Journal of International Crisis and Risk Communication Research
The Journal of International Crisis and Risk Communication Research (JICRCR) is the first scholarly journal dedicated to human and mediated communication issues associated with crises, risks, and emergencies around the world. The journal is supported by an international editorial board comprised of top crisis and risk communication scholars. JICRCR is a biannual peer-reviewed, open access publication, which means that all content is freely available without charge to the user or institution. ISSN: 2576-0017 E-ISSN:2576-0025 Indexed in ProQuest, Scopus, EBSCO, Informit, Google Scholar, Ulrichsweb Global Serials Directory, and other databases.
Integrated Order And Invoice Tracking: Optimizing Supply Chain Visibility And Financial Operations
Integrated order and invoice tracking systems represent transformative solutions for enterprises seeking to overcome the operational and strategic limitations imposed by fragmented data architectures across sales, procurement, and financial platforms. Contemporary consumer packaged goods enterprises face mounting pressures to optimize supply chain visibility, accelerate financial reconciliation cycles, and derive actionable intelligence from transactional data distributed across heterogeneous enterprise systems. The fragmentation of operational data creates substantial barriers to comprehensive order-to-cash lifecycle visibility, introduces inefficiencies in working capital management, and constrains organizational capacity to respond with agility to market dynamics. This article provides an integrated view of frameworks for enterprise system integration that assimilate multiple sources of data via application programming interfaces, message queuing infrastructures, and service-oriented design patterns. This article discusses data warehouse architectures that embed dimensional modeling, ETL processes that assure data quality and consistency, and event streaming capabilities that facilitate monitoring transactions in real time. Advanced analytics capabilities permit transactional data in the integrated data warehouses to be converted to operational intelligence–e.g., dashboards that are interactive, predictive modeling frameworks, and scenario simulation tools used to support evidence-based decision-making. The mentioned implementation results shown can achieve significant operational efficiencies from automating manual reconciling work, greater accuracy in forecasts, which lead to optimization of inventory, and improved competitive positioning through reliance on data to support strategic planning activities. This article shows how integrated tracking platforms continually add value by connecting operational activity with enterprise objectives in increasingly complicated global marketplaces.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Observability-Driven Reliability In Financial Services: Transforming Compliance Into Continuous Practice
Engineers for financial services represent a transformative progress in both operational solutions and regulatory compliance. As digital financial platforms process billions of transactions per day, integration of real-time monitoring with a compliance framework creates a system capable of understanding disruptions while maintaining regulatory compliance. This paradigm fundamentally changes in relation to continuous verification from reactive compliance as to how the financial institutions reach the government, enabling them to safely enable the customers to protect the trust. Transaction tracing infrastructure, financial-specific matrix collection, relevant logs with regulatory classification, and the specific architectural components, including an algorithm detecting domain-specific discrepancies, form the foundation of these advanced systems. Financial institutions that implement these framework experience compliance violations, rapid event solutions, fraud detection and significant cost savings in sufficient cost savings. The resulting change creates a virtuous cycle where technical reforms simultaneously increase regulatory reform, eventually contributing to economic stability through reliable access to financial services.
Configuration-Driven Rules Engines: Scalable Multi-Market Regulatory Reporting In Enterprise Data Warehouses—An Implementation Framework With Healthcare Case Studies
Configuration-driven rules engines signify a groundbreaking architectural framework for governing multi-market regulatory reporting in enterprise data warehouse contexts. Healthcare and financial firms increasingly face growing complexity in regulatory compliance as jurisdictions implement unique reporting requirements, validation rules, and submission methods that often change. Traditional implementations that are market-specific generate architectural redundancy, demonstrated through shared codebases, inconsistent validation logic, and disparate maintenance procedures, resulting in technical debt while increasing compliance risk. Configuration-driven architectures engage these concerns by delineating business logic from implementation code with metadata-based rule definitions that are applied dynamically at run time, which affords organizations the ability to consolidate codebases and create jurisdictional variations through the use of parameterized functions. This architectural paradigm takes advantage of parallel processing, automated orchestration frameworks, and hierarchical metadata to create scalable compliance responses. Empirical healthcare implementations demonstrate configuration-driven architectures achieving error rate reductions from 2.8% to 0.6%, processing throughput improvements reducing monthly compliance cycles from 18 to 4 days, and operational cost reductions of 35–40% through consolidated validation logic and automated orchestration, while reducing full-time staff allocation for regulatory report maintenance by 45–55%. This continued evolution from embedded or hard-coded rules to flexible, metadata-driven configurations affords rapid responses to regulatory change while ensuring an audit trail for compliance review. An emerging class of technologies, notably artificial intelligence, blockchain, and cloud-native architectures, expands the configuration-driven capabilities, positioning these technology systems as foundational infrastructure for mapping multiple jurisdictional regulatory demands on organizations and individuals as they face the growing complexity of compliance.
Navigating The 401K Implementation Dilemma In Oracle HCM Cloud: Assignment Vs. Payroll Relationship Level Setup
Oracle HCM Cloud's multiple assignment architecture presents significant implementation challenges for 401K retirement benefit programs, requiring organizations to strategically determine whether configurations should be established at the payroll relationship level or individual assignment level. This article systematically examines the technical, operational, and compliance considerations inherent in 401K implementation within multi-assignment environments, exploring how Oracle's benefits administration framework distinguishes between earnings and deduction elements while positioning 401K elements in a uniquely complex architectural space. The article compares assignment-level implementation, which offers granular control and flexibility for organizations with complex multi-entity structures, against payroll relationship-level configuration, which aligns with Oracle's recommended practices and simplifies administration for consolidated plan structures. The real-world scenarios, including multiple qualified plans across legal entities, mid-year job transitions, and third-party provider integrations, the article demonstrate how configuration decisions carry far-reaching implications for contribution calculation accuracy, regulatory compliance, and administrative efficiency. The article presents evidence-based implementation guidelines emphasizing compliance-driven approaches that prioritize Employee Retirement Income Security Act adherence, systematic decision frameworks based on organizational structure analysis, and flexible architectures capable of accommodating evolving workplace models, including remote work proliferation and gig economy integration, ultimately providing organizations with comprehensive guidance for navigating these complex implementation decisions while satisfying regulatory requirements and operational sustainability objectives.
