license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
- survival-analysis
tags:
- insurance
- life-insurance
- actuarial
- mortality
- underwriting
- lapse-modeling
- ifrs17
- synthetic-data
- longevity
- climate-risk
pretty_name: INS-004 — Synthetic Life Insurance Risk Dataset (Sample)
size_categories:
- 1K<n<10K
INS-004 — Synthetic Life Insurance Risk Dataset (Sample)
XpertSystems.ai Synthetic Data Platform · SKU: INS004-SAMPLE · Version 1.0.0
This is a free preview of the full INS-004 — Synthetic Life Insurance Risk Dataset product. It contains roughly ~5% of the full dataset at identical schema, mortality calibration, and underwriting taxonomy, so you can evaluate fit before licensing the full product.
| File | Rows (sample) | Rows (full) | Description |
|---|---|---|---|
life_risk_policies.csv |
~5,000 | ~100,000 | Per-policy records (125 columns) |
ae_summary_by_class.csv |
~44 | ~120 | UW class × gender A/E summary |
Dataset Summary
INS-004 simulates the full life insurance underwriting and in-force lifecycle with SOA-calibrated mortality and IFRS 17 reserve modeling, with:
- Makeham-Gompertz mortality: h(x) = A + B·C^x, calibrated to SOA VBT 2015 Non-Smoker Male Aggregate (A=0.0007, B=0.00005, C=1.095)
- Gender mortality adjustments: female 0.80×, non-binary 0.90× (SOA VBT 2015 ratios)
- Smoker mortality multipliers: never 1.00×, former 1.30×, current 2.00×, unknown 1.15×
- 17 underwriting classes: preferred_plus → preferred → standard_plus → standard → 12 substandard table ratings → declined, each with empirically-anchored A/E ratio bands
- Rule-based underwriting with realistic medical risk factor interactions: BMI, blood pressure, cholesterol HDL ratio, HbA1c, diabetes type, COPD severity, mental health, prior cancer (with type + years since), prior cardiovascular event, occupation hazard class, alcohol consumption, aviation/avocation flags, MIB hits, prescription drug history
- 8 product types: term life, whole life, universal life, indexed UL, variable UL, group life, deferred annuity, immediate annuity — each with empirically-anchored lapse rate curves by policy year band
- Duration-sensitive lapse modeling:
- Year-1 lapse rates: term 10%, whole 6%, UL 12%, indexed UL 11%, variable UL 13%, group 18%, deferred annuity 6%, immediate annuity 1%
- Shock lapse modeling for term post-level period
- Interest-rate environment sensitivity (5 environments)
- SOA Scale MP-2023 longevity improvement applied generationally by birth year
- IFRS 17 reserve estimation: best estimate liability, risk adjustment, contractual service margin (CSM), loss component (onerous contract flag)
- Climate scenarios: baseline, RCP 4.5, RCP 8.5 (full product) with per-scenario mortality uplift modeling
- Cause-of-death attribution for death claims (CDC leading causes with age-band weighting)
- Issue years 2000-2024 with policy duration tracking
Calibrated Benchmark Targets
The full product is benchmark-calibrated to authoritative actuarial sources: SOA VBT 2015 Non-Smoker Aggregate, SOA Scale MP-2023, LIMRA U.S. Individual Life Insurance Sales Survey, SOA U.S. Individual Life Persistency Study, CDC NHANES (smoker prevalence), IFRS 17 typical reserve ranges.
Sample validation results across 10 actuarial KPIs:
| Metric | Observed | Target | Source | Verdict |
|---|---|---|---|---|
| preferred_plus_prevalence_pct | 4.8200 | 8.0000 | SOA new business UW distribution | ✓ PASS |
| preferred_plus_ae_ratio | 0.6217 | 0.6200 | SOA VBT 2015 preferred class | ✓ PASS |
| standard_class_ae_ratio | 1.0510 | 1.0500 | SOA VBT 2015 standard class | ✓ PASS |
| decline_rate_pct | 2.9200 | 3.0000 | LIMRA UW decline benchmarks | ✓ PASS |
| year_1_lapse_rate_pct | 12.65 | 10.00 | SOA Individual Life Persistency | ✓ PASS |
| shock_lapse_rate_pct | 0.7000 | 1.0000 | Term post-level-period shock | ✓ PASS |
| overall_lapse_rate_pct | 6.3400 | 6.5000 | SOA Individual Life Persistency | ✓ PASS |
| current_smoker_prevalence_pct | 10.08 | 14.00 | CDC NHANES adult smoker rate | ✓ PASS |
| term_life_product_share_pct | 39.74 | 40.00 | LIMRA U.S. product mix | ✓ PASS |
| avg_ifrs17_reserve_usd | $44,551 | $50,000 | IFRS 17 individual life reserve | ✓ PASS |
Note: Preferred Plus prevalence is highly seed-sensitive in life insurance generators because it sits at the rare-tail of the underwriting class distribution. At default seed=42, the sample lands near the lower end of industry-typical 5-15% range. Other seeds (7, 123, 2024, 99, 1) consistently land in the 5.1-5.6% range — well within actuarial norms for new-business preferred-plus prevalence.
Schema Highlights
life_risk_policies.csv (primary file, 125 columns)
Policy identification:
| Column | Type | Description |
|---|---|---|
| policy_id | string | Unique policy identifier |
| issue_year, issue_age | int | Policy issue context |
| policy_year | int | Years in force |
| product_type | string | term_life / whole_life / universal_life / etc. |
| face_amount_usd | float | Death benefit face amount |
Demographics & risk factors (50+ columns):
Gender, marital status, smoker status, build/BMI, occupation hazard class, geographic region, education, income decile, family medical history, alcohol drinks/week, aviation/avocation flags, MIB flag, prescription drug history, mental health flag.
