Biography
Chandra Shekhar Pareek is a Quality Engineering Senior Manager with extensive experience in Quality Assurance Architecture, Advanced Quality Engineering, test automation, and technology transformation within the Life Insurance and Annuity industry. He has led large-scale quality engineering initiatives for complex insurance platforms, integrating modern automation and intelligent testing approaches to improve software quality, delivery efficiency, and production stability.
His professional and research interests focus on intelligent Quality Engineering, AI-assisted test automation, software reliability, policy administration systems, and emerging approaches to testing complex enterprise applications. His work explores how AI and intelligent automation can transform traditional testing practices while maintaining quality, governance, traceability, and risk controls required in highly regulated insurance environments.
Chandra is an active researcher, author, peer reviewer, technical program committee member, and conference judge. He has authored numerous research publications and the book Trust, Verify and Automate. He is a Senior Member of IEEE and holds professional memberships and fellowships in recognized engineering and research organizations. His work bridges enterprise Quality Engineering practice and applied research to advance scalable and intelligent approaches to software quality.
Abstract
Title: From Manual to Intelligent: AI-Powered Quality Engineering in the Insurance Era
Abstract: The insurance industry is at a testing inflection point. Life insurance systems encode thousands of interacting business rules, embed AI-driven underwriting decisions, and process billions in premium transactions — yet traditional quality engineering approaches remain grounded in manual test design and script-based automation that cannot keep pace with the complexity, regulatory demands, or release velocity these systems now require.
To trace the three evolutionary eras of software testing and make the case that the AI-augmented era is not merely faster testing, but qualitatively different testing. The session opens with a real production incident: a life insurance policy issued with the wrong premium that passed every test case. The boundary condition that caused it required a combination of age and health classification that no human tester had imagined — illustrating the fundamental limit of human-scale testing: not effort, but imagination.
