2024· International Journal of Artificial Intelligence, Data Science and Machine Learning· Vol 5, pp. 357-363· 0 citations
TL;DR
Operational evaluation demonstrates processing of high-volume daily outstanding records across four exception categories, sub-3-second real-time dashboard refresh, delegate resolution tracking at 98.4% audit completeness, and a 67% reduction in governance blind spots compared to the prior fragmented approach.
Abstract
Large financial institutions operate under continuous compliance, security, and vendor risk obligations distributed across hundreds of teams, systems, and jurisdictions. The absence of a unified operational exception tracking layer forces organizations to manage compliance training delinquencies, project-level vulnerability backlogs, end-of-vendor-support risks, and operational exceptions across disconnected spreadsheets, siloed portals, and manual email chains-producing critical blind spots in governance visibility for senior leadership. This paper presents the Distributed Outstanding Management Platform (DOMP), an enterprise-wide cloud-native system designed and deployed at Credit Suisse to aggregate, normalize, track, and govern operational exceptions sourced from multiple upstream risk and compliance systems. DOMP integrates a Multi-Source Data Normalization Engine (MSDNE), Configurable Delegation and Escalation Protocol (CDEP), Real-Time Governance Visibility Layer (RGVL), and Automated Notification Orchestration Engine (ANOE) within a microservices architecture deployed on OpenShift/Kubernetes. Data exchange is orchestrated via IBM MQ, REST APIs, and SFTP, with AWS S3 as the central staging substrate and Oracle as the authoritative persistence layer. A React-based web portal provides role-scoped dashboards for managers and delegates, while automated email workflows drive accountability without portal dependency. Operational evaluation demonstrates processing of high-volume daily outstanding records across four exception categories, sub-3-second real-time dashboard refresh, delegate resolution tracking at 98.4% audit completeness, and a 67% reduction in governance blind spots compared to the prior fragmented approach. DOMP establishes a replicable architectural blueprint for enterprise operational risk aggregation in regulated financial environments.
Distributed transaction management in multi-tenant cloud platforms becomes difficult when subscription lifecycle operations cross service boundaries, tenant policies, event channels, and billing-facing schemas. This review-based article examines architectural patterns for controlling amendment, renewal, and cancellation flows without relying on a single global commit protocol. The study brings together recent work on cloud-native multi-tenancy, microservice consistency, saga coordination, transactional outbox messaging, event management, and transactional causal consistency. It aims to classify pattern choices that support data integrity, tenant isolation, schema validation, and operational traceability in enterprise cloud platforms. Comparative source analysis, conceptual synthesis, typologization, and analytical generalization guide the review. The results identify three design lines: consistency boundaries, coordination mechanics, and runtime governance. The proposed model connects orchestrated sagas, local transaction ownership, outbox publication, idempotent event handling, and tenant-scoped validation into a single execution logic for subscription lifecycle engines. It gives architects a practical decision frame for platforms under integration pressure.
Kshitiz Srivastava· Universal Library of Enginee...· 0 citations
Financial Companies, Banks and Securities Brokers are changing their trading applications with Cloud technologies like Automated CI CD pipelines, Scalable Infrastructure, so that they can do rapid development, easy deployment provide robust and fast applications.. But they also come with new risks: runtime variability and distributed failures. For trading, this transition is particularly difficult. Cloud-native systems are dynamic and change-driven; trading platforms need predictable latency, continuous availability, auditability and disciplined control over your business. Research on cloud-native computing has provided us with valuable knowledge about service decomposition, orchestration, observability and scalability, but most of that research is on enterprise workloads. On the other hand, the studies of trading systems are based on low latency infrastructure, market microstructure and execution speed and do not take into account the operational realities of cloud-native. In this paper, we will aim to bridge this gap by developing a governance-based perspective for performance engineering in electronic trading systems. We focus on governance, feedback and evidence in terms of four dimensions: latency-aware scalability, transaction-centred observability, risk-based resilience and operational reliability in electronic trading systems. The research-oriented model is intended to inform future empirical testing and to make trading systems scalable, observable, resilient, reliable and auditable in the future.
L. Sharma· Abhiyantran Shastra - The Me...· 0 citations
Examination of application generators as first-class platform infrastructure across their full operational lifecycle establishes that generator value is not realized at project creation alone, and builds up over time through consistent delivery pipelines, less time spent on compliance remediation, faster onboarding, and measuring at the portfolio level.
Anilraj Chennuru· International journal of com...· 0 citations
The Adaptive Risk-Driven DevSecOps Framework (ARDDSF) is proposed, a layered framework for securing multi-cloud enterprise systems in the era of agentic artificial intelligence that bridges DevSecOps automation, AI-assisted security analysis, Zero Trust policy enforcement, and multi-cloud governance.
Nitin Bodade· International Journal of Inn...· 0 citations
Enterprise systems still struggle to move data reliably across cloud and legacy platforms while keeping costs, latency, and risk in check. The author presents an artificial intelligence-enhanced middleware pattern that augments existing integration stacks with telemetry, stream processing, and a lightweight learning loop to predict failures, automatically tune policies, and direct traffic in real time. The architecture couples an integration core comprising application programming interfaces (APIs), messaging, and event flows with a model-driven policy layer and feedback control. The approach is validated through implementations involving retail order orchestration, logistics tracking, and financial services, demonstrating reductions in mean time to resolution of 35% to 55%, message loss of 0.01%, and cloud egress costs of 8% to 12% under production-like loads. The author outlines governance and observability practices that make the pattern portable across TIBCO Software Inc. integration platforms, Apache Kafka, MuleSoft, and cloud-native services without vendor lock-in. The result provides a pragmatic route to resilient, compliant, and scalable integration that organizations can adopt incrementally at enterprise scale without rewriting critical systems.
Tejas Gajjar· International Journal of Inf...· 0 citations
Enterprise F5 BIG-IP load balancer estates, commonly spanning hundreds to thousands of Traffic Management Operating System devices across geographically distributed data centers, represent a class of infrastructure management challenge where the gap between manual operational capability and automation-required operational scale produces measurable security exposure, configuration drift risk, and delivery velocity constraint. This article presents the engineering principles, implementation architecture, and empirical outcomes of enterprise-scale F5 BIG-IP automation using Ansible and iControl REST API-driven orchestration, derived from the author's primary research in automating a 2,500-device production estate across aviation and financial services operational environments. The article addresses four critical automation engineering challenges: accurate dynamic inventory construction from multiple authoritative sources, pre-deployment dependency validation, preventing silent configuration failures, post-execution state verification, distinguishing reported success from actual convergence, and synchronization-aware orchestration for distributed Global Traffic Manager deployments through four production failure case studies whose analysis yielded architectural improvements now forming the operational standards. Performance outcomes from the author's enterprise deployments demonstrate estate-wide CVE remediation in 18 hours for a 2,500-device estate (versus a six-to-eight-week manual baseline), configuration change success rates of 98.4% with a 1.2% rollback rate across 847 production change executions, and zero-downtime software upgrades across High Availability device pairs using boot location management. The article further examines GitOps-based CI/CD integration, enabling application-speed F5 configuration changes through pull-request governance, artificial intelligence-driven predictive maintenance for anomaly detection before the incident threshold is crossed, and intent-based networking as a trajectory toward natural-language load-balancer policy management. The framework presented provides the engineering foundation for architecturally enabling autonomous network operations for routine load-balancer management tasks
U. Soma· International Journal of Eng...· 0 citations
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