Underwriting and renewal operations
Quote documents read, risk variables extracted, renewal offers prepared within the rules.
Underwriting and renewal operations is an Aginies solution package for the Insurance sector in the “financial analysis & operational intelligence” area. What the agents do: Risk variables extracted from quote and renewal documents; missing items requested from the customer; offer prepared with the pricing engine and underwriting rules; sent if within the authority limit. Expected outcomes: most renewals automatic, offers on time; underwriter time spent on exceptional risks; A measurable rise in the renewal rate. The package uses the Document extraction (Vision), Pricing engine tools, Policy engine, Human-in-the-loop capabilities of the Aginies platform and can be watched live through the Policy renewal offer, Underwriting document extraction, Voice claims intake (FNOL) scenarios in the agent library.
Renewal offers are prepared by hand, documents arrive incomplete, underwriters lose time on simple files; when the customer is called late, the policy goes to a competitor.
- 01Risk variables extracted from quote and renewal documents; missing items requested from the customer.
- 02Offer prepared with the pricing engine and underwriting rules; sent if within the authority limit.
- 03Renewal reminder, offer and missing-document communication on the customer’s preferred channel.
- 04Above-limit and exceptional risks land in the underwriter queue with a decision pack.
- Extraction of quote and renewal documents; risk variables
- Renewal offer and price proposal within underwriting rules
- Customer communication: renewal reminder, offer, missing document
- Decision pack for the underwriter and an exception queue
- API access to policy administration and the pricing engine
- Underwriting rules and authority limits
- Sample quote and renewal documents
Derived from the linked scenarios: the supervisors, specialist agents, models and systems that run this package.
Summed from the simulation definitions of the linked scenarios; recalculated for your scale in the discovery session.
- Most renewals automatic, offers on time
- Underwriter time spent on exceptional risks
- A measurable rise in the renewal rate
| Policies renewed automatically | 60–75% |
| Quote preparation time | 60% shorter |
| Renewal rate | +5–8 points |
Target ranges from comparable deployments; baseline and target are fixed together in the first week of the pilot.
Scenario-level approval rules plus the package’s general controls.
- Policy renewal offerPremium increase > 20% or discount > 10% → renewal team lead approval
- Underwriting document extractionLoading ≥ 50% or sum assured > 5,000,000 → senior underwriter approval
- Voice claims intake (FNOL)Injury or reserve > 250,000 → warm transfer to a claims specialist
- packagePrices above the authority limit and exceptional risks always under underwriter approval
- packagePrice reasoning and variables used stored in the policy file
- 1 · Documents and rule set · 2 weeksDocument types, field dictionary, matching and tolerance rules written with the finance team; a sample set labelled.
- 2 · Parallel run · 4 weeksAgent and current process handle the same documents; the difference report reviewed weekly, cut-over when the accuracy threshold is met.
- 3 · Close integrationTied to the month-end calendar; exception queue, audit trail and reconciliation reports opened to internal audit.
This package’s scenarios in the agent library
Policy renewal offer
Policies re-priced, sharp increases go to approval; customer accepts with one tap and the policy is issued.
Underwriting document extraction
Medical reports and labs read and assessed against the manual; underwriters only see files above threshold.
Voice claims intake (FNOL)
The customer calls and describes it; policy found by plate, cover checked, claim opened and adjuster assigned.
Time to move from experimenting with AI to transforming with it.
In a 30-minute discovery session we take your 2–3 priority business problems, show a live demo of a similar scenario, and draft a roadmap that starts with the Value layer.
- Your 2–3 priority problems
- Live demo of a similar scenario
- Roadmap starting with value analysis