International Journal of AI, BigData, Computational and Management Studies
High-Throughput Sanctions Screening Using Event-Driven Micro-services in Financial Institutions
In today's complex global financial environment, cross-border transactions, and constantly changing sanctions lists, compliance with sanctions screening has emerged as one of the most important compliance tasks for financial institutions. Third parties (including regulatory agencies like the OFAC, FATF and EU) have very strict requirements regarding screening of customers, payments and transactions against sanctioned entities, as well as PEP and restricted party lists. Traditional architectures for sanctions screening are generally monolithic and process batches of data, which can become cumbersome and inefficient as they scale up, have elevated latency, low resilience to failures, and inadequate ability to adjust to regulatory updates. These constraints are particularly significant in high-volume banking environments, both with regard to transaction throughput and efficiency of compliance. In this paper we propose an event-driven micro-services approach for high throughput financial institutions' sanctions screening. The suggested architecture splits up sanctions screening into independent scalable services that are connected by event-streaming infrastructure. Core services are transaction ingestion, sanctions list synchronization, fuzzy matching, risk scoring, decision orchestration and alert management. The architecture is designed to provide high throughput, low latency, and fault tolerance for real-time screening, thanks to the use of asynchronous messaging, distributed processing, and dynamic scaling. It covers the basic elements of architecture such as message brokers, stream processing pipelines, distributed databases and intelligent matching engines. The screening pipeline utilizes advanced matching techniques, including Levenshtein distance, phonetic, token-based and machine learning–assisted ranking, to enhance the accuracy of screening and minimize false-positive results. The event-driven model allows for seamless horizontal scaling, independent deployment, and resilience when transaction loads are high. Experimental results show significant screening effectiveness improvements. The proposed system not only reduces the transaction screening latency but also boosts processing throughput and enhances the screening accuracy with fewer false-positive alerts generated than conventional monolithic systems do. The findings show that event driven micro-services architectures are scalable and resilient solution for regulatory compliance in modern Banking system. The study advances the field of financial technology architecture by introducing a scalable and compliance-oriented sanctions screening system that can be adopted in today's digital banking landscape. The results show that in financial institutions, the implementation of event-driven micro-services has a significant impact on operational efficiency, risk mitigation and the responsiveness to compliance.
Programmatic Governance using Policy-as-Code and ML for Dynamic Compliance Enforcement
In the intricate world of laws and regulations we have today, companies require more than just non-flexible guidelines to be on the right side of the law. They require systems that are not only adaptable but that also change with them in real time. This document investigates a futuristic programmatic governance way of handling compliance issues by combining Policy-as-Code (PaC) and Machine Learning (ML). The core of the idea is in the impairments of manual policy enforcement and rigid compliance checks that are always behind in changes of business operations or regulations. Organizations turn policies into executable code so as they can automate enforcement in distributed environments, thus achieving both uniformity and accountability as a result. Moreover, when combined with ML, these machines become capable of learning from previous compliance behavior, anticipating future violations, and even suggesting the necessary preventive measures. The approach proposed in this study consists of the following steps: writing the policy with a declarative language such as Open Policy Agent (OPA), linking the policy to an event-driven architecture, and allowing ML models to receive and analyze the data in real-time, be they anomaly or risk cases. The main feature of this work is the presentation of an architecture where cloud-native is integrated with dynamic policy engines and ML classifiers, enabling organizations to respond to compliance drifts as they happennot after. The architecture demonstrated in a multi-cloud case shows how it identified access control violations and went ahead to change settings automatically without requiring the involvement of a human. The findings, thus, indicate faster response time, shrinkage of compliance gaps, and notable cost savings as compared to the traditional governance models. This is, in fact, a very strong argument for using a live, learning compliance infrastructure that can not only adjust itself to changes but can actually benefit from them instead of perishing, as in the case of checklists.
Programmatic Governance Integrates Policy-as-Code (Pac)
Compliance Automation to Enable Dynamic Policy Enforcement
Governance-as-Code for Robust Regulatory Compliance
Data Mesh Meets AI: Federated Ownership and ML-Enabled Contracts for Cross-Domain Data Collaboration
In modern data-driven companies, the problem of fragmented data continues to stifle innovation, make it harder to make these decisions, and make it harder for people from different departments to work together. As companies grow, traditional centralized data architectures have trouble growing quickly & meeting the needs of various fields. This has led to the creation of the Data Mesh paradigm, which supports a decentralized approach to data management in which domain teams own, share, and utilize data as a product. However, enabling substantial cross-domain data collaboration within a distributed framework remains a considerable challenge, particularly regarding the establishment of trust, governance & interoperability across many other domains. This essay looks at how federated ownership and AI-driven smart contracts might change the way information is shared and managed in many other different fields. By adding ML to contract logic, businesses can automate access control, usage limitations, compliance checks & predictive alerts. This reduces friction and builds trust between teams. Through case studies and architectural analyses, we show how machine learning-enabled contracts allow for secure, scalable, and auditable data exchange while keeping autonomy and avoiding bottlenecks. Our study shows that adding AI to federated data governance makes data easier to get to and better quality, and it also gives domain teams more flexibility and responsibility to come up with new ideas. The paper offers a paradigm for businesses seeking to implement Data Mesh via intelligent automation, including practical insights into the design, implementation & measurable impacts of this technique in real-world contexts.
