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README.md

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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+)

fraud_ring_edges.csv

@@ -0,0 +1,436 @@
+ring_id,claim_id_a,claim_id_b,edge_type
+RING-017,CLM-2021-AUTO-AL-000005,CLM-2017-AUTO-MI-000008,same_ring
+RING-028,CLM-2017-AUTO-TN-000012,CLM-2019-AUTO-CO-000017,same_ring
+RING-039,CLM-2024-AUTO-LA-000011,CLM-2024-AUTO-NY-000018,same_ring
+RING-008,CLM-2020-AUTO-TX-000003,CLM-2015-HOMEOWNERS-KY-000020,same_ring
+RING-017,CLM-2017-AUTO-MI-000008,CLM-2021-AUTO-OH-000025,same_ring
+RING-003,CLM-2015-AUTO-IA-000009,CLM-2017-AUTO-CO-000026,same_ring
+RING-028,CLM-2019-AUTO-CO-000017,CLM-2020-WORKERS-MO-000028,same_ring
+RING-047,CLM-2020-AUTO-TX-000014,CLM-2022-AUTO-TN-000035,same_ring
+RING-048,CLM-2023-AUTO-MD-000036,CLM-2022-AUTO-NJ-000037,same_ring
+RING-013,CLM-2016-HOMEOWNERS-AL-000024,CLM-2018-COMMERCIAL-OR-000039,same_ring
+RING-028,CLM-2020-WORKERS-MO-000028,CLM-2016-AUTO-CO-000041,same_ring
+RING-043,CLM-2022-DISABILITY-CA-000038,CLM-2019-WORKERS-NY-000043,same_ring
+RING-022,CLM-2023-AUTO-CO-000001,CLM-2024-HEALTH-WV-000045,same_ring
+RING-021,CLM-2021-AUTO-WV-000016,CLM-2021-AUTO-CA-000047,same_ring
+RING-043,CLM-2019-WORKERS-NY-000043,CLM-2017-AUTO-KY-000048,same_ring
+RING-008,CLM-2015-HOMEOWNERS-KY-000020,CLM-2021-GENERAL-WA-000050,same_ring
+RING-047,CLM-2022-AUTO-TN-000035,CLM-2016-AUTO-NY-000051,same_ring
+RING-058,CLM-2019-WORKERS-PA-000022,CLM-2021-AUTO-NJ-000052,same_ring
+RING-033,CLM-2022-AUTO-PA-000019,CLM-2017-AUTO-WA-000054,same_ring
+RING-020,CLM-2024-WORKERS-ID-000053,CLM-2024-AUTO-LA-000055,same_ring
+RING-043,CLM-2017-AUTO-KY-000048,CLM-2024-GENERAL-UT-000056,same_ring
+RING-021,CLM-2021-AUTO-CA-000047,CLM-2024-AUTO-MS-000058,same_ring
+RING-013,CLM-2018-COMMERCIAL-OR-000039,CLM-2016-DISABILITY-FL-000064,same_ring
+RING-027,CLM-2016-AUTO-AL-000057,CLM-2023-WORKERS-TX-000065,same_ring
+RING-043,CLM-2024-GENERAL-UT-000056,CLM-2016-WORKERS-MN-000066,same_ring
+RING-025,CLM-2019-AUTO-TX-000021,CLM-2018-DISABILITY-FL-000067,same_ring
+RING-044,CLM-2017-COMMERCIAL-CA-000034,CLM-2016-AUTO-TX-000068,same_ring
+RING-048,CLM-2022-AUTO-NJ-000037,CLM-2015-AUTO-UT-000069,same_ring
+RING-058,CLM-2021-AUTO-NJ-000052,CLM-2020-HOMEOWNERS-FL-000070,same_ring
+RING-012,CLM-2023-COMMERCIAL-IN-000006,CLM-2021-AUTO-AZ-000071,same_ring
+RING-040,CLM-2019-AUTO-WI-000046,CLM-2022-WORKERS-PA-000072,same_ring
+RING-037,CLM-2016-AUTO-ID-000023,CLM-2024-AUTO-PA-000073,same_ring
+RING-020,CLM-2024-AUTO-LA-000055,CLM-2025-WORKERS-PA-000074,same_ring
+RING-026,CLM-2024-AUTO-CT-000059,CLM-2019-AUTO-MO-000075,same_ring
+RING-036,CLM-2021-WORKERS-LA-000062,CLM-2021-WORKERS-CA-000076,same_ring
+RING-001,CLM-2020-AUTO-NJ-000029,CLM-2024-AUTO-ND-000078,same_ring
+RING-020,CLM-2025-WORKERS-PA-000074,CLM-2020-HEALTH-MO-000079,same_ring
+RING-019,CLM-2018-AUTO-IL-000061,CLM-2024-AUTO-VT-000080,same_ring
+RING-028,CLM-2016-AUTO-CO-000041,CLM-2023-AUTO-MS-000082,same_ring
+RING-051,CLM-2023-WORKERS-DE-000015,CLM-2025-HOMEOWNERS-SC-000083,same_ring
+RING-038,CLM-2017-HOMEOWNERS-AL-000007,CLM-2016-WORKERS-CO-000084,same_ring
+RING-039,CLM-2024-AUTO-NY-000018,CLM-2020-AUTO-CA-000085,same_ring
+RING-021,CLM-2024-AUTO-MS-000058,CLM-2024-WORKERS-NE-000087,same_ring
+RING-029,CLM-2016-AUTO-NH-000089,CLM-2021-AUTO-SD-000090,same_ring
+RING-044,CLM-2016-AUTO-TX-000068,CLM-2020-AUTO-IN-000091,same_ring
