Top 10 prompts to try with hx's Actuarial Agent
3 minutes
Real-world examples to move from spreadsheet firefighting, to strategic impact
Stop fixing spreadsheets. Start shaping strategy.
Today’s actuaries are pulled in every direction: wrestling legacy spreadsheets, cleaning inconsistent data, and translating technical logic into boardroom English.
Today, hx’s Actuarial Agent is helping insurers change that.
Built into the hx platform, it empowers actuaries to move faster, think bigger, and collaborate more effectively across the business. But unlocking its full potential starts with one key habit: how you prompt it.
These aren’t generic AI hacks. These are real-world, high-impact prompts tested with actuarial teams inside global insurers, designed to free up time, elevate model quality, and shift your day from reactive to strategic.
Prompt 1: “Explain this model to a non-actuary.”
Use case: Internal communication & model governance
Why it matters: Turns complex pricing logic into plain-language summaries
The win: Actuaries can quickly brief CUOs, underwriters or regulators without spending hours redrafting documentation
Prompt 2: “Spot potential inconsistencies in this model.”
Use case: Model QA and validation
Why it matters: Identifies missing variables, outdated assumptions, and duplicated logic (especially from inherited Excel models)
The win: Cleaner, more reliable code that’s easier to maintain and scale
Prompt 3: “Add policy details with these nodes.”
Use case: Model development
Why it matters: Build or update model logic using plain English inputs
The win: Actuaries stay focused on innovation, not syntax
Prompt 4: “Build an integration with Google Maps.”
Use case: Enrich models with third-party data. For example, with a Google maps integration you can pull a view of a property to get location context, property characteristics at a glance, and adjacency risk.
Why it matters: Location insights add context to the risk and validate submission data.
The win: Underwriters gain deeper visibility without extra overhead
Prompt 5: “Explain this error and propose fixes.”
Use case: Debugging and troubleshooting
Why it matters: AI explains what’s going wrong, and suggests potential solutions without hiding behind jargon
The win: Faster resolution, deeper understanding, and upskilled actuarial teams
Prompt 6: “Reformat this view to look like [insert preference].”
Use case: UI design for underwriter tools
Why it matters: Translate feedback into functional interface changes
The win: Deploy frontend updates quickly — no dev sprint required
Prompt 7: “Update this part of the tool to make it faster and easier to use.”
Use case: Optimising model deployment
Why it matters: Automates UX and performance improvements
The win: Better-performing models, smoother user experience
Prompt 8: “Build a rater based on this spreadsheet.”
Use case: Excel migration
Why it matters: Converts legacy spreadsheets into Python-based models
The win: Accelerate your transition to Renew, even with limited coding skills
Prompt 9: “Recommend alternative algorithm implementations for [insert use case].”
Use case: Enhancing model sophistication
Why it matters: Explores better or more modern actuarial techniques
The win: Grow technical depth without reinventing the wheel
Prompt 10: “Add comments to my model code.”
Use case: Documentation
Why it matters: Auto-generates helpful annotations for collaborators and reviewers
The win: Boost transparency, governance, and onboarding without losing time
From prompt to practice
The power of hx’s Actuarial Agent doesn’t come from generic AI. It comes from deep context-awareness - built for the data structures, rating logic, and real-world constraints of enterprise insurance.
These prompts aren’t shortcuts. They’re how actuarial teams are reclaiming their time, accelerating their work, and building smarter pricing tools across their portfolios.