AI-powered CRM Transformation: Enhancing Customer Experience through Intelligent Salesforce Solutions
Customer relationship management is frequently abbreviated to CRM. It has evolved well beyond a customer information storage system and is now considered a vital business tool through which a company can deliver personalized customer engagement, increase operational efficiency, and even enable promotion. Nowadays, when organizations handle not only elevated customer expectations and much larger amounts of data but the issue of extremely quick decision-making as well, most of the conventional CRM approaches will be incapable of offering the kind of instant response and personalization that the competition requires. The entry of Artificial Intelligence (AI) in the CRM industry has brought a revolutionary change by enabling enterprises to not only extract value from customer data but also to anticipate their actions and automate their daily tasks. Since Salesforce is a top CRM system, it too has experienced tremendous growth through deployment of very complex AI features such as Einstein AI, predictive analytics, intelligent automation, natural language processing, and machine learning-based ​‍​‌‍​‍‌recommendations. The paper discusses how the Salesforce solutions empowered with AI are revolutionizing customer experience management by providing deeper customer engagement, enhancing sales productivity, optimizing service operations, and supporting data-based decisions. The study investigates the embedding of AI capabilities in Salesforce programs and measures the effect of such technologies with the help of case studies from the industry, a review of published work, as well as direct experience with use. Results show that businesses using Salesforce solutions supported by AI have a better relationship with customers, more sales made, faster fixing of issues, more accurate forecasting, and overall better working of the company. Besides this, through automation, AI helps reduce the time spent by people on the work that can be done by machines, which gives the staff a chance to concentrate on customer interactions that have higher value and think of strategic plans. In addition, the paper underscores the critical factors involved in deploying these technologies such as data quality, user adoption, governance, and ethical AI practices. The major value of the present study is showing how smart Salesforce solutions can help meet the high customer expectations while at the same time supporting the business, resulting in a CRM environment that is more proactive, personalized, and scalable.
Data Governance and Trust in Modern CRM Platforms: Strategies for Scalable Customer Data Management
Customer​‍​‌‍​‍‌ Relationship Management (CRM) systems have become the mainstay of customer-centric organizations, enabling them to collect, manage, and analyze customer information from multiple channels and touchpoints. As more companies are turning to data for their decision-making, high-quality, consistent, safe, and reliable customer data have not only become key factors that influence business outcomes and customer satisfaction, but also play a role in complying with regulations. In this regard, data governance and trust are crucial in ensuring that customer data remain correct, accessible, safe, and ethically handled throughout their lifecycle. This article explores how data governance structures and trust-enhancing practices are becoming part and parcel of CRM tools nowadays, most importantly concentrating on those methods that allow scalable handling of customer data even in rapidly changing business circumstances. The paper aims to identify primary governance measures, quantify their overall impact on data quality and organizational trust, and determine how today's businesses can maintain a balance between data openness and ​‍​‌‍​‍‌protection/privacy. A​‍​‌‍​‍‌ qualitative methodology supported by a comprehensive case study of a CRM system implementation has been the method of this work exploring governance policies, data stewardship frameworks, metadata handling procedures, access control mechanisms, and compliance tactics which are the main factors for successful customer data management. The findings indicate that organizations that articulate their governance frameworks clearly, standardize their data management processes, and publicly set up accountability lines, experience a substantial increase in the accuracy of data, operational efficiency, customer trust, and regulatory preparedness. Also, the study highlights the role of technologically advanced tools like AI, automation, and live monitoring in enhancing governance accuracy and customer data resources trust. By delivering strategic insights and scalable governance models, this paper supports the growing body of research on CRM data management and provides practical recommendations for business executives, data professionals, and technology partners to build dependable, strong, and future-proof CRM systems leading to continuous business success and enhanced customer ​‍​‌‍​‍‌relations.
Transforming Prior Authorization through Artificial Intelligence: A National Framework for Reducing Administrative Burden and Improving Patient Access
Prior authorization is widely used to manage healthcare utilization and control costs, yet its current implementation often creates substantial administrative burden, delays in treatment, inconsistent insurer requirements, and barriers to patient access. Healthcare professionals frequently spend considerable time preparing documentation, responding to payer queries, and managing appeals, while patients may experience uncertainty, interrupted care, and delayed access to clinically necessary services. This paper examines how artificial intelligence can transform prior authorization through a national framework designed to reduce administrative workload while improving timeliness, transparency, equity, and clinical accountability. The proposed framework integrates interoperable health-data exchange, AI-supported documentation extraction, eligibility checking, risk-based case triage, clinician-led review, patient communication tools, and continuous performance monitoring. AI is positioned as a decision-support mechanism rather than an autonomous decision-maker, ensuring that complex, high-risk, and denied cases remain subject to qualified human oversight. The framework also incorporates safeguards for privacy, explain ability, bias detection, patient appeal rights, and independent governance. By standardizing workflows and reducing repetitive administrative tasks, an AI-enabled national prior-authorization system may improve provider efficiency, accelerate appropriate treatment access, and support a more patient-centered healthcare system. Future empirical research should evaluate the framework using real-world payer, provider, and patient outcome data.
AI-Driven Governance and Privacy-Preserving Architecture for Healthcare CRM in Multi-Org Cloud Environments: A Human-Centered Big Data Framework for Trustworthy Decision-Making
Healthcare and insurance organizations are increasingly expected to modernize digital service operations while protecting privacy, preserving trust, and improving the quality of decisions made across large and complex ecosystems. In multi-organization cloud environments, healthcare customer relationship management platforms support interactions among members, agents, care coordinators, operational teams, and enterprise partners. Artificial intelligence can improve these interactions through guided recommendations, summarization, risk insight, and service orchestration, but it also introduces new concerns about data exposure, accountability, and governance. This paper proposes an AI-driven governance and privacy-preserving architecture for healthcare CRM in multi-org cloud environments. The framework combines human-centered design, big-data intelligence, privacy-preserving controls, and management oversight into a unified operating model for trustworthy decision-making. The paper argues that in healthcare service systems, AI cannot be treated as an isolated automation layer. It must be shaped by privacy boundaries, reviewable controls, explainability, and organizational discipline. The contribution of this study lies in presenting a practical architecture that links advanced computational capabilities with management-level trust, operational resilience, and more humane digital experiences.