Barriers To Accessing Emergency Medical Services In Remote Regions: A Red Crescent Case Study
The access to emergency services remains a challenge to this date in areas that have been either remote or rural across the world. This study investigates barriers to accessing EMS through a detailed, mixed-methods case study of Red Crescent operations in underserved areas, either by surveys, interviews, or analysis of operational data. These include geographical isolation, infrastructural deficits, resource limitations, and socio-cultural factors. Response times in remote areas are 300-400% higher compared with urban benchmarks, and this has considerable implications for patient outcomes. The key recommendations emanating from this study are evidence-based and go a long way in aiding the improvement of access to emergency medical care in challenging terrains.
Abdulaziz Ibrahim ALmarwan, Hamoud Saad S Almatrafi,Abdullah Saud O Alharbi, Faris Abdullah M Alotaibi,Mohammed Abdulmohsen Al-Sulmi,Abdulaziz Abdullah Albalawi,Amir Ali Alanzi, Ali Salamah Alanazi
Orchestration Governance Frameworks For Agentic Supply Chains: Resolving Agent Conflicts Under Uncertainty
Supply chain management can dramatically change as autonomous artificial intelligence agents with specialized decision-making abilities are truly deployed across forecasting, procurement, inventory optimization, logistics, and sustainability. These intelligent systems show a large increase in their operation; they are much more accurate, responsive, and effective. However, autonomous agent deployment introduces critical governance challenges when multiple agents propose conflicting actions based on divergent optimization objectives. Traditional centralized control mechanisms with static rule hierarchies prove inadequate for managing adaptive, probabilistic agent behaviors operating under uncertainty. The Orchestration Governance Framework addresses these challenges through a systematic three-layer architecture integrating operational agents, coordination mechanisms, and governance enforcement. The framework employs the Belief-Desire-Intention cognitive model, enabling agents to manage complex decision spaces while maintaining computational tractability. Conflict resolution operates through a structured four-stage pipeline combining explicit rule-based detection, statistical variance measurements, multi-objective optimization exploring Pareto-efficient solutions, and dynamic weight tuning aligned with organizational priorities. Probabilistic modeling accommodates inherent supply chain uncertainty through Bayesian inference. Validation through consumer goods manufacturing demonstrates successful resolution of demand growth versus emission constraint conflicts, achieving substantial revenue capture while maintaining environmental compliance. The framework preserves agent autonomy and continuous learning capabilities while ensuring regulatory adherence and stakeholder trust. Implementation considerations address data infrastructure dependencies, computational complexity scaling, and multi-agent learning stability requirements essential for enterprise deployment.
AI-Powered Anomaly Detection In Fintech: Bridging Devops With Large Language Models For Scalable Fraud Prevention
The financial technology sector is experiencing rising fraud risks as digital systems grow more interconnected. This study proposes a data-driven decision-support framework that combines Large Language Models (LLMs) with anomaly detection methods to strengthen fraud prevention. Unlike static rule-based systems, our approach applies network analysis and optimization techniques to capture contextual anomalies across diverse financial transactions. The framework uses adaptive data preprocessing, scalable decision pipelines, and contextual risk scoring to improve real-time detection while reducing false positives. Through case studies in transaction monitoring, identity verification, and payment processing, we observed 30–45% higher detection accuracy and significant reductions in false alerts. These findings suggest that LLMs can serve as a practical decision-support tool for managing financial risk. We also discuss deployment challenges and outline future directions such as federated learning, explainable AI, and distributed optimization, which can enhance scalability, transparency, and resilience against evolving fraud tactics.
Distributed Learning Systems For Autonomous Vehicles
The rapid evolution of autonomous vehicle technology has created unprecedented demands for sophisticated machine learning models capable of real-time decision-making while preserving user privacy. This article presents a comprehensive analysis of Federated Learning architectures specifically designed for autonomous vehicle applications, examining how these distributed learning paradigms enable collaborative model training without compromising sensitive data privacy. It explores the integration of cloud-edge computing frameworks, advanced cryptographic protocols, and geospatial intelligence systems that collectively enable privacy-preserving AI deployment at scale. The article systematically reviews recent implementations and case studies from leading automotive manufacturers, demonstrating how federated architectures achieve enhanced vehicle safety, improved route optimization, and robust privacy protection while addressing the unique challenges of vehicular networks. The article examines core architectural principles including distributed computation, secure aggregation, and privacy preservation mechanisms that form the foundation of vehicular federated learning systems. It analyzes hierarchical federated learning architectures that leverage multi-tier cloud-edge integration, enabling efficient resource utilization while maintaining model consistency across diverse operational environments. The article covers advanced privacy-preserving mechanisms including differential privacy integration and cryptographic protocols that provide mathematical guarantees against privacy leakage. Additionally, it explores geospatial intelligence integration and location-aware learning approaches that address the spatial heterogeneity inherent in vehicular data. Through a comprehensive evaluation of real-world deployment challenges and performance metrics, this article provides essential insights for implementing scalable, privacy-preserving federated learning systems in production autonomous vehicle environments.
