Mortality & Longevity Service
age=55, sex=F, table=2017 CSO · US Life Insurance
Top drivers
- Base mortality rates 0.80
- Sex-specific adjustment 0.15
⌁ mcp.call("adw-014") View → Mortality rate / life expectancy for age, sex & table?
age=55, sex=F, table=2017 CSO · US Life Insurance
Top drivers
⌁ mcp.call("adw-014") View → qx lookup + derive life expectancy & survival probabilities
Version 2017 CSO · validated to beat a naive baseline · benchmark: actuarial software vendors
One call returns the answer with its reasoning attached. This is the live sample from build/samples/ADW-014.json.
{
"product_id": "ADW-014",
"entity": "age=55, sex=F, table=2017 CSO",
"score": 0.95,
"trend": "stable",
"confidence": 0.99,
"top_drivers": [
{
"factor": "Base mortality rates",
"contribution": 0.8
},
{
"factor": "Sex-specific adjustment",
"contribution": 0.15
}
],
"prediction_horizon": "1 year",
"recommended_use": "Life insurance underwriting",
"methodology_version": "2017 CSO",
"freshness": "2026-06-15T06:00:00Z",
"coverage": "US Life Insurance",
"qx": 0.0032,
"life_expectancy_years": 31.5,
"survival_probability_10yr": 0.968,
"table_version": "2017 CSO",
"source_lineage": [
"SOA Mortality Tables"
],
"allowed_use": "evaluation, commercial"
} Every product conforms to the 13-field Intelligence Object Model — typed, versioned, and discoverable.
Dashboard
Read the score + drivers in the console.
REST API
/v1/intelligence/adw-014
MCP tool
adw.adw_014
Marketplace
List via data marketplaces + the MCP registry.
White-label
Embed under your own brand (Platinum).
Natural-hazard risk for a US location & which hazards drive it?
Method: EAL = Exposure × Annualized Frequency × Historic Loss Ratio; normalize to national percentile; drivers = top hazards by EAL
PV / annuity / remainder factors for an age, term & §7520 rate?
Method: Standard actuarial PV from §7520 rate + 2010CM mortality
Modeled loss distribution for a portfolio/peril?
Method: Oasis Monte Carlo → AAL + OEP/AEP curves