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README.md

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+---
+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+)

ae_summary_by_class.csv

@@ -0,0 +1,45 @@
+underwriting_class,gender,count,mean_qx_expected,mean_qx_actual,mean_ae,death_claims,mean_lapse_rate
+declined,female,76,0.03239521052631579,0.32666718421052626,6.870131578947368,0,0.053489473684210524
+declined,male,68,0.05040880882352942,0.364670794117647,6.9058970588235296,0,0.06844264705882352
+declined,non_binary,2,0.026458000000000002,0.391208,7.466,0,0.0915
+preferred,female,156,0.010462429487179487,0.009595044871794871,0.8005576923076924,1,0.06461153846153846
+preferred,male,138,0.013480717391304348,0.012090521739130436,0.7989202898550725,1,0.06864420289855072
+preferred,non_binary,1,0.012437,0.009101,0.758,0,0.2034
+preferred_plus,female,117,0.007072777777777778,0.005006564102564102,0.6220683760683761,2,0.05991880341880342
+preferred_plus,male,120,0.009640383333333334,0.006706683333333334,0.6228750000000001,0,0.0664575
+preferred_plus,non_binary,4,0.00649,0.00369975,0.57325,0,0.05155
+standard,female,211,0.02076157345971564,0.023644829383886255,1.0517251184834122,10,0.06521800947867298
+standard,male,214,0.03067991588785047,0.03647254672897196,1.0504906542056076,3,0.06752663551401869
+standard,non_binary,5,0.0421342,0.0379796,1.042,0,0.031219999999999998
+standard_plus,female,63,0.008538,0.008617190476190476,0.9516666666666667,0,0.059961904761904765
+standard_plus,male,63,0.007667126984126984,0.007684714285714285,0.949015873015873,0,0.06732857142857143
+standard_plus,non_binary,2,0.0029370000000000004,0.003104,0.95,0,0.0475
+substandard_table_1,female,448,0.01929403125,0.03256805580357143,1.3748415178571427,15,0.07540870535714285
+substandard_table_1,male,447,0.0251552192393736,0.043183404921700225,1.3780626398210292,29,0.06353847874720357
+substandard_table_1,non_binary,3,0.020959333333333333,0.040843,1.3709999999999998,0,0.09549999999999999
+substandard_table_10,male,3,0.16892300000000002,0.6781980000000001,4.131,2,0.0715
+substandard_table_12,female,9,0.03372477777777778,0.32087566666666667,5.577888888888889,4,0.07407777777777778
+substandard_table_12,male,12,0.06724775,0.35747799999999996,5.497916666666666,4,0.07354166666666667
+substandard_table_12,non_binary,2,0.035989,0.35222800000000004,5.6,1,0.04795
+substandard_table_2,female,346,0.02352989306358382,0.05258589306358381,1.6282456647398846,17,0.0683606936416185
+substandard_table_2,male,318,0.03468005031446541,0.07675061320754717,1.6202672955974844,26,0.0684503144654088
+substandard_table_2,non_binary,4,0.067497,0.17060375,1.65175,1,0.097
+substandard_table_3,female,53,0.020302415094339624,0.05524711320754717,1.8760754716981132,3,0.06741132075471698
+substandard_table_3,male,76,0.023425092105263157,0.06623563157894737,1.8733026315789474,6,0.07195394736842105
+substandard_table_4,female,359,0.030337069637883008,0.09883417270194986,2.1302869080779945,36,0.05926518105849582
+substandard_table_4,male,330,0.031508627272727276,0.10411778484848484,2.1280878787878788,37,0.06366727272727273
+substandard_table_4,non_binary,10,0.037431000000000006,0.1123467,2.1023,1,0.055400000000000005
+substandard_table_5,female,401,0.02406545137157107,0.0915165012468828,2.37669825436409,39,0.06183167082294264
+substandard_table_5,male,371,0.024995563342318058,0.09235614824797843,2.376320754716981,45,0.06570754716981132
+substandard_table_5,non_binary,11,0.029406454545454545,0.10096954545454545,2.3631818181818183,2,0.06774545454545454
+substandard_table_6,female,186,0.04230620430107527,0.18110537096774196,2.6143494623655914,35,0.05990430107526882
+substandard_table_6,male,180,0.04549656111111112,0.19398962777777776,2.6238055555555557,37,0.06767888888888889
+substandard_table_6,non_binary,2,0.06615299999999999,0.318246,2.6014999999999997,0,0.02955
+substandard_table_7,female,53,0.05146152830188679,0.18873743396226414,2.870811320754717,13,0.06438113207547169
+substandard_table_7,male,50,0.053252959999999995,0.22096053999999998,2.88112,16,0.060088
+substandard_table_7,non_binary,3,0.12909199999999998,0.5348,2.8593333333333333,1,0.04873333333333333
+substandard_table_8,female,27,0.041642074074074074,0.22956244444444443,3.2652592592592593,7,0.052922222222222225
+substandard_table_8,male,20,0.0350408,0.1890024,3.2673499999999995,6,0.07375000000000001
+substandard_table_8,non_binary,3,0.06877233333333334,0.348188,3.280666666666667,1,0.027399999999999997
+substandard_table_9,female,18,0.07313561111111111,0.3336276111111111,3.767,7,0.05532777777777778
+substandard_table_9,male,15,0.0668656,0.3874606666666667,3.747666666666667,6,0.06505333333333334