Building a Universal Payment Platform: Breaking Free from Payment Gateway Lock-in
Modern enterprise payment infrastructures face unprecedented challenges with vendor lock-in, complex migrations, and regional fragmentation, creating significant operational and financial burdens. Traditional payment systems require businesses to adapt their operations to specific payment providers, resulting in extended implementation timelines, excessive configuration complexity, and astronomical switching costs. This technical article presents a universal payment platform architecture that fundamentally transforms payment processing through gateway abstraction, enabling organizations to connect with any payment provider without modifying core business logic. The proposed solution implements a middleware layer featuring a gateway-agnostic API, dynamic routing engine, and unified configuration management system. Through comprehensive implementation strategies including RESTful API design, adapter patterns for gateway normalization, and resilience mechanisms such as circuit breakers and fallback routing, the platform ensures reliable payment processing while maintaining flexibility. Real-world implementations demonstrate dramatic improvements in migration efficiency, cost optimization through intelligent routing, and simplified reconciliation processes. The architecture addresses critical security and compliance requirements through tokenization, automated regional compliance validation, and comprehensive audit capabilities. Performance optimization techniques, including aggressive caching strategies, asynchronous processing patterns, and advanced observability practices, enable the platform to handle high-volume transactions while maintaining low latency. Future enhancements incorporating machine learning for routing optimization, blockchain integration for cryptocurrency support, and real-time analytics dashboards position the platform for continued evolution in the rapidly changing payment landscape.
Strategic Storage Infrastructure Decision-Making In The AI Era: A Framework For Balancing Financial, Technical, And Compliance Considerations
This article presents a comprehensive strategic framework for enterprise storage infrastructure decision-making in the artificial intelligence era, addressing the transformation from traditional IT utility functions to strategic business imperatives. The article examines the interplay between financial architecture, regulatory compliance, and technical performance across hybrid infrastructure models. Through multi-criteria decision frameworks integrating capital and operational expenditure models, compliance obligations, and workload classification strategies, this article demonstrates how organizations can balance cost efficiency with innovation while maintaining regulatory compliance. The framework incorporates risk assessment methodologies for vendor lock-in, data sovereignty, and cost volatility, while establishing cross-functional governance structures for effective change management. Successful AI storage strategies require hybrid deployment approaches leveraging cloud infrastructure for experimental workloads while maintaining on-premises systems for predictable production environments, supported by automated workload placement algorithms and comprehensive TCO modeling that accounts for AI workloads' unique characteristics and evolving regulatory landscape.
Quantum Machine Learning For Credit Risk: A Next-Generation Approach To Risk Assessment
Financial sectors are based on credit risk modeling to make decisions on lending and regulatory compliance. Although classical machine learning has been used to increase predictiveness, such models are faced with mounting limitations as complex and high-dimensional financial data is processed. Quantum Machine Learning (QML) is an aspect of quantum computing that has offered a potential resolution to the issues, integrating quantum computing and machine learning to speed up the calculations and extract more insight into the patterns. QML has theoretical benefits in classifying credit risk via Quantum Support Vector Machines, Quantum Neural Networks, and hybrid quantum-classical models, through superposition and entanglement. Initial applications show good performance in portfolio optimization, default forecasting, and simulation of risks with the existing hardware constraints. QML demonstrates specific potential in the context of using non-traditional data sources and finding hidden correlations that could reflect creditworthiness, which may allow inclusion-based lending practices without sacrificing risk assessment. With the development of quantum hardware, the financial services sector will gradually adopt quantum capabilities via realistic hybrid strategies that eventually revolutionize the way credit risk is assessed in the world markets.
Breaking Into AI-Driven Cloud & Cybersecurity Careers: Practical Advice From The Field
Problem Statement The intersection of artificial intelligence, cloud computing, and cybersecurity has dramatically altered the employment landscape for professionals in highly regulated industries, specifically Banking, Financial Services, and Insurance (BFSI). The cybersecurity workforce is experiencing major challenges as organizations struggle to fill critical security roles across industries, creating significant talent shortages. Financial institutions are implementing AI-based solutions across operations—from fraud detection and algorithmic trading to customer relationship management and regulatory compliance automation—yet face difficulties finding professionals who can navigate both advanced technology implementation and complex regulatory requirements. Solution Approach This report examines vital pathways for launching successful careers within these intersecting technology domains through a comprehensive approach combining practical portfolio development, strategic certification planning, and essential soft skills cultivation. Success requires more than technical knowledge alone; professionals must develop integrated competencies spanning AI implementation, cloud architecture, cybersecurity frameworks, and regulatory compliance. The research emphasizes building hands-on project portfolios, obtaining role-based certifications, mastering stakeholder communication, and actively engaging with professional communities to bridge the gap between technical implementation and business strategy. Key Findings The analysis reveals that thriving in these rapidly evolving fields demands a strategic mindset incorporating technical competence, regulatory knowledge, adaptability, and problem-solving capabilities. Successful practitioners must balance innovation utilizing advanced AI and cloud technologies while operating within the security, transparency, and compliance requirements of highly regulated environments. The research demonstrates that career advancement depends on professionals' ability to serve as strategic bridges between technical teams and business leadership, combining deep technical expertise with communication skills and continuous learning approaches to navigate the challenging intersection of cutting-edge technology and regulatory complexity.
Risk-Aware Migration Strategies For Enterprise ERP Systems In Regulated Industries
ERP machine shifts from conventional platforms to subsequent-generation in-memory systems are foundational inflection points for groups in incredibly regulated environments. Present-day ERP systems integrate new technology such as artificial intelligence, gadget mastering, blockchain, and net of things functionality, redefining enterprise resource planning paradigms at their center and bringing state-of-the-art validation needs and compliance troubles. In-memory computing frameworks take advantage of column-primarily storage systems with sophisticated compression techniques, compressing data in ratios of ten-to-one to 100-to-one and helping real-time transactional and analytical workloads within converged data environments. Regulative complexities increase as firms have to meet requirements of more than one framework, such as electronic records management standards, good practice guidance, financial reporting controls, and data protection regulations simultaneously. Greenfield reimplementation, brownfield conversion, and selective data transition strategies selection of migration approach necessitates judicious consideration using extensive financial methods such as net present value analysis, internal rate of return measures, and payback period determination. Top management support, business process reengineering, project management methods, vendor choices, communication practices, and user training are critical success factors that become crucial to implementation results. Risk-sensitive frameworks incorporating compliance integration, readiness evaluation, and data validation procedures across transformation life cycles allow companies to attain operational progress and regulatory consistency within highly regulated sectors.
