File size: 13,581 Bytes
cd08fa9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 | ---
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
```python
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
```bibtex
@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+)
|