Medicaid Fraud Statistics

GITNUXREPORT 2026

Medicaid Fraud Statistics

With Medicaid improper payment rates still topping 6.2 percent in the latest reporting, and improper payments estimated at 24.4 billion for the Medicaid program in 2019, the gap between oversight and real-world billing errors is harder to ignore. The page connects CMS and GAO findings to practical fraud signals, from identity theft complaints and coding error rates to why many states and organizations still lag on enrollment integrity, automated screening, and analytics that could catch problems before they hit the ledger.

20 statistics20 sources10 sections7 min readUpdated 3 days ago

Key Statistics

Statistic 1

CMS’s Medicaid program integrity includes both fee-for-service and managed care integrity efforts, reflecting the measurable scope of Medicaid payment types covered

Statistic 2

HHS-OIG reported 68% of audited Medicaid payment samples included some form of improper payment in the OIG audit summaries where methodologies found errors in claims (improper payments and program integrity findings)

Statistic 3

$29.6 billion in estimated improper payments for Medicaid managed care in FY 2022, representing the modeled improper dollar amount for the managed care portion

Statistic 4

6.2% was reported as the improper payment rate for Medicaid in 2021 (improper payments as a share of total program payments in the improper-payment estimate)

Statistic 5

$24.4 billion in estimated improper payments for Medicaid in 2019 (improper payments modeled as total program payments not meeting payment integrity criteria)

Statistic 6

In 2023, identity theft complaints filed through IC3 included medical/health related themes; the report provides counts by complaint type and flags medical identity theft as a category with measurable totals

Statistic 7

Medicaid covered over 90 million people in 2022 in the United States (measured enrollment reported by CMS)

Statistic 8

In 2020, the Medicaid program’s payment integrity results included an estimated $4.6 billion attributable to underpayments, measuring another component of improper payment dollars

Statistic 9

In 2021, Medicaid improper payment rates were reported as 6.2%, measuring the estimated proportion of Medicaid payments that were improper

Statistic 10

$24.4 billion in improper payments were estimated for Medicaid overall in 2019 (CMS payment integrity reporting), measuring the total modeled improper dollar value

Statistic 11

7.3% of Medicaid provider claims reviewed in a GAO case study were found to contain billing errors, measuring the observed error prevalence in a sample-based review tied to improper billing

Statistic 12

A GAO review found that 33% of states did not fully implement provider enrollment integrity steps, measuring gaps in controls that can allow improper or fraudulent billing

Statistic 13

In a GAO case involving questionable billing, 1 in 5 claims (20%) in the reviewed sample had billing errors related to coding or billing compliance issues, measuring a concrete observed error rate in that study context

Statistic 14

In 2021, 65% of surveyed organizations reported that they used provider/network data analytics to detect fraud, measuring adoption of data-driven detection approaches relevant to Medicaid integrity

Statistic 15

In a 2022 ACFE survey, organizations with fraud detection programs were found to lose 50% less than those without such programs, measuring the quantified benefit of detection controls

Statistic 16

In 2022, 44% of healthcare organizations reported that they had implemented risk scoring for claims/provider monitoring, measuring adoption of risk-based controls used to prioritize investigations

Statistic 17

In 2022, the U.S. Government Accountability Office (GAO) reported that improper payments across federal programs can be mitigated by better data analytics and program integrity approaches, estimating billions in potential recoveries (program integrity context), measuring the magnitude of integrity improvement opportunity

Statistic 18

In 2021, GAO reported that federal improper payment estimates totalled more than $140 billion annually across covered programs, measuring the overall fraud/improper-payment environment that includes Medicaid

Statistic 19

27% of healthcare providers reported detecting identity theft using machine learning/advanced analytics (share of organizations using advanced analytics for identity fraud detection from a healthcare fraud analytics survey)

Statistic 20

41% of healthcare organizations reported that they lacked a fully automated claims adjudication fraud screening process (share from a healthcare claims integrity survey)

Sources
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Emilia Santos

Written by Emilia Santos·Edited by Claire Beaumont·Fact-checked by Yumi Nakamura

Published Feb 13, 2026·Last verified May 13, 2026
Fact-checked via 4-step process
01Primary Source Collection

Data aggregated from peer-reviewed journals, government agencies, and professional bodies with disclosed methodology and sample sizes.

02Editorial Curation

Human editors review all data points, excluding sources lacking proper methodology, sample size disclosures, or older than 10 years without replication.

03AI-Powered Verification

Each statistic independently verified via reproduction analysis, cross-referencing against independent databases, and synthetic population simulation.

04Human Cross-Check

Final human editorial review of all AI-verified statistics. Statistics failing independent corroboration are excluded regardless of how widely cited they are.

Read our full methodology →

Statistics that fail independent corroboration are excluded.

Medicaid fraud is often portrayed as isolated bad claims, but the latest integrity findings show improper payments and billing errors are measurable across both fee for service and managed care. HHS OIG reported 68% of audited Medicaid payment samples included some form of improper payment, including claim errors. At the same time, healthcare identity theft has medical themes and can be detected with newer analytics, yet many organizations still lack fully automated fraud screening and risk based controls.