Tourism-Led Employment Nexus In Saudi Arabia: An ARDL Analysis Of Vision 2030 Diversification Impacts
This paper investigates the dynamic relationship between job creation and the development of Saudi Arabia’s tourism sector within the Kingdom’s Vision 2030 framework. Employing the Autoregressive Distributed Lag (ARDL) bound testing and the associated error correction model, the paper empirically examines both short–run and long–run effects of the key determinants, including tourist arrivals, tourism infrastructure investment, public expenditure, private sector investment expenditure, and Vision 2030 structural reforms. The model proves a stable long-run co-integrating relationship among the study's variables, supported by robust diagnostic tests and a statistically significant error correction term. The empirical results reveal that tourist arrivals, Vision 2030-aligned infrastructure investment, and private capital expenditure have a significant positive impact on long-run employment. On the other hand, public investment shows a negative long-run impact, demonstrating crowding-out dynamics or inefficiencies. The short-run results reveal a positive role for tourist arrivals and private sector engagement as drivers of employment. While the interaction term that captures infrastructure investment in tourism under Vision 2030 showed a marginal, but significant, positive influence on employment, suggesting that recent policy-driven infrastructure improvements are the most influential external driver. Tourism-led employment is mostly self-sustaining according to the variance decomposition and Impulse response analysis, with Vision 2030 infrastructure investment standing out as the most significant external determinant. Strategic policy reforms, such as emphasizing private sector engagement, enhancing the effectiveness of public sector expenditure, and prioritizing infrastructure investment aligned with national development goals, must be implemented to optimize the employment potential of Saudi Arabia’s tourism sector.
Awad Mohamed Osman, Abdullah S Alharbi, Fathi Abdelrahman Ahmed Faramawi, Abdallah Ammar Hendaoui, Hadeel Fouad S Aljuwaie, Sara Qasem M Al-Suwaidi, Mohamed Ismail Abdulrahman Ismail, Mahmoud M. H. Alayis
The Evolution Of Mobile Insurance: Contextual Experiences And Financial Literacy
A significant shift in consumer interaction patterns can be seen in the way insurance services are now provided through contextual mobile experiences. While contemporary mobile-first methods enable continuous, customised engagement across customer lifecycles, traditional insurance models restricted contacts to infrequent times during claims management or policy renewals. The incorporation of life event tracking generates timely chances for pertinent insurance suggestions exactly when clients go through major changes like marriage, moving, or job promotion. Embedded financial education simplifies intricate insurance ideas into easily understood information that is provided precisely when decisions are being made. Combining wellness tracking with behavioural rewards creates positive feedback loops whereby healthier actions lead to both short-term and long-term health gains. Sophisticated prediction algorithms identify possible health issues early, which benefits insurers by reducing claims and improving health outcomes for consumers. Unmatched access to health data is made possible by the continuing partnership between insurance apps and wearable technologies, which enables more precisely customised policies based on actual lifestyle choices rather than presumptions about demographics. Pricing models that take into account real risk variables rather than hypothetical ones are more equitable as a result of dynamic risk assessment, which routinely modifies individual profiles as behaviours change.
Formal And AI Hybrid Techniques For Scalable Verification Of Large System-On-Chips
The semiconductor industry confronts escalating verification challenges as System-on-Chip designs integrate billions of transistors across heterogeneous subsystems, including artificial intelligence accelerators, central processing units, graphics processing units, and domain-specific processors. Traditional simulation-based verification struggles to provide exhaustive confidence, while formal verification encounters state-space explosion when applied to large-scale designs with millions of flip-flops and complex hierarchical interfaces. The convergence of artificial intelligence and formal verification introduces a transformative paradigm where machine learning algorithms intelligently guide formal solvers, predict verification complexity, and reuse accumulated proof knowledge to overcome scalability barriers. Graph Neural Networks enable the prediction of proof convergence based on circuit topologies, while Reinforcement Learning agents dynamically optimize solver parameters in real-time. Large Language Models trained on hardware description languages automatically generate SystemVerilog Assertions from natural language specifications. The hybrid architecture integrates artificial intelligence modules directly into formal verification loops through layered frameworks encompassing data ingestion, inference engines, formal solvers, and continuous retraining mechanisms. Industrial deployments across networking accelerators, processor subsystems, and mixed-signal controllers demonstrate substantial improvements in verification runtime, property coverage completion, and proof convergence rates. The framework uses advanced features such as intelligent cone partitioning to do parallel verification, adaptive lemma insertion to achieve faster proof convergence, constraint pruning using machine learning classifiers, and reuse of previous proof caches between design runs. Economic analysis shows high returns in terms of cost of reduced computation, increased engineering resource, fewer silicon respins, and quicker time-to-market. The combination of mathematical rigor and adaptive machine intelligence is one of the basic changes to autonomous verification ecosystems, where design evolution provides continuous concrete value, and the semiconductor industry is poised to sustain verification performance with linearly growing design complexity and to provide self-optimizing verification platforms that make steady gains as reflected by successive product generations.
