{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/d11c5fce945649c39c44e5acfcea53d5\" frameborder=\"0\" width=\"1662\" height=\"1246\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1246,"width":1662,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1246,"thumbnail_width":1662,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/d11c5fce945649c39c44e5acfcea53d5-d5a913df4a949760.gif","duration":186.432,"title":"Auto Insurance Claims Regression Agent Demo","description":"This Loom demonstrates an agent that auto-approves auto-insurance claims and escalates potential fraud patterns for manual review. The speaker, Pawan, explains that the system learns from each claim and, if it detects a pattern such as “closed in 40 minutes with no inspection” matching a known striping pattern at the same car park, it raises a nominated case. It then allows a reviewer to mark it as manually reviewed and critical by providing a reviewer name, triggering an agent flow in the next run. The agent is deployed in MongoDB, supports conversation-based access to policies, and includes guardrails that redact sensitive numbers and block system prompt related prompt injection attempts."}