Actuary
Experience Studies & Assumption Setting
What You Do Today
Analyze actual experience versus expected for mortality, morbidity, lapse, and persistency assumptions. You're running A/E studies, updating assumption tables, and justifying changes to auditors.
AI That Applies
ML models that detect cohort-specific experience deviations faster than traditional A/E analysis. AI that identifies emerging trends in mortality or lapse behavior before they reach statistical significance in traditional tests.
Technologies
How It Works
For experience studies & assumption setting, the system identifies emerging trends in mortality or lapse behavior before they r. Machine learning models identify the patterns in historical data that most strongly predict the target outcome, then apply those patterns to score new inputs. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The credibility weighting and professional judgment.
What Changes
Assumption drift detection becomes continuous instead of annual. The AI flags that lapse rates for a specific product/demographic cohort shifted 3 months before your annual study would have caught it.
What Stays
The credibility weighting and professional judgment. Small books need blending with industry data, and the actuary decides how much weight to give your own experience versus the market.
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 experience studies & assumption setting, 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 experience studies & assumption setting 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 experience studies & assumption setting?”
They set the standards for model validation and governance
your data science or analytics lead
“Who on our team has the deepest experience with experience studies & assumption setting, 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 experience studies & assumption setting, 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.