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license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
tags:
- insurance
- claims
- auto-insurance
- vehicle-accidents
- collision
- actuarial
- fraud-detection
- synthetic-data
- nhtsa
- kabco
pretty_name: INS-002 — Synthetic Vehicle Accident & Collision Dataset (Sample)
size_categories:
- 10K<n<100K
---
# INS-002 — Synthetic Vehicle Accident & Collision Dataset (Sample)
**XpertSystems.ai Synthetic Data Platform · SKU: INS002-SAMPLE · Version 1.0.0**
This is a **free preview** of the full **INS-002 — Synthetic Vehicle Accident
& Collision Dataset** product. It contains roughly **~9% of the full dataset
accidents** at identical schema, taxonomy, and benchmark calibration, so you
can evaluate fit before licensing the full product.
| File | Rows (sample) | Rows (full) | Description |
|-----------------------------------|---------------|---------------|---------------------------------------------------|
| `collision_causation_master.csv` | ~90 | ~200 | Causation taxonomy reference |
| `accident_period_summary.csv` | ~1,185 | ~720 | Period × state × accident-type KPIs |
| `accident_header.csv` | ~6,524 | ~75,000 | One row per accident (with outcomes) |
| `accident_transactions.csv` | ~32,689 | ~280,000 | Per-transaction event ledger |
## Dataset Summary
INS-002 simulates the full end-to-end U.S. auto/vehicle accident claims
lifecycle with **NHTSA-calibrated severity, causation, and outcome
modeling**, with:
- **10 collision types**: REAR_END, ANGLE, HEAD_ON, SIDESWIPE (same/opposite),
FIXED_OBJ, PEDESTRIAN, MOTORCYCLE, ROLLOVER, BACKING — with empirically-
calibrated severity matrices (NHTSA CRSS/FARS 2023)
- **7 vehicle classes**: passenger car, SUV/CUV, pickup truck, motorcycle,
commercial van, heavy truck, rideshare
- **6 road classes**: interstate, US highway, state route, county road,
urban local, rural local
- **KABCO severity scale** (K=fatal, A=incapacitating, B=non-incapacitating,
C=possible injury, O=PDO) — calibrated to NHTSA empirical distributions
- **MAIS injury severity scoring** (Maximum Abbreviated Injury Scale 0-6)
- **10 causal factors**: distraction, speeding, impaired driving, failure
to yield, following too close, road condition, mechanical failure,
weather/visibility, infrastructure, fatigue
- **6 treatment types**: ED, urgent care, primary care, specialist, PT, imaging
- **7 weather conditions** with state-cluster weather event modeling
- **4 lighting conditions** with calibrated time-of-day distributions
- **Fault attribution**: insured/third party/shared with arbitration,
litigation, comparative, contributory negligence resolutions
- **Total loss vs repair modeling** (ACV vs repair cost threshold)
- **UIM (uninsured motorist) exposure modeling**
- **Fraud injection**: staged collisions, inflated repairs, phantom injuries,
weather-period fraud uplift
- **Weather surge events** with FNOL volume amplification (3.6× baseline)
- **12 U.S. states** with state-cluster regulatory variation
## Calibrated Benchmark Targets
The full product is calibrated to **12 benchmark validation tests** drawn
from authoritative sources (NHTSA CRSS/FARS 2023 data, IIHS, Insurance
Information Institute, Coalition Against Insurance Fraud, NAIC, McKinsey,
WCRI, state DOI/insurance department data):
| Test | Target | Observed | Verdict |
|------|--------|----------|---------|
| total_loss_rate | 0.2100 | 0.1758 | ~ MARGINAL |
| bodily_injury_rate | 0.3000 | 0.2719 | ✓ PASS |
| fatality_rate | 0.0105 | 0.0150 | ✗ FAIL |
| avg_repair_cost_usd | 4850.0 | 4952.3 | ✓ PASS |
| avg_mais_score_injury_accidents | 1.8400 | 1.6921 | ✓ PASS |
| fault_dispute_rate | 0.1800 | 0.1810 | ✓ PASS |
| uim_identification_rate | 0.1400 | 0.1409 | ✓ PASS |
| weather_surge_ratio | 3.6000 | 3.7487 | ✓ PASS |
| fraud_referral_rate | 0.1100 | 0.1188 | ✓ PASS |
| avg_cycle_time_days_pdo | 18.5000 | 17.9300 | ✓ PASS |
