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<n<10K
INS-003 — Synthetic Catastrophe Scenarios Dataset (Sample)
XpertSystems.ai Synthetic Data Platform · SKU: INS003-SAMPLE · Version 1.0.0
This is a free preview of the full INS-003 — Synthetic Catastrophe Scenarios Dataset product. It contains roughly ~10% of the full dataset at identical schema, peril taxonomy, and actuarial calibration, so you can evaluate fit before licensing the full product.
| File | Rows (sample) | Rows (full) | Description |
|---|---|---|---|
cat_scenarios.csv |
~5,000 | ~50,000 | Per-event stochastic cat scenarios (78 cols) |
ep_curve_summary.csv |
~48 | ~48 | OEP exceedance probability curves by peril |
Dataset Summary
INS-003 generates stochastic catastrophe scenarios from a 10,000-year event catalog spanning 6 perils, 6 geographic regions, and full actuarial reinsurance pipeline modeling — the kind of data RMS, AIR, KCC, and Verisk catastrophe models produce, but synthetic and freely usable for research.
6 perils with peril-specific physics:
- Hurricane: max wind speed (knots), central pressure (mb), storm surge (ft), Saffir-Simpson category 1-5, radius of max winds, forward speed, track curvature, rainfall (72hr), inland penetration
- Earthquake: moment magnitude (Mw), hypocenter depth, rupture length, peak ground acceleration (g), Modified Mercalli Intensity, liquefaction risk, aftershock/tsunami flags, fault name
- Flood: flood type (riverine/coastal/flash/pluvial/dam failure), FEMA flood zone (A/AE/V/VE/X), inundation depth (ft), inundation area, duration, peak discharge (cfs), floodway breach
- Wildfire: acres burned, structures affected, fire severity
- Tornado: EF-scale category, path width, path length
- Winter storm: snowfall, ice accumulation, wind chill
6 geographic regions with peril affinity:
- US-Gulf, US-Atlantic, US-Pacific, Caribbean, Europe, Asia-Pacific
- Region-peril affinity matrices reflect real-world geographic risk (e.g. US-Pacific is 40% earthquake, Caribbean is 60% hurricane)
Full actuarial reinsurance pipeline:
- Loss decomposition: insured loss, economic loss, residential, commercial, industrial, auto, marine cargo, business interruption
- EP curve metrics: OEP percentile, AEP percentile, PML%, TVaR
- Reinsurance recoveries and net retained loss (cedant accounting)
- Cat bond trigger flag (OEP > 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
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
@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+)