Global Healthcare Fraud Detection

Healthcare Fraud Detection Comprehensive Study by Type (Descriptive Analytics, Predictive Analytics, Prescriptive Analytics), Application (Insurance Claims Review, Payment Integrity, Other), Components (Service, Software), End Users (Private Insurance Payers, Public/Government Agencies, Third-Party Service Providers, Employers, Others), Delivery Mode (On-Premises Delivery Models, On-Demand Delivery Models) Players and Region - Global Market Outlook to 2030

Healthcare Fraud Detection Market by XX Submarkets | Forecast Years 2024-2030  

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  • Summary
  • Market Segments
  • Table of Content
  • List of Table & Figures
  • Players Profiled
About Healthcare Fraud Detection
Healthcare fraud detection is specially designed to prevent healthcare frauds, abuse, and waste so that any unauthorized payment and benefits can be avoided. They are usually used to avoid misrepresenting dates, falsification of data by physicians, and submitting claims for services not provided among others. An increasing number of patients seeking health insurance is the vital factor escalating the market growth, also the rising prepayment review model, increasing returns on investment, increase in pharmacy claims-related fraud, increasing population adapting health insurance and rising need for solutions that have biometric sensors to identify frauds are the major factors among others driving the healthcare fraud detection market. Moreover, the increasing adoption of healthcare fraud analytics in developing countries, the rising emergence of social media and its impact on the healthcare industry and the role of AI in healthcare fraud detection will further create new opportunities for healthcare fraud detection

AttributesDetails
Study Period2018-2030
Base Year2023
UnitValue (USD Million)


The market is highly competitive with the active presence of dominant players. The companies are exploring the market by adopting mergers & acquisitions, expansions, investments, new service launches and collaborations as their preferred strategies. The players are exploring new geographies through expansions and acquisitions to avail a competitive advantage through combined synergies. Analyst at AMA Research estimates that Global Players will contribute the maximum growth to Global Healthcare Fraud Detection market throughout the forecasted period. Established and emerging Players should take a closer view at their existing organizations and reinvent traditional business and operating models to adapt to the future.

IBM (United States), Optum (United States), HCL Technologies Limited (India), CGI Group Inc. (Canada), RELX Group plc (United Kingdom), Wipro Limited (India), SAS (US), McKesson (United States), SCIO (United Stats) and Verscend (United States) are some of the key players that are part of study coverage. Additionally, the Players which are also part of the research coverage are ExlService Holdings Inc. (Ireland), DXC (United States), Northrop Grumman (United States), LexisNexis (United States) and Pondera (United States).

Segmentation Overview
AMA Research has segmented the market of Global Healthcare Fraud Detection market by Type (Descriptive Analytics, Predictive Analytics and Prescriptive Analytics), Application (Insurance Claims Review, Payment Integrity and Other) and Region.



On the basis of geography, the market of Healthcare Fraud Detection has been segmented into South America (Brazil, Argentina, Rest of South America), Asia Pacific (China, Japan, India, South Korea, Taiwan, Australia, Rest of Asia-Pacific), Europe (Germany, France, Italy, United Kingdom, Netherlands, Rest of Europe), MEA (Middle East, Africa), North America (United States, Canada, Mexico). If we see Market by Components, the sub-segment i.e. Service will boost the Healthcare Fraud Detection market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth. If we see Market by End Users, the sub-segment i.e. Private Insurance Payers will boost the Healthcare Fraud Detection market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth. If we see Market by Delivery Mode, the sub-segment i.e. On-Premises Delivery Models will boost the Healthcare Fraud Detection market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth.

Influencing Trend:
Increasing use of data mining and machine learning techniques to detect fraud.

Market Growth Drivers:
Increasing number of patients seeking health insurance and Increasing returns on investment

Challenges:
High cost of healthcare fraud detection technologies

Restraints:
Lack of skilled and trained profession

Opportunities:
Machine learning (ML) and artificial intelligence (AI) techniques to look at claims data and find fraudulent activity. and Enhanced data visualization tools and techniques

Market Leaders and their expansionary development strategies
In May 2023, Mastercard and HealthLock say they’ve teamed to help consumers avoid medical bill fraud and claim errors. The partnership, announced Monday (May 22), gives Mastercard health savings account and flexible spending account users access to Healthlock’s platform, which lets them monitor all healthcare claims in one place, prevent fraud, potentially reduce bills and reverse rejected claims.
In June 2023, Verisk Launches Next Generation of Insurance Fraud Analytics in Israel with Kyndryl Technology. The new solution leverages Kyndryl’s powerful computing resources and Verisk’s deep domain expertise to quickly analyze new auto claims for bodily injury (compulsory insurance in Israel) and detect indications of fraud. Insurance fraud in Israel costs insurers hundreds of millions of New Israeli Shekel (NIS) per year, resulting in higher premiums for policyholders.


