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