xpertsystems/hlt015-sample

HLT-015 — Synthetic Insurance Medical Claims Dataset — Payer Operations (Sample Preview)

A free, schema-identical preview of the full HLT-015 commercial product from XpertSystems.ai.

A fully synthetic enterprise-grade payer-side healthcare claims dataset combining member eligibility / enrollment, prior authorization workflows, end-to-end claim adjudication (Professional / Institutional / Pharmacy / Dental / Vision / Behavioral Health), fraud detection with 12 NHCAA typologies, value-based care contract attribution (5 HHS LAN APM categories), and HEDIS quality measure tracking — calibrated to AHIP / CMS / NHCAA / NCQA / HCUP / CAQH benchmarks.

⚠️ PRIVACY & SYNTHETIC NATURE Every record in this dataset is 100% synthetic. No real patient data, no PHI, no real provider NPIs, no real claim records. Population-level distributions match published AHIP / CMS / NHCAA / NCQA / HCUP / CAQH / FAIR Health references but the claims, members, and authorizations are computationally generated.

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How does this differ from HLT-008 Healthcare Claims?

Both products cover synthetic healthcare claims, but from different operational perspectives:

Aspect	HLT-008 (Provider/X12 Billing)	HLT-015 (Payer Operations)
Buyer persona	Provider revenue cycle, X12 EDI integrators	Payer ops, prior auth admin, fraud SIU
Schema focus	X12 837/835 EDI structure, HCC risk adjustment, CMS CCW chronic conditions	Claim adjudication workflow, prior auth, fraud typology, value-based care attribution
Member view	21 CCW chronic conditions, CMS HCC risk score	15 chronic conditions, RAF + expected annual cost
Fraud taxonomy	5 NHCAA patterns (Upcoding/Phantom/Unbundling/Duplicate/Identity_Theft)	12 NHCAA typologies (full taxonomy including DME, Kickback, Provider Impersonation, etc.)
Quality measures	HEDIS 5 measures (BCS, COL, CDC, AWV, Depression)	NCQA HEDIS extended (SPC, MPT, TRC, CDC, PCR)
Workflow coverage	Claim adjudication outcome	Full lifecycle: eligibility → PA → submission → adjudication → fraud SIU → VBC attribution
Adjudication engines	n/a	Tracked: which payer engine adjudicated
VBC attribution	n/a	5 contract types (FFS/Shared Savings/Capitation/Bundled/P4P)
Prior authorization	n/a	Separate workflow table with peer-to-peer + appeals
Schema width	54 cols medical claims	52 cols claims + 33 cols members + 16 cols prior auth

Use HLT-008 for X12 EDI pipeline development, provider revenue cycle, and HCC risk adjustment ML. Use HLT-015 for payer operations analytics, prior auth optimization, fraud SIU investigation, and value-based care contract performance modeling.

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What's in this sample

5,000 claims × 300 members × 12-month observation window linked by member_id.

File	Rows × Cols	Description
members.csv	300 × 33	Member master — demographics, coverage tier, deductible/OOP/copay, RAF risk score, 15 chronic conditions, expected annual cost
claims.csv	5,000 × 52	Full claim lifecycle — submission_channel, adjudication_engine, ICD-10 + CPT + DRG, financials, COB, denial categories, 12 fraud typologies, HEDIS, VBC
prior_auths.csv	~250 × 16	Prior authorization workflow — turnaround days, peer-to-peer, appeals, clinical criteria evaluation

Total: ~1.9 MB across 3 CSVs + scorecard JSON.

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Schema highlights

members.csv (33 columns)

Identity & demographics: member_id, date_of_birth, age, sex

Coverage: payer_type (Commercial / Medicare_Advantage / Medicaid_MCO / ACA_Marketplace), plan_type, coverage_tier, enrollment_start_date, enrollment_end_date

Cost-sharing: deductible_individual, deductible_family, oop_maximum_individual, copay_primary_care, copay_specialist, coinsurance_rate

Risk: risk_score_raf (CMS HCC normalized mean=1.0), expected_annual_cost

15 chronic conditions (binary flags): cc_diabetes, cc_hypertension, cc_hyperlipidemia, cc_ischemic_heart_disease, cc_heart_failure, cc_atrial_fibrillation, cc_copd, cc_asthma, cc_ckd, cc_depression, cc_anxiety, cc_obesity, cc_osteoporosis, cc_stroke, cc_cancer, plus chronic_condition_count

claims.csv (52 columns)

