Director of Actuarial
Conduct experience studies and assumption updates
What You Do Today
Analyze actual experience against expected — mortality, morbidity, lapse rates, expense levels. Update assumptions used in pricing and reserving based on emerging experience.
AI That Applies
AI-assisted experience analysis that processes larger datasets faster, identifies cohort-level patterns, and suggests assumption adjustments based on statistical significance testing.
Technologies
How It Works
The system ingests larger datasets faster as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Experience analysis becomes more granular. AI processes data at a finer level of detail, identifying assumption updates that aggregate analysis might miss.
What Stays
Professional judgment on when experience is credible enough to change assumptions and how to balance company-specific data with industry benchmarks.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for conduct experience studies and assumption updates, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long conduct experience studies and assumption updates takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your chief actuary
“What data do we already have that could improve how we handle conduct experience studies and assumption updates?”
They set the standards for model validation and governance
your data science or analytics lead
“Who on our team has the deepest experience with conduct experience studies and assumption updates, and what tools are they already using?”
They build complementary models and share the same data infrastructure
your regulatory filing lead
“If we brought in AI tools for conduct experience studies and assumption updates, what would we measure before and after to know it actually helped?”
AI-assisted rate filings need to meet regulatory standards
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.