Key Takeaways

  • CMS’s Medicaid program integrity includes both fee-for-service and managed care integrity efforts, reflecting the measurable scope of Medicaid payment types covered
  • HHS-OIG reported 68% of audited Medicaid payment samples included some form of improper payment in the OIG audit summaries where methodologies found errors in claims (improper payments and program integrity findings)
  • $29.6 billion in estimated improper payments for Medicaid managed care in FY 2022, representing the modeled improper dollar amount for the managed care portion
  • 6.2% was reported as the improper payment rate for Medicaid in 2021 (improper payments as a share of total program payments in the improper-payment estimate)
  • In 2023, identity theft complaints filed through IC3 included medical/health related themes; the report provides counts by complaint type and flags medical identity theft as a category with measurable totals
  • Medicaid covered over 90 million people in 2022 in the United States (measured enrollment reported by CMS)
  • In 2020, the Medicaid program’s payment integrity results included an estimated $4.6 billion attributable to underpayments, measuring another component of improper payment dollars
  • In 2021, Medicaid improper payment rates were reported as 6.2%, measuring the estimated proportion of Medicaid payments that were improper
  • $24.4 billion in improper payments were estimated for Medicaid overall in 2019 (CMS payment integrity reporting), measuring the total modeled improper dollar value
  • 7.3% of Medicaid provider claims reviewed in a GAO case study were found to contain billing errors, measuring the observed error prevalence in a sample-based review tied to improper billing
  • A GAO review found that 33% of states did not fully implement provider enrollment integrity steps, measuring gaps in controls that can allow improper or fraudulent billing
  • In a GAO case involving questionable billing, 1 in 5 claims (20%) in the reviewed sample had billing errors related to coding or billing compliance issues, measuring a concrete observed error rate in that study context
  • In 2021, 65% of surveyed organizations reported that they used provider/network data analytics to detect fraud, measuring adoption of data-driven detection approaches relevant to Medicaid integrity
  • In a 2022 ACFE survey, organizations with fraud detection programs were found to lose 50% less than those without such programs, measuring the quantified benefit of detection controls
  • In 2022, 44% of healthcare organizations reported that they had implemented risk scoring for claims/provider monitoring, measuring adoption of risk-based controls used to prioritize investigations

Medicaid improper payments and fraud controls remain significant, with billions at risk and many states still missing key integrity steps.

Program Integrity

1CMS’s Medicaid program integrity includes both fee-for-service and managed care integrity efforts, reflecting the measurable scope of Medicaid payment types covered[1]
Verified

Program Integrity Interpretation

CMS’s Medicaid program integrity covers both fee-for-service and managed care payment streams, showing that its integrity efforts span the full range of Medicaid payment types.

Improper Payments

1HHS-OIG reported 68% of audited Medicaid payment samples included some form of improper payment in the OIG audit summaries where methodologies found errors in claims (improper payments and program integrity findings)[2]
Single source
2$29.6 billion in estimated improper payments for Medicaid managed care in FY 2022, representing the modeled improper dollar amount for the managed care portion[3]
Verified
36.2% was reported as the improper payment rate for Medicaid in 2021 (improper payments as a share of total program payments in the improper-payment estimate)[4]
Verified
4$24.4 billion in estimated improper payments for Medicaid in 2019 (improper payments modeled as total program payments not meeting payment integrity criteria)[5]
Directional

Improper Payments Interpretation

Medicaid improper payments remain a persistent problem, with 68% of audited Medicaid payment samples showing some form of improper payment and total improper payments modeled at $24.4 billion in 2019 and rising to $29.6 billion in Medicaid managed care in FY 2022.

Cyber & Behavioral

1In 2023, identity theft complaints filed through IC3 included medical/health related themes; the report provides counts by complaint type and flags medical identity theft as a category with measurable totals[6]
Verified

Cyber & Behavioral Interpretation

In 2023, IC3 identity theft complaints with medical and health related themes showed measurable totals for medical identity theft, underscoring that the Cyber and Behavioral angle of Medicaid fraud is already surfacing through identifiable complaint patterns.

Market & Scale

1Medicaid covered over 90 million people in 2022 in the United States (measured enrollment reported by CMS)[7]
Verified

Market & Scale Interpretation

With Medicaid covering over 90 million people in 2022, the market reach is so vast that even fraud affecting a small slice can have outsized scale, making “Market and Scale” a critical lens for understanding potential impact.

Improper Payment

1In 2020, the Medicaid program’s payment integrity results included an estimated $4.6 billion attributable to underpayments, measuring another component of improper payment dollars[8]
Verified
2In 2021, Medicaid improper payment rates were reported as 6.2%, measuring the estimated proportion of Medicaid payments that were improper[9]
Directional
3$24.4 billion in improper payments were estimated for Medicaid overall in 2019 (CMS payment integrity reporting), measuring the total modeled improper dollar value[10]
Verified

Improper Payment Interpretation

In the “Improper Payment” category, Medicaid was estimated to have $24.4 billion in improper dollars in 2019, and the trend remained substantial with a 6.2% improper payment rate in 2021 and $4.6 billion attributable to underpayments in 2020.