Identity & workflow: claim_id, member_id, claim_type (6 types: Medical_Professional, Medical_Institutional, Pharmacy, Dental, Vision, Behavioral_Health), claim_status (Paid/Denied/Pended/Adjusted), submission_channel (Electronic_EDI / Provider_Portal / Paper), submission_date, service_date_from, service_date_to, adjudication_date, adjudication_engine, adjudication_turnaround_days

Provider: rendering_provider_npi, billing_provider_npi, provider_taxonomy_code, provider_specialty, place_of_service_code, network_status (In_Network / Out_of_Network / Emergency)

Clinical coding: primary_diagnosis_code (ICD-10-CM), secondary_diagnosis_codes, procedure_code (CPT/HCPCS), drg_code, formulary_tier

Member context: payer_type, plan_type, member_age, member_sex, risk_score_raf

Denial: denial_code, denial_category (8 categories: Medical_Necessity / Timely_Filing / Duplicate / COB / Network / Eligibility / Authorization / Coding_Error)

Financials: billed_amount, allowed_amount, paid_amount, member_deductible_applied, member_copay_applied, member_coinsurance_applied, contractual_adjustment, cob_primary_paid, cob_secondary_paid

Fraud (NHCAA): fraud_label, fraud_typology (12 types), fraud_risk_score, anomaly_flags, siu_referral, provider_fraud_risk_tier

Quality & cost: high_cost_claimant_flag, readmission_30d_flag, preventable_admission_flag, mlr_contribution, leakage_flag, generic_substitution_flag, hedis_measure_triggered, value_based_contract_type (5 categories per HHS LAN)

prior_auths.csv (16 columns)

auth_id, member_id, auth_request_date, auth_decision_date, auth_turnaround_days, auth_procedure_category, auth_urgency, auth_decision, auth_denial_reason, auth_units_requested, auth_units_approved, peer_to_peer_requested, appeal_filed, appeal_outcome, clinical_criteria_met, payer_type

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Calibration source story

The full HLT-015 generator anchors all distributions to authoritative payer industry references:

• AHIP 2023 (America's Health Insurance Plans) — Claim denial rates, in-network shares
• CMS HCC v28 — Hierarchical Condition Categories risk adjustment
• NHCAA (National Health Care Anti-Fraud Association) — 12-typology fraud taxonomy
• X12 835 (HIPAA EDI) — CARC denial codes
• CMS HRRP (Hospital Readmissions Reduction Program) — 30-day readmission benchmarks
• CAQH CORE 2023 — EDI 837 adoption rates
• HHS LAN APM Framework — Value-based care contract categories
• NCQA HEDIS — Healthcare Effectiveness Data and Information Set quality measures
• CMS CCW — Chronic Conditions Warehouse
• FAIR Health — Independent medical procedure pricing reference

Sample-scale validation scorecard

Metric	Observed	Target	Status	Source
Fraud prevalence	3.32%	3% ± 1.5%	✅ PASS	NHCAA
Claim denial rate	17.4%	18.5% ± 4%	✅ PASS	AHIP 2023
RAF score mean	1.0000	1.0 ± 0.05	✅ PASS	CMS HCC v28
In-network share	87.3%	87% ± 8%	✅ PASS	AHIP
EDI submission share	85.0%	85% ± 8%	✅ PASS	CAQH CORE 2023
30-day readmission	14.9%	15% ± 5%	✅ PASS	CMS HRRP
Fraud typology count	12	12 (NHCAA)	✅ PASS	NHCAA taxonomy
Denial category count	8	8 (X12)	✅ PASS	X12 835 / CARC
VBC contract type count	5	5 (HHS LAN)	✅ PASS	HHS LAN APM
Chronic condition count	15	15 (CCW)	✅ PASS	CMS CCW

Grade: A+ (100/100) — verified across 6 random seeds (42, 7, 123, 2024, 99, 1).