+RING-014,CLM-2023-AUTO-NC-000081,CLM-2015-AUTO-NY-000092,same_ring
+RING-033,CLM-2017-AUTO-WA-000054,CLM-2016-AUTO-AR-000093,same_ring
+RING-051,CLM-2025-HOMEOWNERS-SC-000083,CLM-2020-HOMEOWNERS-TX-000095,same_ring
+RING-058,CLM-2020-HOMEOWNERS-FL-000070,CLM-2019-HOMEOWNERS-MN-000096,same_ring
+RING-051,CLM-2020-HOMEOWNERS-TX-000095,CLM-2023-HOMEOWNERS-NY-000097,same_ring
+RING-048,CLM-2015-AUTO-UT-000069,CLM-2025-AUTO-NE-000098,same_ring
+RING-043,CLM-2016-WORKERS-MN-000066,CLM-2015-AUTO-IA-000100,same_ring
+RING-019,CLM-2024-AUTO-VT-000080,CLM-2020-AUTO-WA-000102,same_ring
+RING-033,CLM-2016-AUTO-AR-000093,CLM-2020-AUTO-NJ-000103,same_ring
+RING-043,CLM-2015-AUTO-IA-000100,CLM-2023-AUTO-SC-000104,same_ring
+RING-040,CLM-2022-WORKERS-PA-000072,CLM-2022-AUTO-PA-000106,same_ring
+RING-022,CLM-2024-HEALTH-WV-000045,CLM-2024-AUTO-MN-000107,same_ring
+RING-006,CLM-2024-AUTO-IA-000101,CLM-2023-AUTO-IA-000108,same_ring
+RING-044,CLM-2020-AUTO-IN-000091,CLM-2022-AUTO-OH-000112,same_ring
+RING-020,CLM-2020-HEALTH-MO-000079,CLM-2024-AUTO-OH-000113,same_ring
+RING-052,CLM-2018-HOMEOWNERS-MO-000032,CLM-2025-HOMEOWNERS-TX-000115,same_ring
+RING-013,CLM-2016-DISABILITY-FL-000064,CLM-2023-HOMEOWNERS-AL-000116,same_ring
+RING-002,CLM-2022-AUTO-TX-000002,CLM-2018-AUTO-KY-000118,same_ring
+RING-003,CLM-2017-AUTO-CO-000026,CLM-2018-AUTO-VA-000119,same_ring
+RING-016,CLM-2023-AUTO-ME-000111,CLM-2015-AUTO-NY-000120,same_ring
+RING-031,CLM-2021-COMMERCIAL-MI-000094,CLM-2017-WORKERS-AR-000121,same_ring
+RING-040,CLM-2022-AUTO-PA-000106,CLM-2020-GENERAL-AZ-000122,same_ring
+RING-055,CLM-2015-AUTO-LA-000049,CLM-2021-GENERAL-GA-000123,same_ring
+RING-005,CLM-2021-HOMEOWNERS-TX-000042,CLM-2019-AUTO-NV-000125,same_ring
+RING-030,CLM-2023-COMMERCIAL-SC-000088,CLM-2016-WORKERS-MI-000126,same_ring
+RING-027,CLM-2023-WORKERS-TX-000065,CLM-2025-AUTO-NJ-000127,same_ring
+RING-057,CLM-2020-AUTO-PA-000004,CLM-2017-HOMEOWNERS-CT-000128,same_ring
+RING-041,CLM-2020-AUTO-SC-000105,CLM-2019-HOMEOWNERS-MA-000129,same_ring
+RING-037,CLM-2024-AUTO-PA-000073,CLM-2021-AUTO-NJ-000130,same_ring
+RING-013,CLM-2023-HOMEOWNERS-AL-000116,CLM-2018-WORKERS-AK-000131,same_ring
+RING-046,CLM-2023-COMMERCIAL-MS-000027,CLM-2021-WORKERS-DE-000132,same_ring
+RING-045,CLM-2021-AUTO-WA-000060,CLM-2024-WORKERS-FL-000133,same_ring
+RING-025,CLM-2018-DISABILITY-FL-000067,CLM-2017-AUTO-VT-000134,same_ring
+RING-026,CLM-2019-AUTO-MO-000075,CLM-2017-AUTO-MI-000136,same_ring
+RING-016,CLM-2015-AUTO-NY-000120,CLM-2020-COMMERCIAL-MI-000137,same_ring
+RING-010,CLM-2018-AUTO-SD-000117,CLM-2018-WORKERS-AZ-000138,same_ring
+RING-042,CLM-2018-AUTO-CA-000086,CLM-2023-AUTO-IL-000139,same_ring
+RING-045,CLM-2024-WORKERS-FL-000133,CLM-2022-AUTO-MT-000140,same_ring
+RING-001,CLM-2024-AUTO-ND-000078,CLM-2021-AUTO-CA-000141,same_ring
+RING-058,CLM-2019-HOMEOWNERS-MN-000096,CLM-2019-AUTO-CO-000142,same_ring
+RING-049,CLM-2017-HOMEOWNERS-VT-000110,CLM-2021-HOMEOWNERS-OH-000143,same_ring
+RING-050,CLM-2017-AUTO-MT-000124,CLM-2019-AUTO-IA-000145,same_ring
+RING-057,CLM-2017-HOMEOWNERS-CT-000128,CLM-2023-AUTO-NH-000146,same_ring
+RING-007,CLM-2016-AUTO-PA-000010,CLM-2019-AUTO-KS-000147,same_ring
+RING-050,CLM-2019-AUTO-IA-000145,CLM-2021-AUTO-ME-000148,same_ring
+RING-028,CLM-2023-AUTO-MS-000082,CLM-2022-AUTO-WA-000149,same_ring
+RING-015,CLM-2024-WORKERS-OK-000114,CLM-2015-HOMEOWNERS-FL-000150,same_ring
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