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README.md

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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+)

ep_curve_summary.csv

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+peril_type,return_period_years,oep_loss_usd,exceedance_probability
+earthquake,10,86973339.0,0.1
+earthquake,25,219968889.0,0.04
+earthquake,50,401149546.0,0.02
+earthquake,100,540848528.0,0.01
+earthquake,200,890778281.0,0.005
+earthquake,250,1118903122.0,0.004
+earthquake,500,1439455869.0,0.002
+earthquake,1000,5863961233.0,0.001
+tornado,10,27373438.0,0.1
+tornado,25,77939507.0,0.04
+tornado,50,107944992.0,0.02
+tornado,100,288851176.0,0.01
+tornado,200,579048693.0,0.005
+tornado,250,579048693.0,0.004
+tornado,500,648912053.0,0.002
+tornado,1000,648912053.0,0.001
+hurricane,10,136337929.0,0.1
+hurricane,25,360421440.0,0.04
+hurricane,50,618856897.0,0.02
+hurricane,100,1136617273.0,0.01
+hurricane,200,1758304457.0,0.005
+hurricane,250,2496260701.0,0.004
+hurricane,500,9082812259.0,0.002
+hurricane,1000,14761633935.0,0.001
+wildfire,10,137247369.0,0.1
+wildfire,25,405196386.0,0.04
+wildfire,50,779327734.0,0.02
+wildfire,100,1491442390.0,0.01
+wildfire,200,1773308090.0,0.005
+wildfire,250,1842490541.0,0.004
+wildfire,500,2172417964.0,0.002
+wildfire,1000,2172417964.0,0.001
+flood,10,152260339.0,0.1
+flood,25,379548950.0,0.04
+flood,50,808671570.0,0.02
+flood,100,1454298511.0,0.01
+flood,200,2600398108.0,0.005
+flood,250,3331081316.0,0.004
+flood,500,5803108879.0,0.002
+flood,1000,6818795093.0,0.001
+winter_storm,10,11819689.0,0.1
+winter_storm,25,40169200.0,0.04
+winter_storm,50,106604855.0,0.02
+winter_storm,100,129894414.0,0.01
+winter_storm,200,245359261.0,0.005
+winter_storm,250,245359261.0,0.004
+winter_storm,500,308017222.0,0.002
+winter_storm,1000,365308751.0,0.001