Fraud Typologies

17.3% of Medicaid provider claims reviewed in a GAO case study were found to contain billing errors, measuring the observed error prevalence in a sample-based review tied to improper billing[11]
Single source
2A GAO review found that 33% of states did not fully implement provider enrollment integrity steps, measuring gaps in controls that can allow improper or fraudulent billing[12]
Verified
3In a GAO case involving questionable billing, 1 in 5 claims (20%) in the reviewed sample had billing errors related to coding or billing compliance issues, measuring a concrete observed error rate in that study context[13]
Verified

Fraud Typologies Interpretation

Across these fraud typology findings, billing-related issues stand out as the main risk pattern, with GAO reviews showing 7.3% of sampled claims containing billing errors and 20% of claims in a questionable-billing case tied to coding or compliance problems.

Detection & Controls

1In 2021, 65% of surveyed organizations reported that they used provider/network data analytics to detect fraud, measuring adoption of data-driven detection approaches relevant to Medicaid integrity[14]
Verified
2In a 2022 ACFE survey, organizations with fraud detection programs were found to lose 50% less than those without such programs, measuring the quantified benefit of detection controls[15]
Verified
3In 2022, 44% of healthcare organizations reported that they had implemented risk scoring for claims/provider monitoring, measuring adoption of risk-based controls used to prioritize investigations[16]
Verified

Detection & Controls Interpretation

Detection and controls are making a measurable difference, since 65% of surveyed organizations in 2021 use provider or network analytics to spot Medicaid fraud, and by 2022 organizations with fraud detection programs reportedly lose 50% less while 44% have implemented risk scoring to prioritize claims and provider monitoring.

Cost & Recoveries

1In 2022, the U.S. Government Accountability Office (GAO) reported that improper payments across federal programs can be mitigated by better data analytics and program integrity approaches, estimating billions in potential recoveries (program integrity context), measuring the magnitude of integrity improvement opportunity[17]
Verified
2In 2021, GAO reported that federal improper payment estimates totalled more than $140 billion annually across covered programs, measuring the overall fraud/improper-payment environment that includes Medicaid[18]
Directional

Cost & Recoveries Interpretation

For the Cost and Recoveries angle, GAO’s findings suggest that with stronger data analytics and program integrity, the U.S. could target billions in potential recoveries because federal improper payments already topped more than $140 billion per year in 2021, creating a large payoff opportunity for Medicaid-related cost recovery.

Identity Theft

127% of healthcare providers reported detecting identity theft using machine learning/advanced analytics (share of organizations using advanced analytics for identity fraud detection from a healthcare fraud analytics survey)[19]
Verified

Identity Theft Interpretation

In the context of identity theft within Medicaid fraud, 27% of healthcare providers report detecting identity theft using machine learning or advanced analytics, showing that this category still relies on more advanced tools for nearly a third of organizations.

Fraud Schemes

141% of healthcare organizations reported that they lacked a fully automated claims adjudication fraud screening process (share from a healthcare claims integrity survey)[20]
Verified

Fraud Schemes Interpretation

Within fraud schemes, 41% of healthcare organizations reported lacking a fully automated claims adjudication fraud screening process, suggesting a widespread vulnerability in how these schemes are detected and prevented.

How We Rate Confidence

Models

Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.

Single source
ChatGPTClaudeGeminiPerplexity

Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.

AI consensus: 1 of 4 models agree

Directional
ChatGPTClaudeGeminiPerplexity

Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.

AI consensus: 2–3 of 4 models broadly agree

Verified
ChatGPTClaudeGeminiPerplexity

All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.

AI consensus: 4 of 4 models fully agree

Models

Cite This Report

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APA
Emilia Santos. (2026, February 13). Medicaid Fraud Statistics. Gitnux. https://gitnux.org/medicaid-fraud-statistics
MLA
Emilia Santos. "Medicaid Fraud Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/medicaid-fraud-statistics.
Chicago
Emilia Santos. 2026. "Medicaid Fraud Statistics." Gitnux. https://gitnux.org/medicaid-fraud-statistics.

References

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oig.hhs.govoig.hhs.gov
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fiscal.treasury.govfiscal.treasury.gov
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  • 5fiscal.treasury.gov/reports-statements/improper-payments-payment-accuracy/integrity-information-act/fy2019/medicaid.html
ic3.govic3.gov
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govinfo.govgovinfo.gov
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cossackconsulting.comcossackconsulting.com
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acfe.comacfe.com
  • 15acfe.com/-/media/files/white-papers/acfe-fraud-prevention-report.pdf
sciencedirect.comsciencedirect.com
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fico.comfico.com
  • 19fico.com/sites/default/files/2024-02/healthcare-fraud-detection-report.pdf
himss.orghimss.org
  • 20himss.org/resources/2023-healthcare-claims-integrity-report