Harender Bisht

Talk Title:

Explainable AI for Trustworthy High-Stakes Decision Analytics

Abstract:

AI is increasingly used in insurance, finance, healthcare, and compliance, where decisions must be accurate, understandable, and defensible. Using insurance fraud detection as a practical example, this keynote presents explainable AI as a core component of responsible intelligent systems.

The talk introduces evidence-first interfaces, role-specific explanations, human verification, contestability, and audit trails. It also presents a practical architecture connecting AI models, explanations, human review, governance controls, and decision records. The central message is that explainability should be designed into the decision workflow, not added after model development.

Profile:

Dr. Harender Bisht is a Senior Solution Architect with more than 12 years of experience across enterprise architecture, finance, insurance, reinsurance, data analytics, cloud platforms, and applied AI.

He holds a PhD in Information Technology, with research focused on explainable AI, fairness, accountability, and transparency in high-stakes insurance fraud detection. His current interests include responsible AI, enterprise AI agents, intelligent automation, and auditable decision systems.