Most actuarial work isn't modeling.
It's reading contracts, drafting memos, and explaining technical results to people who don't want technical explanations.
This is where GenAI actually helps.
Reading Long Documents
You receive a 300-page reinsurance treaty.
You need to understand the terrorism exclusion on page 187.
Traditionally, you'd search the PDF, read surrounding context, cross-reference definitions, and piece together the logic.
With GenAI, you paste the relevant sections and ask:
"What losses are excluded under this terrorism clause?"
It summarizes the language.
Highlights ambiguities.
Points to related sections.
You still verify against the source.
But you've compressed hours of reading into minutes of verification.
Example:
You're reviewing a group life contract with complex beneficiary designation rules.
Instead of rereading the same paragraphs five times, you ask GenAI to explain the hierarchy in plain terms.
It gives you: "Primary beneficiaries split equally unless predeceased, then their share goes to contingent beneficiaries per stirpes."
You confirm that matches the contract language.
Move on.
Writing Summaries
Your pricing model just ran.
You have 47 pages of output tables, sensitivity tests, and diagnostic plots.
The underwriting team needs a one-page summary.
GenAI drafts it.
You feed it:
- Current rate level
- Proposed changes
- Key assumptions
- Loss ratio impacts
It produces a structured summary with:
- Executive overview
- Rate change breakdown by coverage
- Expected impact on retention
- Implementation timeline
You edit for accuracy and tone.
Five minutes instead of an hour.
Example:
Your reserve analysis shows favorable development in commercial auto but adverse movement in general liability.
GenAI turns your technical commentary into a board summary:
"Reserves remained adequate overall, with $2.3M favorable emergence in auto offset by $1.8M adverse movement in liability, primarily driven by higher severity in third-party claims."
You verify the numbers.
Adjust the framing.
Send it.
Explaining Results
A non-actuarial executive asks: "Why did our loss ratio increase if we raised rates?"
You could explain lag effects, exposure changes, and loss trend.
Or you could ask GenAI to draft an explanation at their level.
It produces:
"Rate increases don't affect claims that already happened. We're still paying for last year's accidents at old rates, while only new business gets the new rates. It takes 12-18 months to see the full impact."
You refine it.
Add your company's specific lag period.
Reply.
Example:
Your catastrophe model shows increased hurricane risk for coastal properties.
The CFO doesn't understand why rates need to go up when no hurricanes hit last year.
GenAI drafts:
"The model estimates future risk, not past experience. Even in years without losses, the probability of a major hurricane remains. We're pricing for what could happen, not just what did happen."
You add your specific return period assumptions.
Send it over.
The Pattern
GenAI fits where the work is:
- Language-heavy, not calculation-heavy
- Repetitive, not novel
- Drafting, not deciding
It handles the first 80% of routine communication tasks.
You handle the final 20% that requires judgment.
What This Looks Like Daily
Morning: Paste overnight model results into GenAI. Get initial draft of status email.
Mid-morning: Receive vendor contract amendment. Ask GenAI to summarize changes from previous version.
Lunch: Draft response to regulatory inquiry about reserve methodology. GenAI structures the outline.
Afternoon: Explain credibility weighting approach to pricing analyst. GenAI converts technical explanation to learning-friendly language.
End of day: Summarize key decisions from rate filing meeting. GenAI drafts minutes from your notes.
Each task takes minutes instead of half an hour.
Not because GenAI is smart.
Because these are pattern-completion tasks dressed up as professional work.
GenAI doesn't replace actuarial judgment.
It replaces the typing that surrounds actuarial judgment.
That's enough to matter.