Mr. Sasibhushan Rao Chanthati

Architecting Next-Gen Financial Systems with AI and Cloud-Native Microservices

Abstract:

Sasibhushan Rao Chanthati, As a Senior Member of IEEE, Baltimore Section, is a recognized industry leader in enterprise systems innovation. Mr. Chanthati has been at the forefront of deploying workflow driven architectures in financial technology, cloud-native automation, and intelligent compliance systems. His work spans both industry and research recognized through judging appointments for prestigious award platforms and peer review roles for high-impact journals like IEEE Access. Currently serving as Assistant Vice President and Senior Software Engineer at T. Rowe Price. Strong hands-on leadership in enterprise-grade implementations, including ServiceNow-based digital transformation, Mr. Chanthati is a frequent contributor to the AI and communities. His scholarly work is indexed in Clarivate Web of Science, and he is a sought-after speaker for thought leadership on applied AI, secure architecture, and operational excellence. Abstract: The accelerating pace of digital transformation in the financial services industry demands a redefinition of enterprise infrastructure to enable real-time responsiveness, compliance, and intelligent decision-making. This paper presents a framework for designing next-generation financial systems by combining AI models with cloud-native microservices and secure workflow orchestration. Leveraging scholarly contributions to IEEE journals, the author introduces a modular reference architecture that integrates Large Language Models (LLMs), vector databases, Kafka-based real-time ingestion, and zero-trust security patterns. Use cases span revenue automation, AML surveillance, ESG scoring, and investment operations optimization. The methodology includes: LLM-powered insights embedded into microservices via LangChain and Kubernetes Data federation through real-time Kafka streams and vector embeddings (e.g., MongoDB Atlas) Intelligent workflow automation using ServiceNow integrated with AI decision engines Compliance enforcement using containerized audit logs and policy-as-code (OPA/Rego) Implementations that improved SLA compliance by 47%, reduced manual overhead by 60%, and delivered explainable AI outcomes traceable for regulatory audit. This work underscores the convergence of intelligent algorithms and elastic infrastructure as the cornerstone of next-gen financial systems. It serves as a reference for architects, researchers, and industry leaders seeking to bridge the gap between innovation and regulatory resilience. https://www.linkedin.com/in/sasibhushanchanthati/ Keywords: AI in Finance, Cloud Native Microservices, Large Language Models, Kafka, LangChain, ServiceNow, Enterprise Automation, Financial Compliance, Vector Embeddings.