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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
```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+)
|