Context-Aware Enterprise LLM Architecture for Trusted Customer Intelligence and Generative AI Applications in CRM Platforms
Customer relationship management (CRM) platforms increasingly depend on artificial intelligence to convert fragmented customer interactions into actionable intelligence. However, conventional CRM analytics pipelines typically separate predictive modeling, knowledge retrieval, governance, and human decision support into loosely connected components. This separation becomes problematic when large language models (LLMs) are introduced into enterprise environments, because generative systems can produce fluent but unsupported content, expose sensitive information, amplify biased patterns, and misalign recommendations with business context. This paper proposes a context-aware enterprise LLM architecture for trusted customer intelligence and generative AI applications in CRM platforms. The proposed framework integrates contextual signal modeling, retrieval-augmented generation, enterprise knowledge grounding, privacy-preserving data governance, explainability, human-in-the-loop control, and continuous monitoring. The architecture is designed to support customer service automation, sales enablement, churn-risk interpretation, next-best-action recommendation, knowledge-assisted agent support, and executive customer intelligence. Methodologically, the paper develops a layered conceptual model and an evaluation framework that jointly assesses factuality, contextual relevance, privacy risk, fairness, latency, business utility, and governance compliance. The analytical discussion shows that enterprise LLM value in CRM depends less on model scale alone and more on the alignment among customer context, authoritative enterprise knowledge, operational workflows, and trustworthy control mechanisms. The paper contributes a structured reference architecture and a multidimensional performance framework for deploying LLM-enabled CRM capabilities in high-accountability organizations.
Runtime Causal Fingerprinting for Root-Cause-Aware Autoscaling and Adaptive Resource Governance in Kubernetes Service Meshes
Kubernetes-orchestrated microservice systems increasingly operate under volatile workload patterns, strict latency service-level objectives, and complex inter-service dependencies introduced by service mesh communication layers. Although Kubernetes provides established autoscaling mechanisms, conventional reactive autoscaling policies frequently interpret symptoms such as CPU saturation, request latency, or queue growth as direct scaling triggers without distinguishing whether the observed degradation is caused by workload demand, downstream dependency propagation, resource contention, noisy-neighbor effects, configuration drift, or release-induced regression. This limitation often leads to unnecessary scaling, delayed remediation, over-provisioning, and repeated service-level objective violations. This paper proposes a runtime causal fingerprinting framework for root-cause-aware autoscaling and adaptive resource governance in Kubernetes service meshes. The framework constructs continuously updated causal fingerprints from distributed traces, service mesh telemetry, container metrics, event streams, and workload signals. These fingerprints encode not only anomaly signatures but also causal propagation patterns across services, pods, nodes, and resource dimensions. The proposed approach integrates causal graph inference, temporal fingerprint matching, SLO-risk estimation, and policy-constrained scaling actions within a closed-loop governance architecture. Unlike conventional autoscalers that scale based primarily on local resource utilization, the proposed framework differentiates demand-driven saturation from causally propagated degradation and selects remediation strategies accordingly. The paper develops the conceptual model, methodological pipeline, evaluation criteria, and analytical discussion necessary for implementing root-cause-aware autoscaling in production-grade Kubernetes environments. The study contributes a technically grounded framework for improving autoscaling precision, reducing false-positive scaling, mitigating cascading failures, and strengthening operational trust in cloud-native platforms.
A Cloud-Native Reference Architecture for Data Engineering, Generative AI, and Decision Intelligence Using AWS and Amazon Bedrock
As data in enterprises is growing at an accelerated pace and with the advancement of artificial intelligence, how the organizations are obtaining business intelligence and operational insights has undergone a significant change. Today's companies are increasingly relying on scalable, secure, intelligent, cloud-native architectures that can support high-volume data engineering, generative artificial intelligence (GenAI), and real-time decision intelligence. However, traditional data platforms are not designed to handle data silos, low latency, complex infrastructure, or integration with AI, and these are some of the challenges they face in today's business context. Solutions that leverage elastic infrastructure and elastic services are a good way to overcome these challenges. This paper suggests a cloud-native reference architecture for a data engineering pipeline, AI systems, and decision intelligence integration system with Amazon Web Services (AWS) and Amazon Bedrock. The architecture utilizes AWS services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Kinesis, AWS Lambda, Amazon SageMaker, and Amazon Bedrock to create a scalable intelligence ecosystem for enterprise. The proposed architecture provides the possibility of collecting, processing, transforming and analyzing structured and unstructured data, as well as integrating foundation models for intelligent automation and augmentation of decisions. The architecture is built around four key layers: data ingestion, data processing and storage, AI intelligence and delivery of decision intelligence. The data ingestion layer can ingest data using AWS-native services, such as batch and streaming workloads. The processing layer provides scalable ETL, ELT and data transformation pipelines. The AI intelligence layer includes AI and generative AI features such as natural language analytics, intelligent summarization, anomaly detection, predictive analytics, and recommendation systems. The decision intelligence layer transforms the results of the analysis into meaningful business insights via dashboards, automated workflows, and decision systems supported by artificial intelligence. The research compares the architecture based on the following performance metrics: Pipeline latency, Data processing throughput, Model inference speed, Query response time, Business decision acceleration. The experimental results have shown the substantial enhancements of the operational efficiency, data processing speed and decision quality over the conventional architectures. Results show that AI augmentation can lead to lower processing latency, responsiveness in analytics, and better AI-driven decision support capabilities. This research proposes a practical architecture that integrates modern data engineering with generative AI and intelligent decision-making, creating a seamless and enterprise-ready solution. It offers scalable infrastructure, a secure environment for AI deployment, and high-performance analytics, making it an ideal choice for digital transformation projects. It is a reference model for enterprises looking to modernize their data platforms and implement an AI-driven decision ecosystem with AWS cloud technologies.
