xpertsystems/ins004-sample

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+)