Mr. Venugopal Tamraparani

AI & Gen AI applications for enterprise modernization from complex monolithic to distributed computing in FinTech and Health Tech organizations

Abstract:

As the need for scaling, flexibility and innovation are driving FinTech and Health Tech enterprises away from often complex monolithic architectures towards a distributed computing model. AI and Gen AI is the critical part of that phase in terms of accelerating the modernization journey. Using AI applications like predictive analytics, process automation and decision optimization, monolithic systems can be disaggregated into micro services with minimal disruption, downtime and maximum integration efficiency. AI in the form of Gen AI makes this process an order of magnitude better with the facilities for fast prototyping, code generation from specifications and examples and orchestration systems that help incorporate existing components into working solutions.AI based solutions in FinTech allow for improved fraud detection, customization of financial services and dynamic risk management; distributed systems likewise offer greater robustness and scalability with large transaction loads. In the same way in Health Tech, not only does AI facilitate precision medicine, predictive patient care delivery and bolsters operational efficiency but distributed computing assures secure, high volume data exchange between health systems. This essay discusses the different roles AI and Gen AI can play in enterprise modernization and how design, implementation and performance influences a systems impact. By showcasing individual use case analyses and a comparative analysis of monolithic versus distributed architectures, we illustrate how AI and Gen AI are revolutionizing the enterprise domains for sustainable innovation and growth in these critical verticals.

Profile Brief :

Venugopal is an experienced technology executive  with professional and research  experience in AI/ML, data analysis, and enterprise modernization, with a focus on the fintech, insurance, and healthcare sectors. His most recent research work and patents  include developing AI-powered tools for fraud detection and credit scoring in fintech, as well as predictive analytics and AI to enhance clinical research and patient care  in healthcare.He has led enterprise-wide initiatives to adopt cloud-native architectures, improve scalability, and enable real-time analytics, resulting in improved efficiency and substantial cost savings. His focus on combining technology with business needs has consistently delivered solutions that meet complex challenges and provide long-term value.