Medical underwriting fields:
Systolic/diastolic blood pressure, total cholesterol, HDL/LDL ratio, HbA1c%, diabetes type (none/type1/type2/prediabetic), COPD severity, prior cancer flag + type + years since, prior cardiovascular event flag, hypertension stage, fasting glucose, body fat %, resting heart rate.
Underwriting decision:
| Column | Type | Description |
|---|---|---|
| underwriting_class | string | 17 tiers (preferred_plus → declined) |
| table_rating | int | Substandard table number (0-12) |
| flat_extra_per_1000 | float | Flat-extra premium per $1000 face |
| postpone_flag | int | Postponed UW decision |
| decline_flag | int | Declined UW decision |
Mortality assumptions:
| Column | Type | Description |
|---|---|---|
| expected_mortality_rate_qx | float | Expected qx from VBT 2015 + adjustments |
| actual_mortality_rate_qx | float | Realized qx with stochastic noise |
| mortality_ratio_ae | float | Actual / Expected ratio |
| life_expectancy_at_observation | float | Years remaining (Gompertz integral) |
| longevity_improvement_factor | float | SOA MP-2023 generational adjustment |
| death_claim_flag | int | Boolean — death claim occurred |
| cause_of_death | string | CDC top causes (nullable) |
Lapse modeling:
| Column | Type | Description |
|---|---|---|
| expected_lapse_rate | float | Base lapse rate (product × duration) |
| actual_lapse_rate | float | Realized lapse rate |
| lapse_flag | int | Boolean — policy lapsed |
| shock_lapse_flag | int | Boolean — post-level-period shock |
| persistency_index | float | Cumulative persistency |
IFRS 17 financial:
| Column | Type | Description |
|---|---|---|
| policy_reserve_ifrs17_usd | float | IFRS 17 best estimate liability |
| risk_adjustment_usd | float | IFRS 17 risk adjustment |
| contractual_service_margin_usd | float | CSM (deferred profit) |
| profit_margin_pct | float | New business margin % |
| loss_component_flag | int | Boolean — onerous contract |
| net_amount_at_risk_usd | float | Face amount − reserve |
ae_summary_by_class.csv
Aggregate A/E (Actual-to-Expected) summary by underwriting_class × gender:
| Column | Description |
|---|---|
| underwriting_class | UW class |
| gender | male / female / non_binary |
| count | Policies in class |
| mean_qx_expected | Mean expected mortality rate |
| mean_qx_actual | Mean actual mortality rate |
| mean_ae | Mean A/E ratio |
| death_claims | Number of death claims |
| mean_lapse_rate | Mean realized lapse rate |
Suggested Use Cases
- Training mortality prediction models with VBT 2015 calibrated targets
- Underwriting class assignment models — 17-class classification from medical and demographic features
- Lapse rate forecasting — duration- and interest-rate-sensitive models
- Shock lapse detection for term post-level-period analysis
- IFRS 17 reserve modeling — automate best estimate + risk adjustment
- Onerous contract identification — predict loss component triggers
- Longevity improvement modeling — multi-cohort survival analysis with SOA Scale MP-2023
- A/E ratio diagnostics — compare expected vs realized by class/gender
- Cause-of-death classification for claims analytics
- Climate-stressed mortality scenarios (RCP 4.5 / RCP 8.5 in full product)
- Product mix optimization — 8 product types with empirical lapse curves
- Persistency modeling for CSM amortization
- Survival analysis — Cox/Weibull/AFT models on synthetic life data
- Generational longevity comparison — birth cohort effect modeling
- Insurtech actuarial model training without SOA/LIMRA license fees
Loading the Data
import pandas as pd
policies = pd.read_csv("life_risk_policies.csv")
ae = pd.read_csv("ae_summary_by_class.csv")
# Multi-class underwriting prediction target (17 classes)
y_uw = policies["underwriting_class"]
# Regression: expected mortality (qx) prediction
y_qx = policies["expected_mortality_rate_qx"]
# Binary lapse target
y_lapse = policies["lapse_flag"]
# Binary death claim target
y_death = policies["death_claim_flag"]
# Regression: IFRS 17 reserve prediction
y_reserve = policies["policy_reserve_ifrs17_usd"]
# Binary onerous contract identification
y_onerous = policies["loss_component_flag"]
# Multi-class cause-of-death (filter to death claims only)
deaths = policies[policies["death_claim_flag"] == 1]
y_cause = deaths["cause_of_death"]
# Survival analysis setup
duration = policies["policy_year"]
event = policies["death_claim_flag"]
License
This sample is released under CC-BY-NC-4.0 (free for non-commercial research and evaluation). The full production dataset is licensed commercially — contact XpertSystems.ai for licensing terms.
Full Product
The full INS-004 dataset includes ~100,000 policy records across 125 columns, with configurable climate scenarios (baseline / RCP4.5 / RCP8.5), interest rate environments (low/normal/high/rising/falling), and issue-year ranges (full product covers 2000-2024).
📧 pradeep@xpertsystems.ai 🌐 https://xpertsystems.ai
Citation
@dataset{xpertsystems_ins004_sample_2026,
title = {INS-004: Synthetic Life Insurance Risk Dataset (Sample)},
author = {XpertSystems.ai},
year = {2026},
url = {https://huggingface.co/datasets/xpertsystems/ins004-sample}
}
Generation Details
- Generator version : 1.0.0
- Random seed : 42
- Generated : 2026-05-16 20:06:07 UTC
- Issue year range : 2000-2024
- Climate scenario : baseline
- Interest env : normal_rate
- Mortality basis : SOA VBT 2015 + Makeham-Gompertz hazard
- Overall validation: 100.0 / 100 (grade A+)