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license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
tags:
- insurance
- fraud-detection
- claims
- siu
- anomaly-detection
- graph-ml
- social-network-analysis
- synthetic-data
- p-and-c
- red-flag-scoring
pretty_name: INS-007 — Synthetic Insurance Fraud Claims Dataset (Sample)
size_categories:
- 1K<n<10K
---
# INS-007 — Synthetic Insurance Fraud Claims Dataset (Sample)
**XpertSystems.ai Synthetic Data Platform · SKU: INS007-SAMPLE · Version 1.0.0**
This is a **free preview** of the full **INS-007 — Synthetic Insurance Fraud
Claims Dataset** product. It contains roughly **~10% of the full dataset**
at identical schema, fraud taxonomy, and SIU investigation workflow, so
you can evaluate fit before licensing the full product.
| File | Rows (sample) | Rows (full) | Description |
|----------------------------|---------------|---------------|----------------------------------------------|
| `fraud_claims.csv` | ~5,000 | ~50,000 | Per-claim records with fraud labels (99 cols)|
| `fraud_ring_edges.csv` | ~435 | ~5,000 | Network edge list for graph-based fraud ML |
## Dataset Summary
INS-007 simulates the **full fraud claims investigation lifecycle** — from
claim intake through SIU referral, surveillance, forensic investigation,
and adjudication — with realistic ring-based fraud structures, staged
accident typologies, and ISO ClaimSearch-calibrated red flag scoring.
**Calibration sources**:
- **Coalition Against Insurance Fraud** — confirmed fraud rates, savings
- **FBI Insurance Fraud Annual Report** — organized ring fraud share
- **NAIC AS-101** — Anti-Fraud Plan regulations and SIU referral rates
- **Insurance Research Council (IRC)** — attorney representation rates
- **ISO ClaimSearch** — red flag indicator catalog and scoring
- **NICB ForeWARN** — staged accident pattern typologies
**9 lines of business** with empirically-anchored fraud rates:
- Auto bodily injury (28%) — highest fraud rate LOB
- Auto property damage (18%)
- Auto PIP / no-fault (14%) — high fraud rate in no-fault states
- Workers compensation (12%)
- Homeowners (10%)
- Commercial property (6%)
- Health (5%)
- General liability (4%)
- Disability (3%)
**8 staged accident pattern typologies** (NICB-calibrated):
- Swoop-and-squat
- Drive-down
- Side-swipe
- Rear-end setup
- Paper accident (no actual collision)
- Owner give-up (insurance dump)
- Medical mill only (no accident at all)
**12 fraud type confirmations** (post-investigation):
- Staged accident
- Soft fraud / exaggeration
- Medical billing fraud
- Provider fraud (corrupt medical / repair providers)
- Agent fraud (corrupt agent / broker)
- Owner give-up
- Workers comp fraud
- Disability fraud
- Slip and fall fraud
- Fire loss fraud
- Water damage fraud
- Ring-organized fraud (multi-party coordinated)
**Fraud ring social network**:
- 60 fraud rings in sample (full product: ~150 rings)
- Average ring size 5-15 members
- Shared attributes: addresses, phone numbers, attorneys, body shops,
medical providers, witnesses
- 435 network edges in sample (full product: ~5,000)
- Edge types: same_ring, shared_address, shared_phone, shared_attorney,
shared_body_shop, shared_medical_provider, shared_witness
**SIU investigation workflow**:
- SIU referral with reason taxonomy
- Surveillance conducted flag
- Recorded statement taken
- Statement inconsistency count
- Investigation duration
- Outcome (paid_no_siu, paid_post_siu_legitimate, settled_with_reduction,
denied_unsubstantiated, denied_fraud_confirmed, withdrawn_by_claimant,
referred_to_da, criminal_prosecution, civil_recovery)
- Savings achieved (avoided loss)
- Recovery amount (post-payment clawback)
**Red flag scoring** (40+ binary indicators with prefix `rf_`):
- Claimant red flags: prior_claims_history, gaps_in_treatment, no_witnesses
- Provider red flags: medical_mill_pattern, billing_anomalies,
shared_attorney_concentration
- Vehicle red flags: salvage_history, pre_existing_damage, phantom_vehicle
- Network red flags: shared_address, shared_phone, shared_body_shop
- Statement red flags: inconsistent_statements, recorded_changes
- Behavioral red flags: late_reporting, policy_lapse_pre_loss,
coverage_recently_increased
- ISO ClaimSearch-calibrated weights
**Red flag composite score** (0-100):
- Computed weighted score across all triggered red flags
- Cleanly separates fraud (mean 50.1) from legitimate (mean 7.2)
- Direct feature for ML model training
**Claim-level features**:
- Claim ID with structured format (CLM-YEAR-LOB-STATE-SEQ)
