based on Historical Data? How is the architecture of the Clinical Decision Support System represented? How is clinical validation and evaluation achieved? How is the presentation and user interaction with predictions controlled? Despite being a nascent area, numerous published works are available on the topic, and clinical validation efforts are in various stages of maturity. Historical data have become increasingly more available, are generally regarded as safe, and, unlike predictive models constructed on Future Data, do not raise questions regarding biases introduced in the construction of supervised learning models. However, while safety and bias concerns are reduced, historical data are prone to their own logical shortcomings. Therefore, users must exercise caution when interpreting the insights derived from Historical Data Analytics. Properly defined and presented, such insights do not impose a decision making burden on clinicians but instead ease the decision making process. The existence of a predictive model that maps the Clinical Data relevant to a Clinical Decision to the Clinical Decision itself is often regarded as a requirement for CDS to be genuinely useful for everyday clinical practice. Such a statement suffers from circular reasoning. In summary, because no method of construction of predictive models can claim to be free from bias, the pannational nature of probability suggests that, provided sufficient Causally Uncorrelated Data points are available notwithstanding time, space and biological differences, the possibility of identifying and using a Supervised Learning Model that accurately predicts the Decision of a Clinician can not be excluded.
Automated Compliance Sampling via Expert-Rule Orchestration in Audit Environments
Automation of Audit Sampling Using Rule-Based Decision Systems: an objective, scholarly analysis of automated sampling approaches, evidence-based assessment, and formal structure. Automation and computerization in auditing are ubiquitous and offer unparalleled assistance to auditors in improving efficiency, effectiveness, and overall cost. Audit sampling is an effective means to make audit decisions based on part of the evidence rather than the whole. Rule-based decision systems are popular in many business and accounting areas, but not widely deployed for audit sampling yet. Audit sampling can be automated by creating sampling rules based on audit data, professional guidelines, and/or judgment. Such rules specify sampling conditions and thresholds, the population elements that trigger the rule-set, the sampling specification produced, and the sample sizes required and whether the auditor should consider additional information of other related decisions. The approach also supports a human-in-the-loop function, providing the auditor with automation assistance but allowing judgment to deviate from the rules. The extent of automation can vary to meet auditors’ needs and is not limited only to the mention mode of rule-based systems. Effectiveness and efficiency can be evaluated in terms of accuracy, coverage, false positives, false negatives, processing time, and resource utilization
Demand Chain Intelligence Rethinking Flow in the Age of Data
Demand for supply chain optimization through industrial data analytics has surged, fueled by lessons learned during the COVID-19 crisis. A systematic review of methods applied in practice in 2021 summarizes how data science and operations research techniques are being adopted to tackle key issues. The review follows the ground-up flow of data in a typical supply chain. Publications describe predictive maintenance; end-to-end supply chain visibility; and inventory optimization. The data sources and processes for these analytics are also considered, echoing the importance of data quality and integration. Organizations are investing in technologies and competencies, yet progress remains slow. Acknowledgment of data quality dimensions is essential to achieve reliable models, yet limited. Moreover, a lack of interoperability hampers integration across factory-level operational systems and, consequently, impact on global visibility. These observations highlight the gap between the theory and practice of supply chain optimization. The study makes a threefold contribution to operations research and data science. It analyzes how pioneering companies are leveraging data science for supply chain optimization, synthesizing methodological applications within the identified analytics. On the data side, it discusses the sources that feed manufacturing and logistics operations, emphasizing critical dimensions of data quality and the requirements for integration across distinct analytical domains. Finally, it translates this body of knowledge into practical insights for managers.
Neural-Augmented Resilience Mapping for Multi-Tier Automotive Parts Networks in Smart Manufacturing Eras
Digital transformation in automotive supply networks—typically characterized by Just-in-Time principles and high-risk vulnerability to shocks—enables predictive logistics: a predictive-prescriptive-optimization-enhanced real-time decision-support paradigm spanning demand forecasting, inventory optimization, transportation planning, and capacity network design. Supply networks supporting Industry 4.0 detect, analyze, and respond rapidly to changes, disruptions, and stresses, in the process minimizing total cost of ownership while improving lead-time and service-level targets predicted with a digital twin. Data ecosystems capture, aggregate, analyze, and disseminate data streams generated by a network’s partners employing standardized discrete-event, agent-based, and machine-learning models, deployed within ultra-responsive edge cloud and fog architectures. Despite long-standing research interest, relatively few predictive-logistics applications exist, signaling a gap that demands investigation. Evidence presented here addresses an objective defined through literature synthesis: Explore Industry 4.0 drivers and constraints while assessing the impact of emerging Industry 4.0 capabilities on predictive logistics for distributed automotive supply networks. Key findings illustrate the performance benefits of predictive-logistics in two empirical cases describing automotive semiconductor supply chains and electric vehicle battery and component logistics.
VaultMind Fusing Cloud-Native DevOps Pipelines with Generative AI Threat Intelligence for Next-Generation Payment Security
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.