Integrating Machine Learning, Data Governance, and Cybersecurity in Next-Generation Enterprise Data Ecosystems
The rapid growth of digital transformation initiatives has significantly increased the volume, velocity, and variety of enterprise data. Organizations increasingly rely on advanced analytics, artificial intelligence (AI), machine learning (ML), cloud computing, and distributed data architectures to derive strategic insights and maintain competitive advantage. However, the expansion of enterprise data ecosystems introduces substantial challenges related to data governance, security, privacy, compliance, and operational resilience. Traditional approaches that treat machine learning, data governance, and cybersecurity as independent disciplines are no longer sufficient to address the complexities of modern data-driven enterprises. This study explores the integration of machine learning, data governance, and cybersecurity within next-generation enterprise data ecosystems and proposes a comprehensive framework that aligns intelligent analytics, governance policies, and security controls. The research investigates the interdependencies among these domains and evaluates their collective impact on organizational performance, risk management, regulatory compliance, and data quality. Through a conceptual research methodology supported by comparative analysis of existing frameworks and enterprise practices, the study identifies critical success factors and emerging challenges associated with integrated enterprise data management. The findings indicate that organizations adopting unified governance-security-analytics frameworks achieve higher levels of trustworthiness, operational efficiency, and cyber resilience. Furthermore, machine learning technologies contribute significantly to proactive threat detection, automated governance enforcement, and intelligent data lifecycle management. The study concludes by outlining future research directions focusing on explainable AI, autonomous governance systems, privacy-preserving machine learning, and zero-trust enterprise architectures.
Reducing Audit Failures through Proactive Infrastructure Compliance Monitoring
Cloud computing, Infrastructure as Code (IaC), and continuous delivery have made it harder to maintain regulatory compliance. Traditional compliance models that rely on periodic checks and manual evidence are no longer effective. This paper looks at how proactive infrastructure compliance monitoring can help reduce audit failures in today’s IT systems. It covers the technical, operational, and cultural changes needed for continuous compliance, including policy-as-code, agentless scanning, automation, and integration with CI/CD pipelines. The results show that proactive monitoring improves system reliability, reduces regulatory risk, and simplifies audits in complex environments.
Machine Learning Framework for Electric Vehicle Customer Acquisition in the Automotive Industry
The rapid growth of the EV market presents a major opportunity for automotive companies to expand their customer base and drive sustainable revenue. This paper outlines the development of a data-driven customer acquisition strategy for an automotive client—their first such initiative. The strategy leveraged historical EV sales trends to identify two key segments: existing brand customers and conquest customers. Initially focusing on existing customers, we built a scalable framework for future expansion. A machine-learning-based propensity model was developed using demographic and lifestyle attributes, sales and service history, charging infrastructure, state incentives, and fuel price trends. Key predictors included purchase recency, financial health, infrastructure availability, and technological affinity. Implemented end-to-end, from data preparation to model deployment. This paper explores the methodology, insights, challenges, and the broader impact of data science in advancing EV adoption
Examining the Interplay of Artificial Intelligence-Driven Sustainability and Consumer Trust in Luxury Fashion Purchasing Decisions
This Luxury Fashion Industry (LFI) is going through a radical change because of the adoption of AI technologies and the role played by sustainability. These Luxury Fashion Brands (LFBs) are embracing innovative technologies like machine learning, predictive analytics, blockchain technology, virtual assistants, and intelligent recommendation tools for their business efficiencies, enhanced customer satisfaction, and sustainable practices. Meanwhile, people have become more aware about environmental sustainability, ethical sourcing, transparency, and sustainable consumption, which put pressure on LFBs for being more sustainable. This review paper identified the relationship between sustainability enabled by AI and consumer trust in purchasing Luxury Fashion Products (LFPs). It focuses on the ways through which these advanced technologies contribute to sustainable LF practices via waste elimination, smart inventory management, ethical supply chain activities, personalized customer engagement, and circular economy approach. In addition, this research focuses on the significance of factors that contribute to consumer trust such as transparency, authenticity, privacy assurance, use of AI technology ethically, and brand reputation to shape consumers’ buying decisions. Moreover, some of the problems and ethical issues linked with the implementation of AI technology in organizations have been identified such as data privacy, lack of transparency in AI technology, greenwashing, job displacement, and dependence on technology. Furthermore, the study considers various possibilities in the future and suggestions to ensure that both human oriented LF and technological advancement can coexist successfully. This literature review suggests that the integration of AI technologies can support sustainability initiatives, enhance consumer trust, and improve operational efficiency within the LFI.
Reducing Overstock in Hospitality Lighting Inventory with Data Forecasting
In the hospitality lighting sector, man-aging inventory efficiently remains a persistent challenge. Overstocking not only ties up capital but also consumes valuable warehouse space, es-pecially in an industry characterized by seasonal demand and complex project timelines. This paper presents a data-driven forecasting methodology to reduce overstock in hospitality lighting. By lever-aging historical sales data and applying time series analysis, suppliers can more accurately predict de-mand patterns, streamline procurement, and re-duce waste. The proposed approach improves in-ventory turnover and helps lighting vendors align with the needs of the dynamic hospitality sector. The analysis demonstrates that proper forecasting methods not only mitigate risks associated with surplus inventory but also enhance the respon-siveness of the supply chain. This ultimately re-sults in cost savings, improved margins, and ele-vated customer satisfaction.
