--- 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 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-)