--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression tags: - insurance - catastrophe-modeling - reinsurance - actuarial - climate-risk - synthetic-data - hurricane - earthquake - flood - wildfire pretty_name: INS-003 — Synthetic Catastrophe Scenarios Dataset (Sample) size_categories: - 1K 99th percentile) - **AAL** (Average Annual Loss) contribution per event - **Mean damage ratio** (MDR) with vulnerability curve linkage - **Demand surge** multiplier and loss amplification flag - **Loss development factor** (IBNR-style) **Climate scenarios** (configurable in full product): - baseline (current climate) - RCP 4.5 (moderate climate change) - RCP 8.5 (high emissions scenario) — frequency and severity uplifts per peril (e.g. hurricane intensity +6%, flood frequency +18% by 2050) **Exposure characteristics**: - 5 construction types (wood frame, masonry, steel frame, concrete, manufactured) - 6 occupancy classes (residential single/multi, commercial office, retail, industrial, mixed use) - 8 FEMA flood zones - 4 liquefaction risk categories - Replacement cost per square foot, building age, total insured value **Regulatory metrics**: - Regulatory stress test tier - Solvency II SCR event flag - Cat bond attachment threshold ($2B default) ## Validation Results INS-003 is built around **actuarial hard constraints** rather than calibrated benchmarks. Each generated record is validated against 3 mandatory rules: → `insured_loss ≤ economic_loss` (insured cannot exceed total economic) → `net_retained = insured − reinsurance_recoveries` (cedant accounting identity) → `return_period_years = 1 / event_probability` (Poisson rate consistency) Records that fail any constraint are rejected and regenerated (10 attempts max before accepting). Edge cases (tail events, mega-cats, near-misses) are injected at ~1.5% rate to ensure rare-event coverage. Sample validation results: | Metric | Observed | Target | Source | Verdict | |--------|----------|--------|--------|---------| | n_perils_represented | 6 | 6 | 6 peril types in PERILS | ✓ PASS | | n_regions_represented | 6 | 6 | 6 GEOGRAPHIC_REGIONS | ✓ PASS | | insured_loss_constraint_violations | 0 | 0 | Hard constraint: insured ≤ economic | ✓ PASS | | net_retained_constraint_violations | 0 | 0 | Hard constraint: net = insured − recoverie | ✓ PASS | | cat_bond_trigger_rate_pct | 25.640 | 15.000 | OEP percentile > 99 (industry: 10-40%) | ✓ PASS | | loss_ratio_mean | 0.473 | 0.620 | Insured/economic ratio (Munich Re / Swiss | ✓ PASS | | hurricane_cat45_mdr_min | 0.325 | 0.250 | Cat 4/5 minimum MDR — actuarial floor | ✓ PASS | | n_climate_scenarios | 1 | 1 | 1 climate scenario per sample run | ✓ PASS | | return_period_max | 9944 | 10000 | Stochastic catalog horizon (years) | ✓ PASS | | edge_cases_injected | 75 | 75 | ~1.5% of records get edge case injection | ✓ PASS | ## Schema Highlights ### `cat_scenarios.csv` (primary file, 78 columns) **Event identification**: | Column | Type | Description | |------------------------------|---------|----------------------------------------------| | event_id | string | Unique scenario identifier (MD5-hashed) | | peril_type | string | hurricane / earthquake / flood / wildfire / tornado / winter_storm | | peril_subtype | string | Subtype (e.g. "saffir_simpson_4", "subduction") | | scenario_year | int | Stochastic catalog year | | return_period_years | int | Event return period | | event_probability | float | Annual exceedance probability | | geographic_region | string | 1 of 6 regions | | country_iso3 | string | ISO 3166 country code | **Peril-specific intensity fields** (populated based on peril_type): Hurricane: `max_wind_speed_knots`, `central_pressure_mb`, `storm_surge_ft`, `hurricane_category` (1-5), `rainfall_inches_72hr`, `inland_penetration_miles` Earthquake: `moment_magnitude_mw`, `peak_ground_acceleration_g`, `modified_mercalli_intensity`, `liquefaction_risk`, `aftershock_sequence_flag`, `tsunami_trigger_flag` Flood: `flood_type`, `fema_flood_zone`, `inundation_depth_ft`, `inundation_area_sq_miles`, `flood_duration_days`, `peak_discharge_cfs` **Loss decomposition** (USD): | Column | Description | |-------------------------------------|------------------------------------------| | insured_loss_usd | Total insured loss | | economic_loss_usd | Total economic loss (insured + uninsured)| | residential_loss_usd | Residential portion | | commercial_loss_usd | Commercial portion | | industrial_loss_usd | Industrial portion | | auto_loss_usd | Auto portion | | marine_cargo_loss_usd | Marine/cargo portion | | business_interruption_loss_usd | BI portion | | industry_loss_usd | Industry-wide loss for trigger purposes | **Actuarial pipeline**: | Column | Description | |--------------------------------|----------------------------------------------| | aal_contribution_usd | Average Annual Loss contribution | | oep_percentile | OEP curve percentile | | aep_percentile | AEP curve percentile | | probable_maximum_loss_pml_pct | PML as % of TIV | | tail_value_at_risk_tvar | TVaR (CVaR) | | reinsurance_recoveries_usd | Reinsurance recoveries | | net_retained_loss_usd | Net retained loss (cedant) | | cat_bond_trigger_flag | yes/no — OEP > 99th percentile | | regulatory_stress_test_tier | Regulatory stress test classification | | solvency_ii_scr_event | Solvency II SCR event flag | ### `ep_curve_summary.csv` | Column | Type | Description | |------------------------------|---------|----------------------------------------------| | peril_type | string | Peril | | return_period_years | int | Return period (10, 25, 50, 100, 200, 500, 1000) | | oep_loss_usd | float | OEP loss at this return period | | exceedance_probability | float | 1/return_period | ## Suggested Use Cases - Training **catastrophe loss prediction** models — predict insured loss from intensity features - **EP curve construction & validation** — model OEP/AEP curves at multiple return periods (10-1000 year) - **Reinsurance pricing models** — train layer attachment and recovery models - **Cat bond trigger prediction** — multi-peril 99th-percentile detection - **Climate scenario stress testing** — comparison across baseline / RCP4.5 / RCP8.5 climate scenarios (full product) - **Peril-specific vulnerability curve fitting** by construction type - **Geographic risk concentration analysis** — region × peril modeling - **Solvency II SCR event classification** - **PML / TVaR computation** for portfolio risk - **Demand surge multiplier modeling** post-event - **Mean damage ratio (MDR) prediction** by intensity and construction - **Wildfire/flood frequency forecasting** under climate scenarios - **Hurricane track-curvature & forward-speed modeling** - **Earthquake liquefaction risk + tsunami trigger correlation** - **Insurtech catastrophe model training** without proprietary RMS/AIR licenses ## Loading the Data ```python import pandas as pd scenarios = pd.read_csv("cat_scenarios.csv") ep_curve = pd.read_csv("ep_curve_summary.csv") # Multi-class peril classification target y_peril = scenarios["peril_type"] # Regression: insured loss prediction y_loss = scenarios["insured_loss_usd"] # Binary cat bond trigger prediction (rare event ~10-40%) y_cat_bond = (scenarios["cat_bond_trigger_flag"] == "yes").astype(int) # Hurricane-only analysis hurricanes = scenarios[scenarios["peril_type"] == "hurricane"] hurricane_severity = hurricanes["hurricane_category"] # 1-5 # Build your own EP curve by peril peril = "hurricane" sub = scenarios[scenarios["peril_type"] == peril].sort_values("insured_loss_usd", ascending=False) n = len(sub) ranks = (n - sub.reset_index().index) / n # exceedance probability return_periods = 1 / ranks ``` ## 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-003 dataset includes **~50,000 catastrophe scenarios** across all 6 perils, with configurable climate scenarios (baseline / RCP4.5 / RCP8.5), configurable catalog horizons (10,000-100,000 years), and per-peril deep dives for the catastrophe modeling community. 📧 **pradeep@xpertsystems.ai** 🌐 **https://xpertsystems.ai** ## Citation ```bibtex @dataset{xpertsystems_ins003_sample_2026, title = {INS-003: Synthetic Catastrophe Scenarios Dataset (Sample)}, author = {XpertSystems.ai}, year = {2026}, url = {https://huggingface.co/datasets/xpertsystems/ins003-sample} } ``` ## Generation Details - Generator version : 1.0.0 - Random seed : 42 - Generated : 2026-05-16 19:59:28 UTC - Climate scenario : baseline - Catalog horizon : 10,000 years - Architecture : Stochastic event catalog with hard actuarial constraints - Overall validation: 100.0 / 100 (grade A+)