Guard: A Governance-Anchored Framework for Runtime Monitoring and Incident Response in Enterprise Agentic AI Systems
The rapid enterprise deployment of agentic artificial intelligence (AI) systems introduces operational risks that existing monitoring and incident response (IR) frameworks cannot address. Agentic systems exhibit non-deterministic behavior, autonomous tool invocation, dynamic reasoning chains, and emergent capabilities arising from multi-agent composition — properties that invalidate the static monitoring assumptions of DevOps, MLOps, and LLMOps paradigms. The National Institute of Standards and Technology (NIST AI 800-4, 2026) documents these gaps comprehensively, yet no validated runtime enforcement or IR framework exists for enterprise agentic AI. This paper presents GUARD — Governance-Unified Agentic Runtime Detection and Response — extending the prior enterprise agentic AI lifecycle governance framework of Anuguthala (2026), which established mandatory system-of-record registration, risk tiering, and governance checkpoints but did not specify runtime enforcement mechanisms or structured IR procedures. GUARD closes this gap through three primary contributions: (1) the Agentic System of Record (SoR), extended with a three-entity registration model covering individual agents, workflows, and inter-agent composition boundaries, serving as the authoritative runtime enforcement reference for all agent resource decisions; (2) Registry-Bound Execution Control (RBEC), a three-state runtime mechanism — allow, human-in-the-loop (HITL) pause, or kill-switch — validating every agent resource access against the SoR before execution; and (3) the Agentic Incident Response (AIR) lifecycle, a six-phase risk-tiered IR process anchored to the SoR. Two supporting contributions accompany these: a formal Lethal Trifecta boundary condition — adapted from the risk intersection concept articulated by Willison (2025) — operationalizing risk-tier enforcement within RBEC; and an empirical reference implementation on LangGraph evaluated across 100 trials per scenario. Empirical evaluation across seven scenarios confirms correct detection of all five violation categories — including Lethal Trifecta Boundary Breach detected through monitoring record correlation — with zero false positives across 100 trials per scenario, providing the runtime enforcement layer that completes the governance-to-enforcement architecture initiated in the peer-reviewed prior governance framework (Anuguthala, 2026).
A Reference AI Architecture for the Modern Data Organization: Mapping Seven Branches to a Ten-Layer Implementation Stack
Enterprise data organizations are pursuing AI augmentation, but practitioner literature offers either single-branch agent systems (a data-science agent, a stewardship agent, a project-management agent) or unbounded surveys of agentic capabilities. Neither is implementable on day one. This paper proposes a complete task-to-architecture-to-tooling mapping for a Director of Data Engineering, or technology executive in charge of an enterprise data function. It identifies the seven canonical branches of a mature data organization (BI Engineering, AI/ML Engineering, Analytics, Data Science, Data Stewardship, Project Management Office, and Product Ownership), enumerates the three highest-volume operational tasks per branch (twenty-one tasks total), and proposes a ten-layer reference AI architecture that binds each layer to representative open-source and commercial tools (Cube, AtScale, Snowflake, Databricks, Collibra, Atlan, LangGraph, Pinecone, Monte Carlo, and others). A worked trace shows how a real cross-branch request moves through the architecture, and a phased adoption roadmap converts the framework into a 0-180 day implementation plan that the reader can take to a steering committee on Monday morning. The framework is LLM-agnostic, vendor-substitutable at the capability class, and protocol-aligned with Model Context Protocol and Agent2Agent. Reproducibility and ethical guardrails appear as standalone sections.
Uncertainty-Aware Feature Selection Framework Based on Three-Way Decision Theory for Explainable Machine Learning
Feature selection is a critical stage in machine learning because it improves predictive performance, reduces computational complexity, and enhances model interpretability. However, many existing feature selection methods rely on binary selection strategies that either retain or discard features based on fixed relevance thresholds. This approach can be inadequate when features contain uncertainty, instability, redundancy, or borderline predictive value. To address this limitation, this article proposes an uncertainty-aware feature selection framework based on three-way decision theory for explainable machine learning. The proposed framework classifies features into three decision regions: accepted features, rejected features, and deferred features. Accepted features are considered highly relevant and reliable, rejected features are removed due to low relevance or high redundancy, while deferred features are subjected to further evaluation because of uncertain or inconsistent importance. By integrating uncertainty measurement with feature relevance scoring, the framework provides a more flexible and transparent alternative to conventional two-way feature selection. The framework also supports explainability by providing clear justification for why each feature is selected, rejected, or deferred. This makes it particularly useful for high-dimensional and high-stakes machine learning applications where model transparency, reliability, and decision accountability are important. The proposed approach is expected to improve feature selection stability, reduce uncertainty in selected feature subsets, and enhance the interpretability of machine learning models.
Large Language Model-Based Program Comprehension for Enterprise Software Repositories: A Context-Aware Framework for Code Summarization, Dependency Analysis, and Impact Prediction
Enterprise software repositories contain complex, long-lived, and continuously evolving code assets whose behavior is distributed across services, frameworks, configuration files, database schemas, event streams, and deployment scripts. Conventional program comprehension tools provide useful static analysis, search, and dependency visualization capabilities, but they often fail to explain why a code element exists, how a change propagates across enterprise boundaries, and which downstream artifacts are likely to be affected by a modification. Large language models have recently demonstrated strong ability in source code understanding, documentation generation, and natural-language reasoning over program artifacts. However, direct application of generic LLMs to enterprise repositories is insufficient because enterprise comprehension requires contextual grounding, dependency-aware retrieval, architectural constraints, security awareness, and traceable impact prediction. This paper proposes RepoComprehend-LLM, a context-aware framework for LLM-based program comprehension in enterprise software repositories. The framework integrates repository mining, static dependency extraction, semantic code representation, retrieval-augmented generation, graph-based impact propagation, and confidence-calibrated explanation generation. Unlike isolated method-level summarization, the proposed framework generates hierarchical summaries at method, class, service, API, and business-capability levels; constructs multi-layer dependency graphs across code, configuration, database, event, and deployment artifacts; and predicts change impact using structural, semantic, historical, and developer-activity signals. The paper presents the architecture, methodology, scoring functions, evaluation protocol, implementation considerations, and enterprise governance controls for deploying such a framework in regulated software engineering environments. The central contribution is a research-oriented, implementation-ready approach that treats program comprehension as a context-aware, evidence-grounded, and continuously updated intelligence layer for software maintenance, modernization, and release risk assessment.