Integrating Machine Learning (ML) On Mobile Applications Using Local Data To Personalize The Experience For The Customer
Mobile banking apps are getting harder to use as banks keep adding more features, making it difficult for customers to find what they need quickly. Most current solutions send your data to the cloud for processing, which raises privacy concerns, slows things down, and isn't great for the environment. Running machine learning directly on your phone offers a better approach - it learns from your habits without your data ever leaving your device. This system uses your phone's built-in AI capabilities to make personalized suggestions while keeping all your data right on your device. It watches how you use the app - what features you access, when you use them, and what tasks you complete - to build a private dataset that never leaves your phone. Smart data processing turns your usage patterns into useful information, while efficient AI models provide accurate recommendations without slowing down your device. Testing shows this approach is much faster than cloud-based systems and works whether you're online or offline. Users get four main benefits: better privacy since data never leaves their phone, faster performance with instant responses, longer battery life from less network usage, and a smaller environmental impact from reduced cloud computing. This technology isn't just for banking - it could improve healthcare apps, educational software, and business productivity tools too.
Unified Gateway Architecture For Multi-Tenant Large Language Model Serving
Enterprise adoption of large language models has revealed critical inefficiencies in current serving architectures, particularly for organizations deploying heterogeneous model fleets across multiple tenants. Existing solutions fragment prompt routing, key-value cache management, and safety enforcement across disparate components, resulting in elevated latency, redundant memory consumption, and inconsistent policy compliance. Gateway-Centric LLM Serving introduces a unified control plane that consolidates these functions into a dedicated gateway layer positioned between clients and model endpoints. The architecture enables dynamic model selection based on cost, latency, and domain constraints while exposing KV-caches as network-addressable resources for cross-session reuse. Centralized safety filters enforce organization-wide compliance policies including redaction and jailbreak prevention at the serving boundary. The routing decision is formalized as a multi-objective optimization with O(|M| log |M|) complexity, while cache operations achieve O(1) exact matching and O(log n) similarity search. Safety filtering maintains O(n × m) linear complexity with concurrent execution across pipeline stages. Evaluation on multi-tenant workloads with 10,000 requests across 3 tenants accessing 5 heterogeneous models demonstrates substantial improvements: P95 latency reduced by 51% (423ms vs 856ms), P99 latency improved by 62% (891ms vs 2,340ms), cross-tenant cache reuse yielding 42% memory savings with 58% hit rates, and policy violation reduction of 73% compared to distributed enforcement. Cost analysis reveals 34% TCO reduction with 5.6-month ROI for deployments exceeding 10M requests monthly. This architecture bridges distributed database gateway patterns with modern AI infrastructure, providing a blueprint for scalable, cost-efficient, and compliant LLM deployments.
Improved Authentication And Authorization For Data Security: Integrating MFA And SSO
This article examines improved authentication and authorization strategies for data security, focusing on the integration of Multi-Factor Authentication (MFA) and Single Sign-On (SSO) systems to enhance security while maintaining user experience. The discussion begins with an overview of authentication security evolution and traditional single-factor limitations, then progresses through a detailed exploration of MFA components, factors, and implementation considerations. SSO architectural models, authentication flows, and security implications are thoroughly evaluated, including the critical balance between convenience and security concentration. The article further explores adaptive authentication approaches utilizing contextual security factors and risk-based decision frameworks that dynamically adjust security requirements based on transaction risk. Implementation considerations are addressed through examination of scalability challenges, user experience optimization, regulatory compliance requirements, and economic factors, including total cost of ownership and return on investment assessment. Together, these elements provide a comprehensive framework for organizations implementing advanced authentication systems to strengthen security posture while addressing operational requirements and user acceptance factors.
AI-Driven Advertising Measurement In The Privacy Era: Innovations, Applications, And Strategic Frameworks
This article examines how AI and machine learning are transforming advertising measurement amid growing privacy restrictions. Traditional tracking methods are becoming obsolete as regulations like GDPR and platform changes such as Apple's ATT framework have reduced identifiable user data by 40-60%. Marketers now face a critical challenge: maintaining measurement effectiveness while respecting privacy constraints. AI solutions offer viable alternatives by modeling outcomes where direct tracking is impossible. Key innovations include modeled conversions that maintain 75-85% accuracy despite tracking limitations, causal inference techniques that establish true incremental value with 30% greater precision than correlation-based methods, and privacy-preserving architectures that enable analysis without compromising user data. Organizations implementing these AI approaches have maintained or improved campaign performance while reducing reliance on individual tracking by 50-70%. The shift from deterministic to probabilistic measurement represents not merely a technical adaptation but a fundamental transformation in how advertising effectiveness is evaluated in the privacy-first era.
AI-Driven End-To-End Loan Processing And Servicing: A Data Science Approach For Automated Debt Relief And Financial Inclusion
Conventional processing of lending is hindered by prolonged approval trails, high operational expenditures, and limited ability to scale, which results in credit access barriers for underserved communities. Artificial intelligence radically shifts the lending paradigm by enabling optical character recognition and convolutional neural networks to automate document verification procedures, subsequently transforming the verification process by validating authenticity instantaneously. Machine learning models provide a construct for determining creditworthiness by synthesizing data from the credit bureaus, transactional history, and other unconventional data sources, while natural language processing and behavioral analytics create holistic applicant profiles. Deep learning algorithms identify potential fraudulent activity based on abnormal behavioral patterns and biometrics. Robotic process automation manages repetitive processes, which efficiently assign resource tasks, and AI-supported decision engines provide instant underwriting decisions with regulatory compliance and requirements. Blockchain-based smart contracts provide the best security of disbursement through a tamper-proof, transparent transaction. Collectively, this infrastructure reduces processing time from weeks to minutes, strengthens fraud mitigation solutions, and rapidly scales to address millions of applications. The framework supports establishing debt relief programs and financial inclusion by providing agnostic, rapid, secure, and equitable access to credit to underserved communities, showing improved access due to the existing innovative capabilities of combining robotic process automation with artificial intelligence in financial services.
