README.md

---
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+)