Key Target Audience
Health Insurance Companies, Government Agencies, Fraud Detection & Analytics Companies, Hospitals and Clinics and Others

About Approach
To evaluate and validate the market size various sources including primary and secondary analysis is utilized. AMA Research follows regulatory standards such as NAICS/SIC/ICB/TRCB, to have a better understanding of the market. The market study is conducted on basis of more than 200 companies dealing in the market regional as well as global areas with the purpose to understand the companies positioning regarding the market value, volume, and their market share for regional as well as global.

Further to bring relevance specific to any niche market we set and apply a number of criteria like Geographic Footprints, Regional Segments of Revenue, Operational Centres, etc. The next step is to finalize a team (In-House + Data Agencies) who then starts collecting C & D level executives and profiles, Industry experts, Opinion leaders, etc., and work towards appointment generation.

The primary research is performed by taking the interviews of executives of various companies dealing in the market as well as using the survey reports, research institute, and latest research reports. Meanwhile, the analyst team keeps preparing a set of questionnaires, and after getting the appointee list; the target audience is then tapped and segregated with various mediums and channels that are feasible for making connections that including email communication, telephonic, skype, LinkedIn Group & InMail, Community Forums, Community Forums, open Survey, SurveyMonkey, etc.

Report Objectives / Segmentation Covered

By Type
  • Descriptive Analytics
  • Predictive Analytics
  • Prescriptive Analytics
By Application
  • Insurance Claims Review
  • Payment Integrity
  • Other
By Components
  • Service
  • Software

By End Users
  • Private Insurance Payers
  • Public/Government Agencies
  • Third-Party Service Providers
  • Employers
  • Others

