의료 사기 분석 시장 : 점유율 분석, 업계 동향과 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the healthcare fraud analytics market size is projected to expand from USD 2.66 billion in 2025 and USD 3.23 billion in 2026 to USD 9.22 billion by 2031, registering a CAGR of 23.36% between 2026 to 2031.

This report is Segmented by Solution Type (Descriptive, Predictive, Prescriptive), Deployment Mode (On-Premises, Cloud-Based), Application (Claims Review, Postpayment, Prepayment, Pharmacy Billing, Payment Integrity), End User (Providers, Insurance Companies, Government, Third-Party Providers), and Geography (North America, Europe, and More). The Market Forecasts are Provided in Value (USD).

Global Healthcare Fraud Analytics Market Trends and Insights

Rising Fraud Complexity Across Claims and Payment Workflows

The healthcare fraud analytics market is expanding because fraudulent behavior now moves across claims, payment channels, provider entities, and pharmacy transactions instead of staying inside one isolated billing event. CMS said the Fraud Defense Operations Center suspended USD 5.7 billion in suspected fraudulent Medicare payments and generated 372 referrals tied to USD 3.7 billion in billing during 2025, which shows how broad and connected current fraud patterns have become. A 2025 Scientific Reports paper showed that heterogeneous graph neural network models can detect fraud at the activity level within medical claims, which reflects the growing need to trace relationships rather than only inspect line-item anomalies. That matters because payers are no longer dealing only with isolated coding mistakes or simple overbilling behavior. They are reviewing coordinated activity across providers, patients, services, and submission paths that can look normal when each claim is viewed on its own. As a result, the healthcare fraud analytics market is moving toward continuously updated models and broader data linkages that can adapt faster than manual rule libraries and static watch lists.

Expansion of AI-Based Anomaly Detection in Healthcare Payments

The healthcare fraud analytics market is also gaining momentum as AI-based anomaly detection becomes more practical in daily payment review and investigative workflows. A 2025 review in the Journal of Big Data found that advanced machine learning approaches outperformed many older classifiers in healthcare fraud detection tasks, which supports the ongoing move away from basic rule engines alone. A 2025 paper in Information said federated learning can expand model training across decentralized insurer datasets without exposing raw patient data, which is important in heavily regulated care and insurance environments. A 2024 systematic review in Artificial Intelligence in Medicine also found that limited labeled fraud outcomes still constrain model calibration, which keeps validation quality central to platform selection. These findings support broader use of AI in the healthcare fraud analytics market because they reduce dependence on static alert libraries and narrow retrospective audits. Vendors that can pair anomaly detection with explainable outputs, investigator workflow support, and stable model governance are likely to gain buyer trust more quickly than vendors offering detection scores alone.

Interoperability Gaps Between Claims, EHR, and Pharmacy Data

Interoperability remains a major brake on the healthcare fraud analytics market because claims, clinical, and pharmacy data often sit in separate systems that do not connect well enough for full fraud review. The Asian Development Bank found that 90% of surveyed Asia-Pacific insurers do not collect the ICD or DRG codes needed for deeper fraud analysis, which sharply limits model training and benchmarking depth. That gap weakens the ability to compare behavior across providers, treatments, and payment types, especially in markets where digital records are still incomplete. Germany's statutory insurer federation has pushed for broader billing data pooling because single insurers cannot easily see organized patterns that spread across multiple payers. When records remain fragmented, analysts spend more time stitching files together and less time validating suspicious activity. This slows deployment in the healthcare fraud analytics market and gives a structural advantage to vendors that already hold wider benchmark data and stronger data-integration tools.

Other drivers and restraints analyzed in the detailed report include:

1. Growth in Real-Time Prepayment Controls by Payers
2. Regulatory Pressure for Program Integrity and Audit Readiness
3. High Tuning Burden for False Positive Reduction

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Descriptive analytics held 61.17% of healthcare fraud analytics market share in 2025, which kept it as the largest solution type in the market. Its position came from long use in retrospective claims review, payment pattern analysis, and billing anomaly checks across payer and public program audit settings. Many organizations still depend on descriptive tools because they support repeatable reporting, historical comparisons, and investigator workflows that have been built over many years. Predictive analytics is projected to grow at 24.37% CAGR through 2031, and the healthcare fraud analytics market size for this solution is rising as payers look for earlier warning signals that can surface abnormal behavior before claims move through full payment cycles. Prescriptive analytics remained the smallest segment, but it is gaining attention in environments where platforms can recommend claim holds, payment pauses, and case routing actions inside the review workflow.

