Underwriting Narrative

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]