Datasets:

xpertsystems
/

hlt015-sample

license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - synthetic
  - healthcare
  - insurance
  - payer-operations
  - claims-adjudication
  - prior-authorization
  - fraud-detection
  - ahip
  - cms
  - nhcaa
  - ncqa
  - hcup
  - caqh
  - icd-10
  - cpt
  - drg
  - hcc
  - raf
  - hedis
  - value-based-care
  - shared-savings
  - capitation
  - bundled-payment
  - denial-management
  - carc
  - cob
  - coordination-of-benefits
  - mlr
  - 30-day-readmission
  - siu
  - special-investigations-unit
pretty_name: >-
  HLT-015 Synthetic Insurance Medical Claims Dataset — Payer Operations (Sample
  Preview)
size_categories:
  - 1K<n<10K

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.

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.

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.

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

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).

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)

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

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.

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.).

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

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).

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}
}

Contact

Web: https://xpertsystems.ai
Email: pradeep@xpertsystems.ai
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Sample License: CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) Full product License: Commercial — please contact for pricing.