- Loss date, claim filing date, age of claim
- Claim amount demanded, paid, savings, recovery
- Medical billing amount
- Estimated fraud savings (counterfactual avoided loss)
## Calibrated Validation Results
Sample validation results across 10 fraud-detection KPIs:
| Metric | Observed | Target | Source | Verdict |
|--------|----------|--------|--------|---------|
| n_states_represented | 50 | 12 | Min state coverage (national mix) | ✓ PASS |
| n_lob_represented | 9 | 9 | 9 lines of business | ✓ PASS |
| confirmed_fraud_rate_pct | 10.14 | 10.00 | Coalition Against Ins Fraud | ✓ PASS |
| siu_referral_rate_pct | 16.12 | 16.00 | NAIC AS-101 SIU referral rate | ✓ PASS |
| ring_based_fraud_share_pct | 9.90 | 10.00 | FBI organized fraud share | ✓ PASS |
| attorney_representation_pct | 30.10 | 30.00 | IRC attorney rep (high-suspicion) | ✓ PASS |
| surveillance_conducted_pct | 10.70 | 10.00 | SIU surveillance industry rate | ✓ PASS |
| police_report_filed_pct | 71.84 | 70.00 | Auto-heavy LOB police report rate | ✓ PASS |
| red_flag_separation_ratio | 6.938 | 6.000 | RF score: confirmed/legit ratio | ✓ PASS |
| n_fraud_ring_edges | 435 | 200 | Network edge density (floor) | ✓ PASS |
*Note: Confirmed fraud rate (~10%) is below the configured `--fraud-rate
0.18` parameter because only ~56% of fraud-suspect claims that get SIU
referral end in `fraud_confirmed=True` after investigation. The other ~44%
of suspected fraud is resolved as paid_post_siu_legitimate,
settled_with_reduction, denied_unsubstantiated, or withdrawn — realistic
SIU workflow outcomes. This makes INS-007 useful for training models that
distinguish suspected from confirmed fraud, not just legitimate from
suspicious.*
## Schema Highlights
### `fraud_claims.csv` (primary file, 99 columns)
**Claim identification**:
| Column | Type | Description |
|------------------------------|---------|----------------------------------------------|
| claim_id | string | Structured ID: CLM-YEAR-LOB-STATE-SEQ |
| policy_id, claimant_id | string | Foreign keys |
| incident_state | string | U.S. state code |
| loss_date, claim_filed_date | date | Timeline |
| lob | string | 1 of 9 LOBs |
| incident_type | string | 10 incident types |
| staged_accident_pattern | string | 8 NICB typologies (or 'none') |
**Claim financials**:
| Column | Type | Description |
|------------------------------|---------|----------------------------------------------|
| claim_amount_demanded_usd | float | Initial demand |
| claim_amount_paid_usd | float | Amount paid (post-investigation) |
| medical_billing_usd | float | Medical billing component |
| savings_achieved_usd | float | Avoided loss from SIU intervention |
| recovery_amount_usd | float | Post-payment recovery |
| estimated_fraud_savings_usd | float | Counterfactual full-fraud cost |
**Investigation workflow**:
| Column | Type | Description |
|---------------------------------|---------|----------------------------------------------|
| siu_referral_flag | bool | Referred to SIU |
| siu_referral_reason | string | Categorical reason |
| surveillance_conducted | bool | SIU conducted surveillance |
| recorded_statement_taken | bool | Recorded statement taken |
| statement_inconsistency_count | int | Inconsistencies detected |
| investigation_duration_days | int | Days SIU investigated |
| police_report_filed | bool | Police report on file |
| attorney_represented | bool | Claimant attorney representation |
| attorney_siu_flag | bool | Attorney on SIU watchlist |
| claim_outcome | string | 9-class outcome |
**Fraud labels** (training targets):
| Column | Type | Description |
|------------------------------|---------|----------------------------------------------|
| fraud_confirmed | bool | Binary fraud label (post-investigation) |
| fraud_type_confirmed | string | 12-class fraud type (or 'none') |
| fraud_ring_id | string | Ring ID (nullable) |
| referral_to_law_enforcement | bool | Referred to law enforcement |
| criminal_charges_filed | bool | Charges filed |
| civil_recovery_initiated | bool | Civil recovery action |
**Red flag indicators** (40+ binary columns, prefix `rf_`):
Examples: rf_prior_claims_history, rf_late_reporting, rf_inconsistent_statements,
rf_medical_mill_pattern, rf_no_witnesses, rf_policy_lapse_pre_loss,
rf_coverage_recently_increased_flag, rf_phantom_vehicle, rf_shared_attorney,
rf_shared_body_shop, rf_treatment_gap, rf_billing_anomalies, rf_shared_address,
rf_shared_phone_number, rf_injury_vs_damage_paradox.
