Mortality experience analysis
Actuarial teams cannot compare experience reliably when exposure periods and death records are assembled differently.
The challenge behind the workflow.
Actuarial teams cannot compare experience reliably when exposure periods and death records are assembled differently.
Connect long-duration policy, premium, claims, and actuarial information while retaining historical meaning. Focus on reproducible assumptions, clear customer context, and handoffs between operations, finance, and actuarial teams.
Data to bring together
- Policy exposures
- Death claim records
- Product classifications
Source systems are examples of the data required. Confirm connector availability, permitted access, refresh timing, and the authoritative owner before implementation.
How Genedata supports the work.
Configure one connected workflow, then reuse its mappings, definitions, and review process as the business grows.
- 01
Connect and prepare
Build reproducible exposure periods and reconcile claims to the policy population.
Build the preparation as a reusable visual workflow or reviewed code, with explicit source mappings and validation checks. - 02
Investigate and validate
Analyze experience by approved cohorts while documenting exclusions and data limitations.
Keep shared definitions and source versions with the analysis so another team can reproduce the result. - 03
Publish and review
Deliver a versioned analytical dataset and review-ready experience summaries.
Make the output available to the right people with appropriate access, ownership, and review evidence.
An experience study dataset
Make the next run easier.
Reusable exposure logic reduces recurring preparation and makes assumptions easier to inspect.
Start from one approved example. Save the agreed mappings and definitions, publish the reviewed output, and let the next team follow the same evidence instead of rebuilding the preparation.
Measure your own improvement
Time to reproduce an experience study
Record a baseline before the pilot and compare like-for-like work afterward. Results depend on source quality, configuration, and adoption.Review and responsibility
Actuarial reviewers validate methodology, credibility, and permitted use of sensitive attributes.
AI with professional judgment.
Use AI to organize documents and explain analytical changes. Underwriting, benefit eligibility, and actuarial assumptions need qualified human review.
Try the workflow with your team.
Use representative, approved sample data. Agree the expected output and a review owner before extending the workflow to a live process.