xpertsystems/ins002-sample

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

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

@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-)