Smart Fraud Analytics in Modern InsurTech Using a Hybrid Generative and Agentic AI Architecture
In a hybrid generative and agentic artificial intelligence architecture, generative AI generates fraud-detection signals based on historical events while agentic AI provides decision-suggestion commands to the human stakeholder in-the-loop. This synergistic approach overcomes the limitations of pure generative and agentic frameworks in detecting fraud patterns in modern InsurTech platforms. Rapidly growing digitalization and increased dependence on digital channels increase the susceptibility of insurance platforms to behavioral fraud. Advanced technologies such as AI and Machine Learning (ML) have the potential to mitigate the risk of billions of dollars lost through these frauds. To harness the power of both generative and agentic AIs, a novel hybrid system is proposed. Key stakeholders of the system include the InsurTech venture for which the system is being built, customers of the insurance platform, the development team, and the operational team responsible for maintaining and managing the AI pipelines. Data aspects of the architecture encompass novel feature-engineering techniques, a sophisticated data-pipeline approach, and an incremental learning mechanism to prevent performance deterioration due to concept drift. An unsupervised-ML-based segmentation model has been instantiated for the generation of training datasets and enables scalable continuous-signal generation in a real-time production environment. The quality of signals in the fraud-detection domain is not always suitable for direct operationalization. Hence, the operational design leverages the hybrid nature of the architecture by enabling human evaluation of the suggestion signals in a real-time mode. The primary fraud-detection application of the system is claims related, although it has the capability to generate fraud signals for policy underwriting, channel fraud, and behavioral fraud use cases as well.
Intelligent Healthcare Data Architectures: Combining Big Data, Machine Learning, and Precision Medicine
Progress in big data architecture and machine learning are enabling the deployment of next-generation healthcare data platforms, which facilitate the transition from traditional healthcare to precision medicine in both clinical and research settings. Technical performance, clinical outcomes, and economic viability together influence the adoption of these platforms by hospitals and health systems. A big data architecture facilitates the ingestion and management of diverse and complex data sources—both structured and unstructured—at scale and with high quality. Advanced analytics and machine-learning models further enable the application of predictive, precision, prescriptive, and causal analytics across a range of clinical needs. Novel applications of healthcare data in precision medicine include genomic data integration and cohort-specific variant interpretation, pharmacogenomics for personalized drug therapies, and the use of real-world evidence for the design of clinical trials. For cancer, platforms align genetic and genomic data with therapy information and treatment outcomes. Cloud-based solutions are also deployed in population health and biobank initiatives with a focus on COVID-19. Essential operational considerations for platform engineering are outlined, including infrastructure scaling, observability of data pipelines, quality assurance frameworks, and security and compliance controls. Advances in healthcare data also raise important ethical, legal, and social implications, particularly around patient privacy and the governance of artificial intelligence.
Agentic AI-Driven Customer Experience Orchestration for Enterprise Data Center Service Ecosystems
A smart city is a complex ecosystem powered by various Information and Communication Technology (ICT) systems. Rapid urbanization poses challenges for city officials. Smart technology adoption helps enhance customer satisfaction, provide better services, and optimize resource utilization. Interconnected systems enhance the citizen experience. City services use multiple, dispersed ICT systems. These result in the silos of innovation problem: new technologies improve a specific service without accounting for cascading effects on others. The outcome often resembles a pandora’s box. Intelligent systems capable of autonomous operation help tackle this challenge. Agentic Artificial Intelligence (AI) can be defined as smart, autonomous agents endowed with the ability to make decisions on behalf of users to accomplish set goals, working in harmony with other agents to satisfy all users. Agentic AI drives new customer experience orchestration in enterprise data center service ecosystems by holistically managing customer experience across all components, services, and service channels. Service Orchestration is the automated end-to-end orchestration of services, where multiple, often cooperative, services deliver a related service for the customer. Agentic AI enables a company’s service ecosystem to Learn and Adapt (L&A) and Satisfy and Delight (S&D) customers. The digitally transformed ecosystems of enterprise data center services satisfy customer preferences and evolving expectations, including real-time L&A and S&D for customer service interactions.
AI-Powered Data Engineering Solutions for Real-Time Fraud Prevention in Banking
Fraud prevention poses difficult challenges for retail banks, particularly for cross-border and cross-channel transactions where data tend to reside in silos. AI detection models are being attacked, and decision systems are subject to adversarial data. AI-powered data engineering is essential for ingesting, processing, and distributing risk signals with minimal latency, supporting the use of longer windows of historical data and the fusion of signals from multiple products and channels. Timely data quality and availability are determining factors in maintaining detection quality and accuracy. Recently reported detection and mitigation tactics, including model poisoning, retraining data poisoning, and feature value manipulation, should receive close attention in detection system design. Fraud prevention in retail banking typically involves detecting transactions that do not match a customer’s usual behavior or transactions that are dodgy in nature. The signals for detection usually come from behavioral analytics, anomaly detection, and cross-entity analysis. Such detections of fraud are often an obvious requirement, especially for transfer-based money laundering, but also for other fraud types. With money laundering being a major priority for regulators in many jurisdictions, banks have to conduct automated KYC (know your customer) checks, including for transactions beyond a preset threshold.