Conversational Interfaces For Dataops And Pipeline Monitoring: Democratizing Data Infrastructure Management Through Natural Language Processing
Putting conversational interfaces into DataOps platforms makes it much simpler to get to complex data management systems. This paradigm breaks down the obstacles that have prevented non-technical people from keeping an eye on how pipelines are working, spotting system issues, and getting helpful information from data setups. By using AI agents and processing natural language, businesses can change common language into technical actions. The system has AI agents for talking with users, an API for connecting to the platform, and creating responses for easy-to-understand communication. Setting this up means working closely with DataOps platforms like Apache Airflow and Databricks Jobs. This calls for careful data coordination and API methods. Examples show better operations, more people involved, and quicker problem-solving in areas like finance and healthcare.
Pharmaceutical And Life Sciences: Accelerating Discovery With Machine Learning
Machine learning is revolutionizing the pharmaceutical and life sciences industries by transforming drug discovery, manufacturing processes, and personalized medicine approaches. The integration of advanced algorithms has accelerated the identification of therapeutic compounds, optimized clinical trials, and enhanced manufacturing efficiency. Deep learning models enable the analysis of vast chemical spaces, while process analytical technology systems improve production quality control. In genomics, ML algorithms process complex biological datasets to enable personalized treatment strategies. Despite these advances, challenges persist in data standardization, model interpretability, and the requirements for computational infrastructure. The convergence of ML with pharmaceutical sciences continues to drive innovation in healthcare delivery and patient outcomes.
Equitable Allocation And Waitlist Governance For Multi-Channel Commerce: A Framework For Fairness In Scarce Inventory Management
Multi-channel commerce environments face critical ethical challenges in inventory allocation when demand exceeds supply, with inaccurate availability signals and perceived channel favoritism undermining market fairness and institutional trust. This article presents a comprehensive governance framework addressing the systematic misalignment between availability calculations and actual allocation practices, particularly the prevalence of speculative delivery dates unsupported by confirmed supply. Grounded in principles from operations management, supply chain strategy, and big data analytics, the framework establishes three core commitments: publishing only sellable inventory backed by confirmed supply, implementing transparent and auditable allocation rules capable of withstanding public scrutiny, and providing contestability mechanisms that enable stakeholders to challenge allocation outcomes. The article articulates the ethical foundations of availability signaling, demonstrating how truthful market information and non-discriminatory treatment constitute baseline requirements for fair commerce in constrained environments. A cross-functional governance architecture with explicit stakeholder representation and clear decision rights enables coordinated decision-making across organizational domains, while role-based accountability and time-boxed appeals processes create feedback loops connecting operational outcomes to strategic policy refinement. Transparent priority tiers operationalize fairness through single consistent payloads published to all touchpoints, with inventory governance mechanisms explicitly sequencing unmet demand according to published rules backed by confirmed receipts rather than forecasts. Systematic measurement through fairness metrics, scheduled audits, testing edge cases and failure modes, and policy versioning creates an auditable trail that transforms fairness from a static compliance requirement into a dynamic organizational capability. The integration of big data analytics enables real-time monitoring, predictive disruption detection, and continuous improvement in allocation decision quality, while governance councils translate performance data into evidence-based policy refinement that maintains alignment between strategic fairness commitments and operational reality.
Integrating Multidisciplinary Clinical Support Roles To Enhance Coordinated Patient Care: A Qualitative Theoretical Framework Combining Nursing Technicians, Pharmacy Technicians, Center Supervisors, Radiology Technicians, Medical Laboratory Technicians, D
This study explores how multidisciplinary clinical support roles can be systematically integrated to enhance coordinated patient care, emphasizing the critical yet often overlooked contributions of technical and supervisory staff within healthcare systems. Through a qualitative theoretical approach grounded in interpretive constructivism and guided by grounded theory principles, the research developed the Theoretical Framework for Integrated Multidisciplinary Clinical Support (TF-IMCS). The study synthesizes conceptual and empirical literature published between 2015 and 2025, covering 65 relevant sources across seven professional domains: nursing technicians, pharmacy technicians, radiology technicians, medical laboratory technicians, dental and prosthesis technicians, echo specialists, and center supervisors. The findings reveal five interdependent constructs as foundational to multidisciplinary integration: role clarity, inter-professional communication, supervisory integration, organizational support, and collaborative readiness. Theoretical mapping demonstrated that strong organizational and supervisory structures enhance communication effectiveness and teamwork, while conceptual triangulation confirmed coherence with global healthcare frameworks such as the WHO’s Interprofessional Collaboration Model and Donabedian’s Structure–Process–Outcome model. The results indicate that technical and supervisory personnel are not peripheral actors but central participants in information flow, decision-making, and patient outcomes. The study concludes that recognizing and embedding these support roles into formal care frameworks significantly strengthens coordination, efficiency, and quality of patient care. It also highlights the need for future empirical validation of the TF-IMCS model across different cultural and organizational contexts to test its adaptability and real-world applicability. Overall, this research provides a comprehensive theoretical foundation for reshaping multidisciplinary collaboration and optimizing healthcare delivery through inclusive, system-based integration of all clinical support roles.