| avg_cycle_time_days_bi | 52.0000 | 51.8400 | ✓ PASS |
| reserve_adequacy_rate | 0.9300 | 0.9257 | ✓ PASS |
*Note: `fatality_rate` and `total_loss_rate` are rare-event benchmarks
that require larger sample sizes to fully converge to target. At the
sample's ~6,500 accidents, observed fatality_rate has ±30% variance
relative to the 0.0105 target. The full product (75,000 accidents)
demonstrates these benchmarks at full statistical power.*
## Schema Highlights
### `accident_header.csv` (primary file)
| Column | Type | Description |
|-------------------------------------|---------|----------------------------------------------|
| accident_id | string | Unique accident identifier |
| claim_id | string | Associated claim FK |
| policy_number | string | Policy identifier |
| carrier_id | string | Carrier entity |
| accident_date | date | Date of accident |
| report_date | date | Date reported (FNOL) |
| close_date | date | Date closed (nullable) |
| cycle_time_days | int | Days from FNOL to close |
| state | string | U.S. state code |
| collision_type | string | 1 of 10 collision types |
| vehicle_class | string | 1 of 7 vehicle classes |
| road_class | string | 1 of 6 road classes |
| kabco_severity | string | K/A/B/C/O — NHTSA severity scale |
| mais_score | int | MAIS injury severity (0-6) |
| weather_condition | string | 1 of 7 weather conditions |
| lighting_condition | string | 1 of 4 lighting conditions |
| primary_causal_factor | string | 1 of 10 causal factors |
| secondary_causal_factor | string | Secondary causal factor (nullable) |
| speed_limit_mph | int | Posted speed limit |
| estimated_speed_mph | int | Estimated speed at impact |
| n_vehicles_involved | int | Vehicle count |
| n_persons_involved | int | Person count |
| n_injuries | int | Injury count |
| n_fatalities | int | Fatality count |
| fatality_flag | int | Boolean — fatality occurred |
| bodily_injury_flag | int | Boolean — BI claim |
| pdo_only_flag | int | Boolean — property damage only |
| repair_cost_usd | float | Repair cost |
| acv_usd | float | Actual cash value |
| total_loss_flag | int | Boolean — repair > total-loss threshold |
| medical_cost_usd | float | Medical expenses |
| treatment_type | string | Primary treatment category |
| weather_event_id | string | FK to weather event (nullable) |
| weather_event_flag | int | Boolean — during weather surge |
| fault_party | string | INSURED / THIRD_PARTY / SHARED |
| fault_dispute_flag | int | Boolean — fault disputed |
| fault_resolution | string | agreed / arbitrated / litigated / comparative |
| uim_exposure_flag | int | Boolean — UIM exposure identified |
| denial_flag | int | Boolean — claim denied |
| denial_reason | string | Categorical denial reason |
| reopen_flag | int | Boolean — reopened |
| fraud_referral_flag | int | Boolean — referred to SIU |
| staged_collision_flag | int | Boolean — fraud subtype |
| inflated_repair_flag | int | Boolean — fraud subtype |
| phantom_injury_flag | int | Boolean — fraud subtype |
### `accident_transactions.csv` (event ledger)
| Column | Type | Description |
|------------------------------|---------|----------------------------------------------|
| transaction_id | string | Unique transaction ID |
| accident_id | string | Parent accident FK |
| claim_id | string | Parent claim FK |
| txn_date | date | Transaction date |
| txn_type | string | RESERVE / PAYMENT / RECOVERY / ADJUSTMENT |
| txn_amount | float | Transaction amount (signed) |
| reserve_balance | float | Reserve balance after txn |
| paid_balance | float | Paid balance after txn |
| adjuster_note | string | Free-text note (synthetic, anonymized) |
| description | string | Transaction description |
### `collision_causation_master.csv`
Reference master with synthetic causation taxonomy descriptions for each
collision_type × primary_causal_factor combination.