**Composite scoring**:
| Column | Type | Description |
|-------------------------|---------|----------------------------------------------|
| red_flag_score | float | Composite 0-100 (ISO-calibrated weights) |
| red_flag_count | int | Number of triggered red flags |
| fraud_probability_score | float | Internal Bayesian posterior (training tgt) |
### `fraud_ring_edges.csv` (network graph)
| Column | Type | Description |
|-------------------|---------|----------------------------------------------|
| ring_id | string | Fraud ring identifier (RING-001 to RING-060) |
| claim_id_a | string | Edge source claim |
| claim_id_b | string | Edge target claim |
| edge_type | string | same_ring / shared_address / shared_phone / ...|
## Suggested Use Cases
- **Binary fraud classification** — predict `fraud_confirmed` from claim features
- **Multi-class fraud typing** — predict `fraud_type_confirmed` (12 classes)
- **SIU referral prediction** — predict which claims need investigation
- **Red flag score modeling** — train regression on `red_flag_score`
- **Staged accident pattern detection** — multi-class typology classification
- **Fraud ring detection** — graph-based community detection
- **Network anomaly detection** — find suspicious link patterns
- **Provider fraud detection** — medical mills, body shops, attorneys
- **Soft fraud vs hard fraud** classification
- **Attorney representation prediction** for high-suspicion claims
- **Claim adjudication automation** — predict 9-class `claim_outcome`
- **Savings optimization** — maximize avoided loss per SIU investigation hour
- **Fraud ring growth modeling** — track ring expansion over time
- **Surveillance prioritization** — predict which claims warrant surveillance
- **Statement inconsistency detection** — NLP on recorded statements
- **Insurtech SIU model training** without proprietary ISO ClaimSearch data
- **Graph neural network (GNN) training** with realistic fraud ring topology
## Loading the Data
```python
import pandas as pd
claims = pd.read_csv("fraud_claims.csv")
edges = pd.read_csv("fraud_ring_edges.csv")
# Binary fraud classification target
y_fraud = claims["fraud_confirmed"].astype(int)
# Multi-class fraud type (12 classes; filter to fraud only)
fraud_only = claims[claims["fraud_confirmed"] == True]
y_fraud_type = fraud_only["fraud_type_confirmed"]
# Binary SIU referral prediction
y_siu = claims["siu_referral_flag"]
# Multi-class staged accident pattern (8 classes)
y_staged = claims["staged_accident_pattern"]
# Regression: red flag score
y_rf_score = claims["red_flag_score"]
# 9-class claim outcome
y_outcome = claims["claim_outcome"]
# Build fraud ring graph for GNN training
import networkx as nx
G = nx.Graph()
for _, row in edges.iterrows():
G.add_edge(row["claim_id_a"], row["claim_id_b"],
edge_type=row["edge_type"], ring_id=row["ring_id"])
print(f"Ring graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges")
# Compute community detection features
import community as community_louvain # pip install python-louvain
partition = community_louvain.best_partition(G)
```
## 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-007 dataset includes **~50,000 fraud claims records** across
99 columns, with ~5,000 network edges spanning ~150 fraud rings. Calibrated
to Coalition Against Insurance Fraud, FBI fraud reports, NAIC AS-101, IRC,
and ISO ClaimSearch.
📧 **pradeep@xpertsystems.ai**
🌐 **https://xpertsystems.ai**
## Citation
```bibtex
@dataset{xpertsystems_ins007_sample_2026,
title = {INS-007: Synthetic Insurance Fraud Claims Dataset (Sample)},
author = {XpertSystems.ai},
year = {2026},
url = {https://huggingface.co/datasets/xpertsystems/ins007-sample}
}
```
## Generation Details
- Generator version : 1.0.0
- Random seed : 42
- Generated : 2026-05-16 20:39:10 UTC
- Records : 5,000 (~10.1% confirmed fraud)
- Rings : 60 fraud ring structures
- Calibration basis : Coalition Against Ins Fraud + FBI + NAIC AS-101 + IRC + ISO ClaimSearch
- Overall validation: 100.0 / 100 (grade A+)
|