xpertsystems/ins003-sample

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+)