Intelligent Test Generation Using Large Language Models in Microservices-Based Enterprise Applications
Automated software testing has become a fundamental requirement for maintaining quality, reliability, and scalability in modern enterprise applications. The rapid adoption of microservices architecture has significantly improved system modularity and deployment flexibility; however, it has introduced complex testing challenges due to distributed service interactions, dynamic API contracts, independent deployment cycles, and heterogeneous technology stacks. Traditional automated testing approaches often fail to generate comprehensive test cases for microservices because they lack contextual understanding of service behavior, business logic, and inter-service dependencies. This research presents an intelligent test generation framework using Large Language Models (LLMs) to automate the creation of unit tests, integration tests, and contract tests for microservices-based enterprise applications. The proposed approach leverages LLM-based code generation capabilities combined with service metadata, API specifications, dependency graphs, historical defect information, and application source code to produce context-aware test suites. The framework focuses on Spring Boot-based microservices commonly used in enterprise environments and integrates AI-driven test generation into continuous integration and continuous deployment (CI/CD) workflows. Unlike conventional test automation methods that depend heavily on manually written test scripts, the proposed model analyzes application semantics, identifies potential failure scenarios, and generates optimized test cases with higher coverage. The research methodology includes extraction of microservice architecture information, preprocessing of service artifacts, prompt engineering strategies, LLM-based test generation, automated validation, and feedback-based refinement. The generated test cases are evaluated using software quality metrics including code coverage, defect detection rate, execution success rate, and developer productivity improvement. Experimental analysis on enterprise retail microservices demonstrates that LLM-assisted testing improves overall testing efficiency by reducing manual effort and increasing coverage of complex service interactions. The study further investigates the role of LLMs in handling API evolution, contract validation, and distributed transaction testing. Results indicate that intelligent test generation provides significant benefits in accelerating software delivery while maintaining reliability. The proposed framework establishes a practical foundation for integrating generative artificial intelligence into enterprise software quality engineering. The findings suggest that LLM-based testing can become an essential component of future DevOps ecosystems by enabling adaptive, scalable, and intelligent software validation.
A Multi‑Layer Federated Architecture for Secure Cross‑Institution Fraud Detection
Financial fraud has become increasingly sophisticated as digital banking, online payments, fintech platforms, and cross-border financial services continue to expand. Conventional fraud detection systems are often limited by institutional data silos, where each bank or financial organisation trains models only on its own transaction records. This restricts the ability to detect coordinated fraud patterns that move across multiple institutions. However, direct sharing of customer transaction data raises serious privacy, regulatory, commercial, and security concerns. Federated learning offers a promising solution by allowing financial institutions to collaboratively train fraud detection models without transferring raw data outside their local environments. This article examines the use of federated learning for privacy-preserving fraud detection across financial institutions. It outlines a framework in which participating institutions train local models on private transaction data and share protected model updates for secure aggregation into a global fraud detection model. The study further considers privacy-enhancing mechanisms such as secure aggregation, differential privacy, encrypted model updates, and model auditing. It also discusses key challenges, including non-identically distributed data, communication overhead, model poisoning, inference attacks, regulatory compliance, and explainability. The article argues that federated learning can improve collaborative fraud intelligence while maintaining stronger data protection, provided that technical safeguards, governance structures, and human oversight are properly implemented. The study contributes to the growing discussion on privacy-preserving artificial intelligence in financial crime detection and digital banking security.
Model Context Protocol Servers as Orchestration Layers for Generative AI Agents in Enterprise Software Delivery
Model Context Protocol (MCP) is now a standardised interface for interaction between AI agents, opening new avenues for enterprise software delivery automation. To speed up software engineering processes, intelligent automation, continuous integration and continuous delivery (CI/CD), DevOps pipelines, cloud-native architecture, and large language model (LLM) assistants are becoming more prevalent in modern software firms. The crosscutting issues of communication, inconsistent context, and limited interoperability across development, test, deployment, monitoring, and governance environments pose a major challenge to the integration of heterogeneous AI agents. In this paper, authors provide a formal tool for delivering MCP servers as a context-aware orchestration layer in enterprise CI/CD environments. The proposed architecture places MCP servers between generative AI agents and enterprise software systems, facilitating the exchange of customizable context, coordination of dynamic workflows, intelligent decision support, and secure interactions among various delivery stakeholders. The framework builds on concepts from context-aware computing, agent-based software engineering, semantic orchestration, and service-oriented integration to create a common layer of communication for enterprise development ecosystems. The study explores the benefits of providing context persistence, workflow intelligence, automated task delegation, and real-time decision support via the orchestration using MCP, to enhance software delivery efficiency. It integrates the repository management systems, issue tracking systems, testing frameworks, deployment pipelines, monitoring systems, and knowledge repositories into a shared context. The proposed framework is built on structured workflow analysis and shows improvement of the requirement traceability, deployment reliability, development productivity and operational governance. Moreover, MCP servers give centralized context management that decreases unnecessary agent interactions and boosts coordination among independent software engineering agents. The research is conducted on a conceptual and architecture-based approach, based on the principles of enterprise software engineering. Different orchestration scenarios such as automated code generation, intelligent testing, deployment validation, incident response and continuous monitoring are assessed. The results suggest that using orchestration layers built on top of the MCP can greatly improve the collaboration of AI agents with minimal compromise of enterprise security, compliance, and governance requirements. The architecture itself is also designed to be scalable with modular service integration and extensible protocol interfaces. The suggested framework is part of the expanding body of agentic software delivery that provides a common orchestration model that can be used to support future AI-native enterprise systems. The study offers actionable guidance for organisations looking to leverage generative AI technologies in existing software delivery infrastructure, ensuring reliability, interoperability, and operational transparency. The findings indicate that the MCP servers could be used as basic infrastructure elements of the future intelligent software engineering ecosystem.