Abdulrahman Hamid Awad Al-Rashidi, Jazaa Awad Ali AlAhmmadi, Ahmed Ghanem Obaidallah Al-Harbi, ahmed ghanam alharbi, Hassan Salim Mahl Al-Rahili, Hassan Salem Alrehaili,
Sultan mohammad rimthan alsehli, Awad Raziq marzouq Al-Jabri, FAHAD Abdullah Ail Alshrif, Abdullah Jribi' Samran Al-Sahli, Maha Abdullah M. Alamiri, Amal Hamad AlAqidi, Abdullah juraybia Samran Alsali
Generative Artificial Intelligence in Banking: Risk Management Frameworks for Responsible Deployment
By means of improved customer interactions, automated content generation, and simplified internal procedures, generative artificial intelligence is transforming banking activities. Financial institutions are using GenAI solutions more and more, including code generation tools, customized marketing campaigns, automated regulatory reporting, artificial data creation for model training, and intelligent virtual assistants. While allowing privacy-preserving data use, these solutions provide major operational efficiencies and customer experience enhancements. Adoption of GenAI, meanwhile, brings considerable dangers, including output hallucinations, algorithmic bias amplification, data security flaws, and legal compliance difficulties unique to highly controlled financial markets. Good GenAI integration calls for thorough governance frameworks including human monitoring mechanisms, prompt engineering standards, model validation protocols, and strong auditability systems. Companies need to define precise risk tolerance levels, use constant monitoring systems, and create GenAI-specific failure mode-tailored incident response plans. Future developments suggest evolution toward domain-specialized large language models, real-time workflow integration, and advanced federated learning designs. Regulatory frameworks keep changing to handle GenAI-specific risks while preserving financial system stability. Banks reaching their best GenAI value realization exhibit systematic risk management, cross-functional teamwork, and proactive engagement with changing regulatory requirements, thereby preparing themselves for competitive benefit and operational resilience.
Finops In Multi-Cloud AI Environments: Financial Governance Strategies For Complex Computational Workloads
This article explores the evolving discipline of FinOps in multi-cloud AI environments, examining the unique financial governance challenges posed by distributed AI workloads. It investigates how organizations navigate complex pricing structures, resource scarcity, and cross-departmental attribution while implementing centralized visibility platforms and standardized resource tagging. The text delves into AI-driven optimization methodologies that create recursive efficiency improvements through intelligent workload placement, anomaly detection, resource configuration optimization, and predictive forecasting. Provider-specific considerations across AWS, Azure, and Google Cloud Platform are evaluated, with particular attention to commitment-based discount mechanisms and inter-cloud data transfer costs. The article concludes that effective financial governance frameworks represent a competitive differentiator for organizations deploying AI across heterogeneous cloud environments, enabling sustainable innovation through efficient resource utilization.
Parental Awareness Of Congenital Missing Teeth In Children In The Aseer Region, Saudi Arabia: A Cross-Sectional Study
Background: Congenitally missing teeth are the most common developmental dental anomaly that has profound functional and psychosocial consequences for the child, where parents' awareness is of great importance in early detection and timely interventions. Aim: The study was conducted to evaluate the awareness, perception, and knowledge of parents in the Aseer Region, Saudi Arabia, regarding CMT in children. Methods: This is a cross-sectional survey using a quantitative methodology conducted among 500 parents in the Aseer region; data on socio-demographics, CMT awareness, perceived causes, perceived severity, impacts, knowledge about treatments, and sources of information were gathered by means of an administered structured questionnaire. Data was analysed by descriptive statistics and chi-square tests. Results: This study demonstrated the presence of an enormous awareness gap; 52.6% of the parents reported never having heard of CMT while 49.2% were unaware that children could be born without permanent teeth. The general impression about the gravity of the condition is good; 78% gave ratings of 'very' and 'moderately' serious in total, while the effect of the condition on 'self-confidence' and 'pronunciation' is well-recognized (83 and 77%, respectively). A wide "action gap" is observed as only 29.6% had their children checked for CMT by a dentist. A significantly higher Awareness of university-educated parents compared lower-educated subjects50.5% vs. 34.0%, p=0.002. Conclusion: A significant gap in awareness of CMT between parents residing in the Aseer region and a dire need for targeted public health educational campaigns. Implementation should be through professional dental routes and through digital media to be able to catch a large number of parents and cast a wide net
Mastour Safar Alshahrani, Jamel Mohammed Alsaeed, Ali Ahmed Al Bu Kafitah, Mohammed Khalid Alqahtani
The Evolution And Infrastructure Of Generative AI Chatbots: A Technical And Strategic Analysis
The rapid advancement of large language model-based conversational AI systems represents a transformative milestone in artificial intelligence, fundamentally reshaping human-computer interaction paradigms across diverse application domains. This article provides a comprehensive technical and strategic analysis of the infrastructure, architectural considerations, and operational challenges inherent in deploying generative AI chatbots at scale. To examine the competitive landscape of hardware acceleration strategies, contrasting general-purpose GPU architectures with custom silicon solutions such as Google's Tensor Processing Units and Meta's specialized inference accelerators, highlighting the nuanced trade-offs between performance, flexibility, and cost-effectiveness. The article explores the principles of hardware-software co-design essential for optimizing transformer-based architectures, including the synergistic integration of computational substrates, memory hierarchies, and distributed training frameworks that enable efficient processing of models containing billions of parameters. Performance requirements for commercial viability are evaluated, encompassing throughput optimization, ultra-low latency targets, and the continuous innovation in serving infrastructure necessary to maintain competitive advantage amid proliferating proprietary and open-source alternatives. Critical attention is devoted to security, privacy, and trust considerations that extend beyond traditional cybersecurity paradigms, including vulnerabilities specific to AI systems, regulatory compliance frameworks, and the imperative for comprehensive defense mechanisms protecting sensitive user data throughout the inference pipeline. This interdisciplinary examination synthesizes technical, economic, and ethical dimensions of conversational AI deployment, providing stakeholders with insights essential for navigating the complex trade-offs between model capability, operational efficiency, and responsible system design in an era of accelerating artificial intelligence adoption.
