| --- |
| 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-) |
| |