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Loading examples

Pandas — explore claim mix

import pandas as pd

members = pd.read_csv("members.csv")
claims = pd.read_csv("claims.csv")
prior_auths = pd.read_csv("prior_auths.csv")

# Claim type & status breakdown
print(pd.crosstab(claims["claim_type"], claims["claim_status"], normalize="index").round(3))

# Payer-stratified denial rates
print(claims.groupby("payer_type").apply(
    lambda d: (d["claim_status"] == "Denied").mean()
).round(3))

# Fraud typology counts
print(claims.loc[claims["fraud_label"] == 1, "fraud_typology"].value_counts())

Fraud SIU referral targeting

import pandas as pd

claims = pd.read_csv("claims.csv")

# High-risk claims for SIU review
siu_candidates = claims[
    (claims["fraud_risk_score"] > 0.5) |
    (claims["siu_referral"] == 1) |
    (claims["provider_fraud_risk_tier"] == "High")
]
print(f"SIU candidates: {len(siu_candidates)}")
print(siu_candidates[["claim_id", "fraud_typology", "fraud_risk_score",
                     "billed_amount", "anomaly_flags"]].head(10))

Prior auth turnaround analysis

import pandas as pd

pa = pd.read_csv("prior_auths.csv")

# Turnaround by urgency
print(pa.groupby("auth_urgency")["auth_turnaround_days"].agg(["mean", "median", "max"]).round(2))

# Approval rates
print(pa["auth_decision"].value_counts(normalize=True).round(3))

# Peer-to-peer conversion (denial → P2P → final outcome)
denials = pa[pa["auth_decision"] == "Denied"]
print(f"\nDenials: {len(denials)}")
print(f"Peer-to-peer requested: {denials['peer_to_peer_requested'].sum()}")
print(f"Appeals filed: {denials['appeal_filed'].sum()}")
print(f"Appeal outcomes: {denials.loc[denials['appeal_filed'] == 1, 'appeal_outcome'].value_counts().to_dict()}")

Value-based care contract performance

import pandas as pd

claims = pd.read_csv("claims.csv")

# Claim outcomes by VBC contract type
print("Mean paid / billed by VBC type:")
print(claims.groupby("value_based_contract_type")[["billed_amount", "paid_amount"]].mean().round(2))

# MLR contribution by VBC type
print("\nMLR contribution sum by VBC type:")
print(claims.groupby("value_based_contract_type")["mlr_contribution"].sum().round(2))

Hugging Face Datasets

from datasets import load_dataset

ds = load_dataset("xpertsystems/hlt015-sample", data_files={
    "members":     "members.csv",
    "claims":      "claims.csv",
    "prior_auths": "prior_auths.csv",
})
print(ds)

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Suggested use cases

• Fraud SIU referral classifier — train on siu_referral × claim features; identify provider fraud risk tiers
• NHCAA fraud typology multi-class classification — predict fraud_typology (12 classes) from claim + provider features
• Claim denial prediction — predict claim_status (Paid/Denied/Pended/Adjusted) and denial_category from submission features
• Prior auth optimization — predict authorization decision from request features; reduce unnecessary P2P escalations
• Appeal outcome prediction — predict appeal success from denial features
• Value-based care attribution analytics — analyze claim economics by VBC contract type
• HEDIS quality gap identification — identify members not meeting hedis_measure_triggered
• 30-day readmission prediction — readmission_30d_flag ML from baseline claim features
• High-cost claimant identification — predict high_cost_claimant_flag from early-period features
• MLR (Medical Loss Ratio) forecasting — analyze mlr_contribution trajectories
• Out-of-network leakage analysis — leakage_flag patterns by member segment
• Generic substitution opportunity modeling — generic_substitution_flag rate improvement targeting
• EDI pipeline testing — schema-compliant synthetic data for X12 837/835 EDI integration
• Payer analytics platform development — claims warehouse, BI dashboards, BI reporting
• Healthcare AI pretraining — pretrain payer-side claim models before fine-tuning on real claims (Optum, Truven Marketscan, IBM Watson)
• Educational use — actuarial science, health insurance management, healthcare analytics coursework

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Sample vs. full product

Aspect	This sample	Full HLT-015 product
Claims	5,000	500,000+ (default) up to 50M
Members	300	50,000+ (default) up to 5M
Observation window	12 months	36+ months (multi-year configurable)
Schema	identical	identical
Calibration	identical	identical
License	CC-BY-NC-4.0	Commercial license

The full product unlocks:

• Up to 50M claims for production-grade payer ML training
• 5M+ member populations for representative cohort analytics
• Multi-year longitudinal windows for trend analysis and intervention impact studies
• Custom fraud prevalence injection — control class balance for SIU referral classifiers
• Multi-LOB (Line of Business) splits — separate Commercial / MA / Medicaid model training
• Commercial use rights

Contact us for the full product.