Research in the Journal of Big Data found that deep learning and meta-learning approaches can outperform many legacy models in fraud detection tasks, which supports continued movement toward predictive capability. MDPI also noted that federated learning could widen model training across insurers without requiring raw patient data to be shared, which is important in privacy-sensitive health systems. These developments support the shift from descriptive review toward predictive action in the healthcare fraud analytics industry, especially where payers want stronger prepayment controls without losing explainability. Even so, descriptive tools should remain important because many buyers still need a clear historical evidence base before they expand into more automated intervention models. The near-term balance across solution types suggests gradual modernization rather than abrupt replacement, which supports steady renewal demand across the healthcare fraud analytics market.

On-premises deployment held 54.68% share in 2025, which kept it ahead of cloud-based models across the healthcare fraud analytics market. Government programs, federal contractors, and large payer organizations still prefer tighter local control when audit trails, security reviews, and procurement rules are strict. Existing infrastructure investments also support this position because large institutions often connect fraud systems to broader payment and claims administration environments that are already hosted internally. Cloud-based deployment is forecast to grow at 26.06% CAGR through 2031, and this pace reflects demand from mid-sized payers, administrators, and regional insurers that want faster rollout and lower infrastructure burden. The growth gap shows that deployment flexibility is becoming more important as buyers seek AI capability without waiting for long internal build cycles.

Cognizant said Bupa Hong Kong selected an AI-driven BPaaS model in December 2025 that combines claims automation, fraud, waste, and abuse detection, and compliance tooling in a cloud-led delivery structure. That example shows how the market is increasingly packaging analytics, process change, and managed operations into one contract. Cloud growth is also supported by buyers who need elastic computing power for model training and live scoring, but do not want to manage large internal teams for maintenance. On-premises systems should remain relevant in large government programs and tightly regulated environments, but growth is likely to stay stronger in cloud deployments. This leaves the healthcare fraud analytics industry with a mixed deployment path rather than a single uniform shift, which should keep both hosting models commercially relevant through the forecast period.

Complete Report Scope:

• By Solution Type
  • Descriptive Analytics
  • Predictive Analytics
  • Prescriptive Analytics
• By Deployment Mode
  • On-Premises
  • Cloud-Based
• By Application
  • Insurance Claims Review
  • Postpayment Review
  • Prepayment Review
  • Pharmacy Billing Misuse
  • Payment Integrity
  • Other Applications
• By End User
  • Healthcare Providers
  • Insurance Companies
  • Government Organizations
  • Third-Party Service Providers
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • Australia
    • South Korea
    • Rest of Asia-Pacific
  • Middle East & Africa
    • GCC
    • South Africa
    • Rest of Middle East & Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America held 43.64% of the healthcare fraud analytics market share in 2025, which kept it as the largest regional base in the market. CMS said the Fraud Defense Operations Center suspended USD 5.7 billion in suspected fraudulent Medicare payments and generated 372 referrals tied to USD 3.7 billion in billing during 2025, underscoring the region's strong enforcement intensity and data-driven oversight model. This enforcement depth gives the United States the region's clear lead in the healthcare fraud analytics market because public programs and private payers both need stronger detection and audit support. Canada and Mexico remain smaller markets, but payer digitization and public sector modernization continue to support adoption in specific workflows tied to claims review and payment control. Procurement in North America is closely linked to compliance, audit readiness, and the need to act on suspicious claims before payment rather than relying only on later recovery efforts.

Europe remained the second-largest regional base in the healthcare fraud analytics market, supported by national insurance systems that already use structured fraud controls and formal review frameworks. France reported a higher level of detected and prevented health insurance fraud in 2025, which shows that public payers are expanding analytical oversight and dedicated anti-fraud activity. Germany also reported its highest tracked billing fraud total and is seeking broader data pooling to improve cross-insurer detection, which points to a wider regional need for shared visibility across fragmented payer structures. The Middle East, Africa, and South America are still early-stage markets, but health system digitization in the Gulf and private insurance development in Brazil are creating a practical base for gradual adoption. These regions are smaller today, yet their path into the healthcare fraud analytics market is becoming clearer as digital claims administration improves and fraud controls become more formalized.

