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Panel Discussion: The BFSI Playbook - Scaling AI with Trust and Quality

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Banks and insurers are moving a few pilot projects into production by starting with low‑risk workflows like summarizing claim files and automating test script generation, then graduating agents through guarded stages with human checks; they emphasize trust automation over model novelty, build domain LLMs and drift monitoring, and score systems with a multi‑dimensional AI quality scorecard that measures accuracy, explainability, compliance, and downstream consequences before broad rollout.

AI-written summary · about

  1. 0:00Scaling AI in BFSI When There’s No Room for Error
  2. 1:08Building AI That Can Survive Regulators and Auditors
  3. 4:16What AI Use Cases Are Actually in Production in BFSI?
  4. 4:46From Quality Assurance to AI Governance
  5. 7:52Why Enterprise AI Pilots Stall on Trust, Not Technology
  6. 9:23Moving From Deterministic Testing to Probabilistic Quality
  7. 11:25Testing Whether AI Knows When It Doesn’t Know
  8. 11:57From Test Automation to Trust Automation
  9. 12:58Where Human-in-the-Loop Belongs in AI Development?
  10. 15:01Defining Acceptance Criteria for Probabilistic AI Systems
  11. 17:07Building a Multi-Dimensional AI Quality Scorecard
  12. 18:08Why Domain Expertise Matters More in AI Testing
  13. 19:42Testing Agentic AI: From Intelligence to Autonomy
  14. 21:43Scaling AI Autonomy With Guardrails and Drift Monitoring
  15. 22:47Testing AI Actions, Outcomes, and Real-World Consequences
  16. 23:48Scaling AI From Pilot to Enterprise in BFSI
  17. 26:53Skills Quality Engineers Need for the AI Era
  18. 33:07What Matters More: Better Models or Better AI Governance?
  19. 35:37Governance, Human Judgment, and Data Strategy for Trusted AI
  20. 39:14How AI Regulation Could Shape BFSI Adoption
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