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Limitations & honest disclosures

• Sample is preview-only. 5K claims × 300 members × 12 months is enough to demonstrate schema and calibration, but is not statistically sufficient for production-grade fraud classifier training (only ~166 fraud-labeled claims at sample scale, across 12 typology classes = ~14 per class). Use the full product for serious work.
• Sample uses 12-month observation, not 36-month default. The full product's default 36-month window enables 30-day readmission tracking across full episodes-of-care and multi-year HCC risk adjustment trajectories.
• Fraud labels are statistically planted, not adjudicated. When the generator marks a claim fraud=1, it manipulates billing features (excessive amounts, unbundling patterns, etc.) to look fraud-like. Real fraud labels come from SIU investigation outcomes — use the synthetic labels for ML pipeline development, not pathophysiological inference.
• Provider NPIs are synthetic 10-digit strings, not real CMS NPPES numbers. Provider taxonomy codes are realistic placeholder strings.
• Member IDs are synthetic UUIDs, not real payer member ID formats.
• No real ICD-10/CPT/DRG-specific payment rates. paid_amount is calibrated to overall paid/allowed ratios, not specific Medicare IPPS/OPPS or commercial fee schedules.
• Fraud risk scores follow realistic distributions but are not derived from explainable ML. Use the field for downstream ML, not for fraud explainability research.
• No coordination of benefits (COB) cascade simulation. The cob_primary_paid and cob_secondary_paid fields are calibrated to realistic split ratios but do not simulate multi-payer claim handoff workflows.
• HEDIS measures are name-only references. The full HEDIS denominator / numerator / exclusion logic is not enforced — the hedis_measure_triggered field flags claims that would trigger a measure but does not validate eligibility populations.
• Synthetic, not derived from real payer data. Distributions match published AHIP/CMS/NHCAA/NCQA references but do NOT reflect any specific real payer (UnitedHealth, Anthem, Aetna, Humana, etc.).

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Ethical use guidance

This dataset is designed for:

• Payer-side fraud detection methodology development
• Claims adjudication ML pipeline testing
• Prior authorization optimization research
• Value-based care contract analytics methodology
• HEDIS quality measure pipeline development
• Healthcare AI pretraining for payer-side prediction tasks
• Educational use in actuarial science, health insurance management, and healthcare analytics

This dataset is not appropriate for:

• Making payment decisions about real claims
• Real fraud accusations against real providers
• Discriminatory analyses targeting protected demographic groups or provider taxonomy
• Insurance underwriting or premium-setting for real members
• Real provider network configuration without validation on real claim data

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Companion datasets in the Healthcare vertical

• HLT-001 — Synthetic Patient Population
• HLT-002 — Synthetic EHR
• HLT-003 — Synthetic Clinical Trial
• HLT-004 — Synthetic Disease Progression
• HLT-005 — Synthetic Hospital Admission
• HLT-006 — Synthetic Medical Imaging
• HLT-007 — Synthetic Drug Response
• HLT-008 — Synthetic Healthcare Claims (X12 / Provider Billing)
• HLT-009 — Synthetic ICU Vital Sign Monitoring
• HLT-010 — Synthetic Hospital Resource Usage
• HLT-011 — Synthetic Rare Disease + Trial Eligibility
• HLT-012 — Synthetic Pandemic Spread
• HLT-013 — Synthetic Multi-Modal Genomics
• HLT-014 — Synthetic Consumer Wearable Health
• HLT-015 — Synthetic Insurance Medical Claims (Payer Operations) (you are here)

Use HLT-001 through HLT-015 together for the full healthcare data stack. HLT-015 specifically completes the payer-side analytics axis that HLT-008 began on the provider side — together the two SKUs provide a full bilateral view of healthcare claims (provider billing + payer adjudication).

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Citation

If you use this dataset, please cite:

@dataset{xpertsystems_hlt015_sample_2026,
  author       = {XpertSystems.ai},
  title        = {HLT-015 Synthetic Insurance Medical Claims Dataset (Payer Operations) (Sample Preview)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/xpertsystems/hlt015-sample}
}

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Contact

• Web: https://xpertsystems.ai
• Email: pradeep@xpertsystems.ai
• Full product catalog: Cybersecurity, Insurance & Risk, Materials & Energy, Oil & Gas, Healthcare, and more

Sample License: CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) Full product License: Commercial — please contact for pricing.