By Delivery Mode
  • On-Premises Delivery Models
  • On-Demand Delivery Models

By Regions
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Taiwan
    • Australia
    • Rest of Asia-Pacific
  • Europe
    • Germany
    • France
    • Italy
    • United Kingdom
    • Netherlands
    • Rest of Europe
  • MEA
    • Middle East
    • Africa
  • North America
    • United States
    • Canada
    • Mexico
  • 1. Market Overview
    • 1.1. Introduction
    • 1.2. Scope/Objective of the Study
      • 1.2.1. Research Objective
  • 2. Executive Summary
    • 2.1. Introduction
  • 3. Market Dynamics
    • 3.1. Introduction
    • 3.2. Market Drivers
      • 3.2.1. Increasing number of patients seeking health insurance
      • 3.2.2. Increasing returns on investment
    • 3.3. Market Challenges
      • 3.3.1. High cost of healthcare fraud detection technologies
    • 3.4. Market Trends
      • 3.4.1. Increasing use of data mining and machine learning techniques to detect fraud.
  • 4. Market Factor Analysis
    • 4.1. Porters Five Forces
    • 4.2. Supply/Value Chain
    • 4.3. PESTEL analysis
    • 4.4. Market Entropy
    • 4.5. Patent/Trademark Analysis
  • 5. Global Healthcare Fraud Detection, by Type, Application, Components, End Users, Delivery Mode and Region (value) (2018-2023)
    • 5.1. Introduction
    • 5.2. Global Healthcare Fraud Detection (Value)
      • 5.2.1. Global Healthcare Fraud Detection by: Type (Value)
        • 5.2.1.1. Descriptive Analytics
        • 5.2.1.2. Predictive Analytics
        • 5.2.1.3. Prescriptive Analytics
      • 5.2.2. Global Healthcare Fraud Detection by: Application (Value)
        • 5.2.2.1. Insurance Claims Review
        • 5.2.2.2. Payment Integrity
        • 5.2.2.3. Other
      • 5.2.3. Global Healthcare Fraud Detection by: Components (Value)
        • 5.2.3.1. Service
        • 5.2.3.2. Software
      • 5.2.4. Global Healthcare Fraud Detection by: End Users (Value)
        • 5.2.4.1. Private Insurance Payers
        • 5.2.4.2. Public/Government Agencies
        • 5.2.4.3. Third-Party Service Providers
        • 5.2.4.4. Employers
        • 5.2.4.5. Others
      • 5.2.5. Global Healthcare Fraud Detection by: Delivery Mode (Value)
        • 5.2.5.1. On-Premises Delivery Models
        • 5.2.5.2. On-Demand Delivery Models
      • 5.2.6. Global Healthcare Fraud Detection Region
        • 5.2.6.1. South America
          • 5.2.6.1.1. Brazil
          • 5.2.6.1.2. Argentina
          • 5.2.6.1.3. Rest of South America
        • 5.2.6.2. Asia Pacific
          • 5.2.6.2.1. China
          • 5.2.6.2.2. Japan
          • 5.2.6.2.3. India
          • 5.2.6.2.4. South Korea
          • 5.2.6.2.5. Taiwan
          • 5.2.6.2.6. Australia
          • 5.2.6.2.7. Rest of Asia-Pacific
        • 5.2.6.3. Europe
          • 5.2.6.3.1. Germany
          • 5.2.6.3.2. France
          • 5.2.6.3.3. Italy
          • 5.2.6.3.4. United Kingdom
          • 5.2.6.3.5. Netherlands
          • 5.2.6.3.6. Rest of Europe
        • 5.2.6.4. MEA
          • 5.2.6.4.1. Middle East
          • 5.2.6.4.2. Africa
        • 5.2.6.5. North America
          • 5.2.6.5.1. United States
          • 5.2.6.5.2. Canada
          • 5.2.6.5.3. Mexico
  • 6. Healthcare Fraud Detection: Manufacturers/Players Analysis
    • 6.1. Competitive Landscape
      • 6.1.1. Market Share Analysis
        • 6.1.1.1. Top 3
        • 6.1.1.2. Top 5
    • 6.2. Peer Group Analysis (2023)
    • 6.3. BCG Matrix
    • 6.4. Company Profile
      • 6.4.1. IBM (United States)
        • 6.4.1.1. Business Overview
        • 6.4.1.2. Products/Services Offerings
        • 6.4.1.3. Financial Analysis
        • 6.4.1.4. SWOT Analysis
      • 6.4.2. Optum (United States)
        • 6.4.2.1. Business Overview
        • 6.4.2.2. Products/Services Offerings
        • 6.4.2.3. Financial Analysis
        • 6.4.2.4. SWOT Analysis
      • 6.4.3. HCL Technologies Limited (India)
        • 6.4.3.1. Business Overview
        • 6.4.3.2. Products/Services Offerings
        • 6.4.3.3. Financial Analysis
        • 6.4.3.4. SWOT Analysis
      • 6.4.4. CGI Group Inc. (Canada)
        • 6.4.4.1. Business Overview
        • 6.4.4.2. Products/Services Offerings
        • 6.4.4.3. Financial Analysis
        • 6.4.4.4. SWOT Analysis
      • 6.4.5. RELX Group plc (United Kingdom)
        • 6.4.5.1. Business Overview