Enterprise Mobile Payment Gateway Architecture on IoT Handheld Devices: Security and Reliability Design
The deployment of mobile payment gateways on retail handheld devices connected to the IoT network adds a new security and reliability challenge that is unique to mobile payment deployments. This paper describes a complete enterprise mobile payment gateway deployment architecture based on the production deployment experience on Zebra Technologies handheld devices in a large scale retail environment. Digital payment systems have revolutionized the retail sector, providing real-time transactions, mobility, convenience and efficiency. The addition of payment to the functionality of the handheld IoT devices, however, increases the attack surface and presents several device authentication, transaction confidentiality, network reliability, certificate management, regulatory compliance issues. The proposed architecture mitigates these concerns by using a layered security strategy which includes Point-to-Point Encryption (P2PE), Transport Layer Security (TLS 1.3), secure certificate lifecycle management, tokenization services, device attestation mechanisms and enterprise-wide identity management. Another key aspect of the architecture that adds redundancy, failover services, transaction recoverability and distributed gateway to support HA and operation continuity in the retail market. Special attention is given to meeting Payment Card Industry Data Security Standard (PCI DSS) requirements and ensuring scalability from geographically disparate retail sites. The research explores the ability of android based enterprise handheld devices to handle payment transactions securely, with low latency and high reliability. Analysis performed at device, network, gateway and cloud service layers. Transaction success rates, service availability measures, recovery time objective, and fault tolerance are evaluated for reliability mechanisms. The study shows that layered encryption and certificate-based trust models are key to significantly lower security threats associated with payments, and distributed gateway deployment provides better transaction resilience against adverse network conditions. Moreover, the paper is discussing the operational governance needs such as device enrollment, certificate rotation, audit logging, central governance of policies and security monitoring. These governance mechanisms allow enterprises to control thousands of retail devices, and ensure that the same security postures can be applied to each. Based on experimental analysis, the proposed architecture can provide better security guarantee and reliability than the conventional payment centralized models. The results can be useful in the design of enterprise payment systems, as a realistic and scalable solution for enterprises implementing IoT-enabled handheld payment systems. The architecture proposed is suitable to fit current omnichannel retail environments, as it is a balance between security, reliability, regulatory compliance and operational efficiency. Looking forward, future enhancements can draw on artificial intelligence-based threat detection, edge-based transaction analytics, and zero-trust architectures to help further bolster enterprise mobile payment ecosystems.
Azure DevOps CI/CD Pipeline Design for Regulated Financial Software Deployment Environments
Continuous integration and continuous delivery in regulated financial software environments must satisfy requirements that extend beyond standard software engineering practice, including change management controls, deployment audit trails, segregation of duties enforcement, and rollback capability within regulatory timeframes. This paper presents a design framework for Azure DevOps CI/CD pipelines that satisfy these requirements while maintaining deployment velocity in enterprise financial software organizations.
Spring Batch Framework Patterns for Large-Scale Data Processing in Cloud-Native Microservices: A GCP Deployment Study
Although Spring Batch is a commonly cited framework for large-scale batch data processing in enterprise Java applications, using it in conjunction with cloud-native microservices architectures on Google Cloud Platform (GCP) brings design challenges that are just as significant as those faced in on-premises deployment scenarios with regards to job partitioning, fault handling, event scales and observability. The aim of this paper is to perform a systematic investigation of the four patterns of the Spring Batch framework specifically tailored for cloud-native microservices running on GCP: job partitioning patterns to achieve distributed computing, remote chunking patterns to achieve horizontal scaling, Cloud Pub/Sub integration patterns for event-driven batch triggering, and Stackdriver-based patterns for bull batch monitoring in production. Benefits of using enterprise data processing workloads are clearly illustrated through a series of case studies that show measurable gains in throughput and operational reliability in workloads when compared to the traditional batch deployment model.
Quantum-Classical Hybrid Algorithms for Complex Query Planning: A Scalable Computational Framework for High-Dimensional Decision Optimization in Heterogeneous Data Ecosystems
The rapidly increasing number of heterogeneous data ecosystems, such as structured databases, semi-structured data lakes, real-time streams, distributed knowledge graphs, has further complicated the issue of query planning and optimization in modern information systems extensively. The optimization of queries is essentially an NP-hard combinatorial problem especially when based on multi-join queries, distributed execution plan, dynamic cost model, and uncertainty in the data access patterns. Even decades of concept and engineering advances, classical database optimizers are rapidly becoming unable to offer a globally optimal execution plan when faced with high-dimensional data, high data volatility, with a very tight latency constraint. Good news Recent breakthroughs in quantum computing propose promising computational paradigms that can be used to solve combinatorial optimization problems via quantum parallelism, superposition, and entanglement. Nevertheless, even the near-term quantum hardware is limited in the number of available qubits, noise, and decoherence, making it impractical to achieve fully quantum database systems in any perceived future. This is also the reason why quantum-classical hybrid algorithms are being developed: designed to add quantum optimization subroutines to classical database architectures is guaranteed without breaking their compatibility with existing execution engines. The following paper presents a framework of scalable quantum-classical hybrid computing of complex query planning in heterogeneous data ecosystems. The framework breaks down query optimization into classical and quantum-amenable optimization, which allows NP-hard subproblems, such as join ordering, operator placement, and cost minimization, to be transformed into quantum optimization models such as Quadratic Unconstrained Binary Optimization (QUBO) and Ising Hamiltonians. Classical elements handle query parsing, semantic analysis, constraint verification, and orchestration, whereas quantum processors are called selectively to solve combinatorial decision-making tasks. The suggested architecture will consist of a layered execution, hybrid execution, cost-model encoding approach, and adaptive fallback mechanisms to make them robust to quantum hardware variability. This has been experimentally tested with simulated quantum annealing and variational quantum algorithms that exhibit a quantifiable improvement in the quality and scalability of optimization and heuristics based on entirely classical algorithms, especially in high-dimensional query plans. The findings confirm the practicability of quantum query optimization and put down a feasible route to implement quantum algorithms in the serving machine of the next-generation data management systems.