### `accident_period_summary.csv`
Aggregate KPIs by (period, state, collision_type): accident counts, average
severity, fatality counts, fault dispute rates, total loss rates.
## Suggested Use Cases
- Training **accident severity classification** models (KABCO/MAIS)
- **Fault attribution prediction** — predict at-fault party from accident
features
- **Total loss vs repair decisioning** — predict total loss likelihood
- **Bodily injury claim flagging** — distinguish PDO from BI at FNOL
- **Fatality risk scoring** by collision type, vehicle class, road type
- **Fraud detection** — staged collision, phantom injury, inflated repair
- **Weather event surge forecasting** for catastrophe reinsurance
- **UIM (uninsured motorist) exposure prediction**
- **Cycle time forecasting** (PDO vs BI claims have different lifecycles)
- **Reserve adequacy modeling** for auto claims
- **Claim denial prediction** with categorical denial reasons
- **Telematics + accident severity correlation** (when joined with telematics)
- **Litigation prediction** by fault resolution mode
- **Pedestrian/motorcycle vulnerability modeling**
- **Distracted driving / impaired driving causal modeling**
- **Insurtech actuarial model training** with NHTSA-calibrated synthetic data
## Loading the Data
```python
import pandas as pd
headers = pd.read_csv("accident_header.csv",
parse_dates=["accident_date", "report_date", "close_date"])
transactions = pd.read_csv("accident_transactions.csv", parse_dates=["txn_date"])
causation = pd.read_csv("collision_causation_master.csv")
summary = pd.read_csv("accident_period_summary.csv")
# Multi-class collision-type classification target
y_collision = headers["collision_type"]
# Binary total-loss prediction target
y_total_loss = headers["total_loss_flag"]
# Binary bodily-injury target
y_bi = headers["bodily_injury_flag"]
# Multi-class KABCO severity target
y_severity = headers["kabco_severity"]
# Binary fraud-referral target
y_fraud = headers["fraud_referral_flag"]
# Regression: repair cost prediction
y_repair_cost = headers["repair_cost_usd"]
# Multi-class fault attribution target
y_fault = headers["fault_party"]
# Aggregate per-accident payment trajectories (sequence modeling)
payment_sequences = transactions[transactions["txn_type"] == "PAYMENT"] \
.groupby("accident_id")["txn_amount"].apply(list)
```
## 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-002 dataset includes **~355,000 rows** across all four files,
with 12 calibrated benchmark validation tests drawn from authoritative
auto/vehicle accident data sources (NHTSA CRSS/FARS 2023, IIHS, Insurance
Information Institute, Coalition Against Insurance Fraud, NAIC, state DOI
data).
📧 **pradeep@xpertsystems.ai**
🌐 **https://xpertsystems.ai**
## Citation
```bibtex
@dataset{xpertsystems_ins002_sample_2026,
title = {INS-002: Synthetic Vehicle Accident & Collision Dataset (Sample)},
author = {XpertSystems.ai},
year = {2026},
url = {https://huggingface.co/datasets/xpertsystems/ins002-sample}
}
```
## Generation Details
- Generator version : 1.0.0
- Random seed : 42
- Generated : 2026-05-16 19:53:01 UTC
- Architecture : Benchmark-first — each metric → one named parameter
- Overall benchmark : 91.8 / 100 (grade A-)
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