Asia-Pacific is projected to grow at 25.66% CAGR through 2031, and the healthcare fraud analytics market size in the region is rising faster than in any other geography in the study period. The Asian Development Bank said fraud, waste, and abuse account for 30-40% of health insurance claims costs across Asia-Pacific, while 90% of surveyed insurers do not collect ICD or DRG codes, which shows both the scale of the problem and the depth of the data gap. Nature reported that China is advancing digital health governance through a whole-of-society approach, which supports broader use of AI in claims oversight and audit systems as health data infrastructure becomes more coordinated. Growth in this region comes from expanding insurance pools, public digital health programs, and strong pressure to replace manual review with scalable analytics that can work across large beneficiary populations.

1. Change Healthcare
2. Codoxo
3. Conduent Incorporated
4. Cotiviti, Inc.
5. DXC Technology Company
6. Exl Service
7. Fair Isaac Corporation
8. FRAUDLENS INC.
9. FRISS International B.V.
10. H2O.ai
11. HCL Technologies Limited
12. Healthcare Fraud Shield
13. IBM
14. LexisNexis Risk Solutions
15. MultiPlan Corporation
16. Optum
17. Pondera Solutions
18. Qlarant Commercial Solutions Inc.
19. SAS Institute
20. Wipro

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions & Market Definition
• 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Rising Fraud Complexity Across Claims and Payment Workflows
  • 4.2.2 Expansion of AI-Based Anomaly Detection in Healthcare Payments
  • 4.2.3 Growth in Real-Time Prepayment Controls by Payers
  • 4.2.4 Regulatory Pressure for Program Integrity and Audit Readiness
  • 4.2.5 Underreported, Cross-Channel Fraud Linkage Across Pharmacy and Medical Claims
  • 4.2.6 Underreported, Demand for Graph-Based Provider-Patient-Network Intelligence
• 4.3 Market Restraints
  • 4.3.1 Interoperability Gaps Between Claims, EHR, and Pharmacy Data
  • 4.3.2 High Tuning Burden for False Positive Reduction
  • 4.3.3 Privacy, Data Residency, and Model Governance Constraints
  • 4.3.4 Underreported, Limited Access to Labeled Fraud Outcomes for Model Training
• 4.4 Value / Supply-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Industry Rivalry

5 Market Size & Growth Forecasts

• 5.1 By Solution Type
  • 5.1.1 Descriptive Analytics
  • 5.1.2 Predictive Analytics
  • 5.1.3 Prescriptive Analytics
• 5.2 By Deployment Mode
  • 5.2.1 On-Premises
  • 5.2.2 Cloud-Based
• 5.3 By Application
  • 5.3.1 Insurance Claims Review
  • 5.3.2 Postpayment Review
  • 5.3.3 Prepayment Review
  • 5.3.4 Pharmacy Billing Misuse
  • 5.3.5 Payment Integrity
  • 5.3.6 Other Applications
• 5.4 By End User
  • 5.4.1 Healthcare Providers
  • 5.4.2 Insurance Companies
  • 5.4.3 Government Organizations
  • 5.4.4 Third-Party Service Providers
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Spain
    • 5.5.2.6 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 Japan
    • 5.5.3.3 India
    • 5.5.3.4 Australia
    • 5.5.3.5 South Korea
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 Middle East & Africa
    • 5.5.4.1 GCC
    • 5.5.4.2 South Africa
    • 5.5.4.3 Rest of Middle East & Africa
  • 5.5.5 South America
    • 5.5.5.1 Brazil
    • 5.5.5.2 Argentina
    • 5.5.5.3 Rest of South America

6 Competitive Landscape

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles (includes Global Level Overview, Market-level Overview, Core Segments, Financials, Strategic Information, Market Rank/Share, Products & Services, Recent Developments)
  • 6.3.1 Change Healthcare
  • 6.3.2 Codoxo
  • 6.3.3 Conduent Incorporated
  • 6.3.4 Cotiviti, Inc.
  • 6.3.5 DXC Technology Company
  • 6.3.6 ExlService Holdings, Inc.
  • 6.3.7 Fair Isaac Corporation
  • 6.3.8 FRAUDLENS INC.
  • 6.3.9 FRISS International B.V.
  • 6.3.10 H2O.ai
  • 6.3.11 HCL Technologies Limited
  • 6.3.12 Healthcare Fraud Shield
  • 6.3.13 IBM Corporation
  • 6.3.14 LexisNexis Risk Solutions
  • 6.3.15 MultiPlan Corporation
  • 6.3.16 Optum Inc.
  • 6.3.17 Pondera Solutions
  • 6.3.18 Qlarant Commercial Solutions Inc.
  • 6.3.19 SAS Institute Inc.
  • 6.3.20 Wipro Limited

7 Market Opportunities & Future Outlook

• 7.1 White-space & Unmet-need Assessment