Intelligent Risk Assessment and Classification in Project Management Using Data-Driven AI Models
The rapid evolution of globalization, digitalization, and technological innovations has led to an increase in complexity of the project management domain; therefore, assessing and classifying the risks involved becomes increasingly complicated. The conventional techniques of risk management, which include risk identification, analysis, and forecasting, cannot adequately detect and control complex risks related to finance, operations, technology, strategy, and cybersecurity. In view of the aforementioned difficulties, the use of “Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), predictive analytics, and big data” has been identified as a smart way to improve the management of the project risks. This study is intended to review the contribution of AI-based solutions for risk assessment and classification in project management settings. This study discusses several types of AI algorithms and predictive models, including “Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Networks, Recurrent Neural Network, and Long Short-Term Memory Models”, focusing on their uses, strengths, and weaknesses in project risk management. AI enhances the accuracy of predictions and decisions, enabling real-time monitoring and planning by uncovering patterns of risks and developing measures to mitigate them. Yet, several barriers remain that prevent the implementation of intelligent risk management practices, including data integrity problems, cybersecurity concerns, ethical issues, AI bias, lack of transparency, and absence of real-life validation studies. This paper concludes that the implementation of Explainable AI (XAI), real-time analysis capabilities, cloud computing technologies, and digital twins can improve the performance of intelligent risk management systems.
A Conceptual Framework for Training Effectiveness through AI-enabled Digital Learning in Luxury Automotive Retail
The luxury automobile retailing industry is witnessing a paradigm shift owing to digitalization, changing customer needs, connectivity of vehicles, advancements in electric vehicles, and the integration of Artificial Intelligence (AI) technologies into different business operations. In this backdrop, the training of employees has gained importance and become a key driver of service delivery, customer satisfaction, brand image, and competitive advantage for organizations. In this regard, the present review paper aims to develop a conceptual model to improve training performance through digital learning using AI technologies in luxury automobile retailing. For the purpose of this research, the exploratory approach will be used along with a conceptual review method based on literature reviews on artificial intelligence, digital learning, organizational learning, adaptive learning systems, employee training, and automotive retailing innovations. The conceptual model under investigation shows that artificial intelligence-enabled learning systems serve as major catalysts to ensure efficient training with engagement, learning satisfaction, motivation, and knowledge retention acting as crucial intermediary variables. Based on the results obtained, AI-based learning environments could have a substantial impact on the development of workers' competencies, fast learning processes, high-quality customer service, organizational efficiency, and competitive advantages in luxury automobile retail settings. Nonetheless, issues of algorithmic discrimination, employee privacy protection, ethical management, and maintenance of human elements in premium customer communications should not be underestimated. Therefore, the research comes to the conclusion that the future of training programs in luxury car dealerships lies in combining artificial intelligence technologies and human-based methods in education.
Impact of IoT and Digital Technologies on Project Management Efficiency: A Survey Study
The Internet of Things (IoT) and digital technologies have made a significant impact on project management, enhancing efficiency, communication, monitoring, and decision-making. Some of the traditional methods of project management can sometimes have problems with poor communication, understated estimation, quality of resources usage, and lack of risk management. Advanced digital solutions, including AI, Cloud computing, Big data analytics, Blockchain technology and IoT devices, hold the promise of delivering innovative solutions to these challenges through automation, real-time monitoring, predictive analysis, and intelligent decision-making. The study is a survey research that explores the effect of Digital Technologies and IoT on the efficiency of the project management, specifically on the efficiency of the following project management tasks: planning, monitoring, communicating, managing risk, and optimizing resources. This research also evaluates the added value of digital tools to increase productivity, reduce operating costs, enable collaboration and support data-driven project management. Additionally, it outlines the critical implementation issues, such as cybersecurity concerns and deployment expenses, technical skill gaps, and integration complexities. The study highlights growing importance of digital transformation in project management and provides recommendations for future studies to recognize impact of implementing digital transformation to increase project efficiency in various industries through a comprehensive literature review.
Examining the Role of Adaptive Policy Frameworks and Organisational Sustainability in the Indian Manufacturing Industry under Uncertain Business Conditions
The Indian manufacturing industry is experiencing a radical change as a result of globalization, digitization, environmental factors, technological disruption, and business uncertainties. Supply chain disruptions, fluctuations in prices of raw materials, geopolitical issues, climate change regulations, and the emergence of Industry 4.0 technology are some of the factors responsible for creating operational and strategic issues for manufacturing firms. The traditional rigid governance strategies are becoming obsolete in managing such uncertain scenarios and, therefore, require new policy strategies that enhance adaptability and sustainability. In this research, an explorative methodology will be used through literature analysis concerning adaptive governance, organizational sustainability, Industry 4.0, and sustainable manufacturing. This study explores the role played by the mechanisms of adaptive governance, which include value chain flexibility, real-time data integration, monitoring mechanisms, and organizational culture with a focus on sustainability, in facilitating organizational resilience, efficient use of resources, innovations, and competitiveness. In addition, the study evaluates the key driving forces and the key hindrances to the uptake of sustainability in India. These factors include institutional pressure, competitive pressure, technical hurdles, financial limitations, and supply-related obstacles. It has been established that manufacturing companies which adopt adaptive governance mechanisms and sustainable practices have better chances of managing uncertainties and enhancing resilience.