"Avinash Reddy Segireddy is an accomplished DevOps and cloud transformation specialist whose work bridges technology and finance. His expertise lies in modernizing cross-border payment infrastructures through automation, scalability, and security. At SWIFT, he has engineered end-to-end CI/CD pipelines and cloud-native deployment frameworks that have significantly improved uptime, compliance, and operational efficiency reducing manual interventions by over 70%. Proficient in AWS, Kubernetes, Docker, Terraform, and Ansible, he builds resilient and self-healing systems integrated with monitoring tools such as ELK, Splunk, and App Dynamics. Avinash also pioneers AI/ML and Generative AI applications in DevOps, enabling predictive monitoring, anomaly detection, and intelligent deployment automation. His research and implementations in self-healing architectures and AI-driven DevSecOps pipelines are recognized for advancing secure, adaptive, and compliant financial technology ecosystems—making him a transformative figure in the evolution of cloud-native, AI-powered financial infrastructure."

DevOps, Cloud-Native, Financial Systems AbstractAI-Driven Integrated Framework for Real-Time Fiscal Impact Analysis in Government Financial Management A real-time fiscal impact analysis capability would improve the accuracy and transparency of government financial management and enable quicker, evidence-informed decisions in response to dynamic economic conditions. Such capability supports the automatic generation of models and assumptions required to assess the fiscal consequences of policy proposals, anticipated revenue collection, or spending out-turns as well as delivering counterfactual analyses that compare likely trajectories under different scenarios. A theoretically sound architecture defines the supporting integrated data-engineering and computing framework together with a set of analytic methods that encompass the use of economic models, parameterization, scenario generation, counterfactual and policy simulation techniques, and uncertainty quantification.