---
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+)