Where AI Fits in the Insurance Lifecycle, Stage by Stage (2026)

AI shows up differently at every stage of the insurance lifecycle. This session walks the full path, from a broker's submission email through underwriting, quoting, binding, issuance, policy servicing, claims, and renewal, naming what AI does at each point and what it hands to the stage after it. Chaz Perera, Co-Founder and CEO of Bevaya, presents. Bevaya is the AI Agent platform built exclusively for insurance, with 120+ production deployments across P&C carriers, brokers, and TPAs. What the session covers: Submission intake. Monitoring broker inboxes, classifying what arrives, extracting data from ACORD forms, supplementals and exposure schedules, running clearance against in-house and competing submissions, screening against appetite, and handing the underwriter a one-page risk summary. Underwriting. Normalizing loss runs across carriers into totals, frequency and severity, and reading statements of value, hazard schedules, and fleet and driver schedules in whatever format they arrive. Quoting. Where AI stops. The underwriter owns the price, the terms and the appetite call, because a price has to be explained to a broker, a policyholder, a regulator or a reinsurer. Binding and issuance. Comparing policy terms side by side, drafting contract language from accepted terms, and reading the issued document back against what was bound before the customer sees it. Policy servicing. Certificate of insurance generation with additional insured validation, endorsement intake and routing for approval, and premium audit classification. Claims. Loss notice intake and indexing, in-force confirmation at date of loss, coverage checks, medical bill and vendor invoice review, legal deadline flagging, and adjuster briefs. Renewal. Assembling the renewal package from exposure and loss data that has stayed clean for twelve months, comparing expiring terms year over year, and identifying accounts at risk of non-renewal. Governance. Every result carries a calibrated confidence score. Highlight Mode shows where on the page an answer came from. Every action is logged to an audit trail. Human in the loop review is built into the platform rather than added afterward. The session closes with four diagnostic questions for choosing a first use case, then a live Q&A covering whether the line at pricing moves over time, how to choose between a slow process and an error-prone one, what happens when documents are handwritten or badly scanned, and the most common mistake teams make when expanding past their first use case. Chapters: 0:00 Welcome and housekeeping 1:46 Most teams know AI has a role somewhere, fewer can point to where 2:01 The insurance lifecycle as one continuous piece of work 2:34 Submission intake: broker inbox monitoring and ACORD form extraction 4:33 Underwriting: loss run normalization and exposure schedules 6:14 Quoting: where AI stops and the underwriter prices the risk 7:25 Binding: policy comparison and contract drafting 9:14 Issuance: document assembly and checking the contract against the bind 10:09 Policy servicing: certificates, endorsements and premium audit 11:57 Claims: loss notice through payment 13:45 Renewal: package assembly and at-risk accounts 15:12 What stays the same: confidence scores, traceability, audit trail, human in the loop 16:46 How to choose where to start 17:48 Session wrap 18:35 Q&A: Does the line at pricing move over time? 20:04 Q&A: Slow process or error-prone process, which do you fix first? 21:57 Q&A: What happens with handwritten notes and bad scans? 24:37 Q&A: The most common mistake when expanding past the first use case See the platform in action: https://www.bevaya.ai/tour?utm_source... Bevaya Labs model benchmarks: https://www.bevaya.ai/benchmarks #insurance #AIagents #underwriting #claims #insurtech