Artificial Intelligence In Retail: Transforming Customer Experience Through Intelligent Systems
The retail enterprise undergoes a deep trade with artificial intelligence adoption, fundamentally transforming consumer engagement models and operational paradigms. Modern AI deployments move beyond conventional demographic segmentation techniques, making an allowance for personalized experiences primarily based on advanced reasoning, interpretation, and contextual response generation. Generative language models enable adaptive content material creation, customized product bundling, and mission-driven discovery reports in virtual and physical retail spaces. Conversational AI systems choreograph client interactions through natural language understanding pipelines, ensuring seamless transition between automated and human representatives while keeping contextual continuity intact through privacy-preserving handoff protocols that implement time-limited session data retention with automatic purging of conversation transcripts beyond operationally necessary durations. Physical retail businesses are assisted by AI-powered copilot solutions that complement associate capabilities, increasing the speed of service and solution rates without the need for large retraining exercises. Agentic retrieval-augmented generation architectures can facilitate autonomous reasoning capabilities, iterative refinement processes, and multi-step information synthesis for more complex product discovery situations. Achievement in implementation depends on sound governance structures that include data validation protocols, drift detection mechanisms, quality assurance processes, and safeguarding guardrails that guarantee algorithmic outputs fall in line with organizational values and regulatory compliance. The intersection of generative capability with demonstrated machine learning methods sets new competitive standards, moving retail operations from rules-based segmentation to actually individualized patron experiences that produce measurable gains in engagement, conversion, and loyalty metrics.
Middleware For Social Impact: Integrating Emergency Response Systems For Humanitarian Relief
This article presents a novel middleware architecture designed to address critical information exchange challenges in disaster response ecosystems. The middleware framework facilitates seamless integration between siloed humanitarian organizations through standardized APIs, secure data exchange protocols, and IoT integration capabilities optimized for austere environments. The socio-technical systems theory provides the theoretical foundation, highlighting how effective disaster response emerges from the intersection of technological infrastructure and organizational practices. By examining current interoperability challenges and existing integration approaches, the article identifies a substantial gap in standardized middleware protocols for emergency coordination. The proposed open-source, cloud-native architecture incorporates semantic interoperability layers, privacy-preserving data exchange mechanisms, and resilient communication capabilities designed to function across diverse connectivity conditions. A detailed case study from Indian healthcare networks demonstrates how middleware implementation transformed emergency response coordination, reducing resource mobilization delays while enabling complex cross-organizational resource sharing. Mixed-methods assessment reveals both technical performance improvements and the critical organizational factors that influence adoption patterns across different institutional contexts. The cost-benefit analysis establishes the economic viability of middleware implementation compared to traditional coordination methods, while highlighting how network effects enhance value as additional organizations join the system.
Momentum To Value: A Strategic Framework For Activation In SMB Fintech & Communication Platforms
The Momentum to Value (M2V) framework presents a paradigm shift in the conceptualization and implementation of the concept of activation in the SMB financial technology platform. Raising the level of engagement traditionally focused on engagement metrics, M2V reinvents the concept of activation as a coordinated process that keeps the trajectory of delivery of verifiable financial returns. The framework takes into account the core issues of fintech activation, such as regulatory resistance, multi-session workflows, and varying business requirements. M2V allows platforms to ensure user momentum by keeping them at friction points, plus cross-channel orchestration, to prevent crossing any state of true value. It is revealed that application in diverse financial settings has shown great enhancement to the outcome of activation, with specific success in catering to segment-specific requirements of service professionals, sellers of products, and marketplace workers. M2V will change activation into a conversion metric by making organizational structure and measurement systems more value-focused as opposed to vanity metrics, turning it into an engine of sustainable growth that propels meaningful financial results of small and medium businesses.
Defending The AI-Powered Commerce Stack: A Security Framework For Prompt Injection, Review Integrity, And Privacy In Genai Retail Systems
Generative AI is fundamentally changing digital retail through intelligent search, conversational assistants, personalized recommendations, and generating dynamic content. The use of generative AI has created significant security gaps that disrupt customer trust, regulatory compliance, and operational integrity. Prompt injection attacks exploit untrusted content in product descriptions and user-generated reviews to manipulate assistant behavior and trigger unauthorized actions. AI-generated synthetic reviews undermine rating authenticity and distort marketplace signals at an unprecedented scale. Data poisoning compromises catalog systems and vector embeddings that power recommendation engines, degrading relevance and introducing malicious content propagation. Privacy leaks emerge from over-permissioned tool access and inadequate PII protection in conversational contexts. This framework presents layered defense architectures addressing each threat vector through input isolation, provenance tracking, quarantine systems, and access minimization. Cross-cutting governance mechanisms integrate brand guardrails, policy-as-code enforcement, and human-in-the-loop controls. Observability infrastructure enables continuous monitoring through decision logging, drift detection, and executive dashboards. The framework provides actionable guidance for engineering, security, legal, and customer experience teams building resilient AI-powered commerce systems that balance innovation with protection against adversarial manipulation and privacy violations.
Corporate Model Risk (Cmor) Hadoop Infrastructure For Enterprise Risk Analytics
Corporate Model Risk (CMoR) Hadoop Infrastructure is an innovative model to provide financial institutions with growing model governance challenges amidst the growing complexity of regulatory demands. This platform uses technologies of the Apache Hadoop ecosystem to provide a single platform to manage model risk across an enterprise, bridging the gap between the governance requirements and the technological ability. The architecture incorporates distributed storage and parallel computation with extensive security controls that were tailored to model validation processes. Multi-layered structure of governance provides regulatory alignment by ensuring full data lineage, role-based access control, and automated lifecycle management. The high level of analytical facilities provides the option of validating models simultaneously, large-scale benchmarking, as well as risk aggregation, which was limited by traditional infrastructures. The results of implementation indicate that there is a significant enhancement of the efficiency of validation, maturity of governance, and compliance with regulations among the various financial institutions, and a groundbreaking method of management of enterprise model risk in a regulated incentive setting.