---
name: underwriting-narrative
description: "Write the underwriting file narrative for a risk: the risk story, exposure quantification, loss-history read, mitigating and aggravating factors, terms and subjectivities rationale, appetite fit, and a refer-or-bind recommendation. Use when asked to write up an underwriting file, document why we're writing a risk, prepare a referral to a senior underwriter, or justify terms and exclusions on a submission. Produces a complete underwriting narrative ready for the file or referral."
homepage: https://mohitagw15856.github.io/pm-claude-skills/skill/underwriting-narrative.html
metadata:
  {
    "openclaw": { "emoji": "🛡" }
  }
---

# Underwriting Narrative Skill

An underwriting file must let a peer reconstruct *why* the risk was written, at those terms, at that price — years later, possibly in front of an auditor or a large loss. This skill writes that narrative: the risk story, the numbers behind it, and the reasoning connecting them to the terms offered.

## What This Skill Produces

- A risk story (who the insured is, what they do, why they're buying now)
- Exposure quantification (values at risk, limits deployed, estimated/probable maximum loss)
- A loss-history read distinguishing frequency from severity signals
- Mitigating and aggravating factors, weighed
- Terms rationale — subjectivities, exclusions, deductibles, each with a reason
- Appetite fit and a refer-or-bind recommendation

## Required Inputs

Ask for what's missing; from a thin submission, proceed and mark gaps `[information required before bind]`:

- **The risk** — insured, operations, geography, line of business
- **Exposure figures** — sums insured/TIV, revenue, headcount, limits sought
- **Loss history** — ideally 5 years, with dates, causes, incurred amounts, open/closed
- **Proposed terms** — limit, deductible, premium, exclusions, subjectivities
- **Appetite/guidelines context** — target classes, referral thresholds, if available

## Narrative Framework

**Risk story.** Three questions: who is this insured (operations, scale, tenure), what exactly is the exposure (the loss scenarios this line responds to), and *why now* (new buyer, remarketing, mid-term change)? A remarketed risk needs its reason stated — price, service, or non-renewal by the incumbent are very different signals.

**Exposure quantification.** State total values at risk, the limit deployed against them, and an estimated maximum loss with the assumption behind it (e.g. single-site fire, top-location concentration). Limits materially above realistic maximum loss, or below it, both need a sentence.

**Loss-history read.** Separate the two signals:
- **Frequency** (many small losses) → a process/controls problem; responds to deductibles and risk management conditions.
- **Severity** (rare large losses) → a volatility/limits problem; responds to price, limit management, and exclusions.
Compute a rough loss ratio against premium if figures allow. Narrate any single loss over ~20% of annual premium individually: cause, fix, recurrence risk. A clean record with low tenure is *absence of data*, not evidence of quality — say so.

**Mitigating vs aggravating.** List both columns honestly. Mitigants must be verifiable (sprinklers *confirmed*, not "believed"); unverified mitigants become subjectivities.

**Terms rationale.** Every non-standard term earns its line: each exclusion tied to an exposure you're declining to price; each subjectivity with a deadline and what happens if unmet; deductible tied to the frequency read.

**Appetite fit and recommendation.** In / edge-of / outside appetite, against which guideline. Recommend **bind**, **bind subject to**, **refer** (naming the referral trigger hit), or **decline** — with the one-paragraph reason.

## Output Format

### Underwriting narrative: [insured / line / inception date]

**1. Risk story** — who, what, why now.
**2. Exposure** — table: values at risk | limit sought | EML basis | premium.
**3. Loss history** — frequency vs severity read, loss ratio, large-loss narratives.
**4. Factors** — mitigating | aggravating, two columns, weighed in a closing sentence.
**5. Terms & subjectivities** — each with rationale and deadline.
**6. Appetite fit** — guideline cited, in/edge/outside.
**7. Recommendation** — bind / bind subject to / refer / decline, with reason.

End with: *"This narrative is analytical support, not a binding decision. Authority, referral, and bind decisions follow your organisation's underwriting guidelines and applicable regulation."*

## Quality Checks

- [ ] The "why now" of the submission is answered, especially for remarketed business
- [ ] Loss read explicitly separates frequency from severity and states which one drives terms
- [ ] Every exclusion and subjectivity has a stated rationale; subjectivities have deadlines
- [ ] Unverified mitigants are converted to subjectivities, not counted as credits
- [ ] Recommendation names the specific referral trigger if referring
- [ ] Data gaps are marked `[information required before bind]`, not papered over

## Anti-Patterns

- [ ] Do not write a description in place of a narrative — every fact must connect to a term, a price, or the recommendation
- [ ] Do not treat a short clean loss record as proof of good risk — label it as limited data
- [ ] Do not list a mitigant you cannot verify without making it a subjectivity
- [ ] Do not bury an outside-appetite feature in the middle of the file — surface it in the recommendation
- [ ] Do not invent loss figures or survey findings — mark unknowns `[to confirm]`