        • 6.4.5.2. Products/Services Offerings
        • 6.4.5.3. Financial Analysis
        • 6.4.5.4. SWOT Analysis
      • 6.4.6. Wipro Limited (India)
        • 6.4.6.1. Business Overview
        • 6.4.6.2. Products/Services Offerings
        • 6.4.6.3. Financial Analysis
        • 6.4.6.4. SWOT Analysis
      • 6.4.7. SAS (US)
        • 6.4.7.1. Business Overview
        • 6.4.7.2. Products/Services Offerings
        • 6.4.7.3. Financial Analysis
        • 6.4.7.4. SWOT Analysis
      • 6.4.8. McKesson (United States)
        • 6.4.8.1. Business Overview
        • 6.4.8.2. Products/Services Offerings
        • 6.4.8.3. Financial Analysis
        • 6.4.8.4. SWOT Analysis
      • 6.4.9. SCIO (United Stats)
        • 6.4.9.1. Business Overview
        • 6.4.9.2. Products/Services Offerings
        • 6.4.9.3. Financial Analysis
        • 6.4.9.4. SWOT Analysis
      • 6.4.10. Verscend (United States)
        • 6.4.10.1. Business Overview
        • 6.4.10.2. Products/Services Offerings
        • 6.4.10.3. Financial Analysis
        • 6.4.10.4. SWOT Analysis
  • 7. Global Healthcare Fraud Detection Sale, by Type, Application, Components, End Users, Delivery Mode and Region (value) (2025-2030)
    • 7.1. Introduction
    • 7.2. Global Healthcare Fraud Detection (Value)
      • 7.2.1. Global Healthcare Fraud Detection by: Type (Value)
        • 7.2.1.1. Descriptive Analytics
        • 7.2.1.2. Predictive Analytics
        • 7.2.1.3. Prescriptive Analytics
      • 7.2.2. Global Healthcare Fraud Detection by: Application (Value)
        • 7.2.2.1. Insurance Claims Review
        • 7.2.2.2. Payment Integrity
        • 7.2.2.3. Other
      • 7.2.3. Global Healthcare Fraud Detection by: Components (Value)
        • 7.2.3.1. Service
        • 7.2.3.2. Software
      • 7.2.4. Global Healthcare Fraud Detection by: End Users (Value)
        • 7.2.4.1. Private Insurance Payers
        • 7.2.4.2. Public/Government Agencies
        • 7.2.4.3. Third-Party Service Providers
        • 7.2.4.4. Employers
        • 7.2.4.5. Others
      • 7.2.5. Global Healthcare Fraud Detection by: Delivery Mode (Value)
        • 7.2.5.1. On-Premises Delivery Models
        • 7.2.5.2. On-Demand Delivery Models
      • 7.2.6. Global Healthcare Fraud Detection Region
        • 7.2.6.1. South America
          • 7.2.6.1.1. Brazil
          • 7.2.6.1.2. Argentina
          • 7.2.6.1.3. Rest of South America
        • 7.2.6.2. Asia Pacific
          • 7.2.6.2.1. China
          • 7.2.6.2.2. Japan
          • 7.2.6.2.3. India
          • 7.2.6.2.4. South Korea
          • 7.2.6.2.5. Taiwan
          • 7.2.6.2.6. Australia
          • 7.2.6.2.7. Rest of Asia-Pacific
        • 7.2.6.3. Europe
          • 7.2.6.3.1. Germany
          • 7.2.6.3.2. France
          • 7.2.6.3.3. Italy
          • 7.2.6.3.4. United Kingdom
          • 7.2.6.3.5. Netherlands
          • 7.2.6.3.6. Rest of Europe
        • 7.2.6.4. MEA
          • 7.2.6.4.1. Middle East
          • 7.2.6.4.2. Africa
        • 7.2.6.5. North America
          • 7.2.6.5.1. United States
          • 7.2.6.5.2. Canada
          • 7.2.6.5.3. Mexico
  • 8. Appendix
    • 8.1. Acronyms
  • 9. Methodology and Data Source
    • 9.1. Methodology/Research Approach
      • 9.1.1. Research Programs/Design
      • 9.1.2. Market Size Estimation
      • 9.1.3. Market Breakdown and Data Triangulation
    • 9.2. Data Source
      • 9.2.1. Secondary Sources
      • 9.2.2. Primary Sources
    • 9.3. Disclaimer
List of Tables
  • Table 1. Healthcare Fraud Detection: by Type(USD Million)
  • Table 2. Healthcare Fraud Detection Descriptive Analytics , by Region USD Million (2018-2023)
  • Table 3. Healthcare Fraud Detection Predictive Analytics , by Region USD Million (2018-2023)
  • Table 4. Healthcare Fraud Detection Prescriptive Analytics , by Region USD Million (2018-2023)
  • Table 5. Healthcare Fraud Detection: by Application(USD Million)
  • Table 6. Healthcare Fraud Detection Insurance Claims Review , by Region USD Million (2018-2023)
  • Table 7. Healthcare Fraud Detection Payment Integrity , by Region USD Million (2018-2023)
  • Table 8. Healthcare Fraud Detection Other , by Region USD Million (2018-2023)
  • Table 9. Healthcare Fraud Detection: by Components(USD Million)
  • Table 10. Healthcare Fraud Detection Service , by Region USD Million (2018-2023)
  • Table 11. Healthcare Fraud Detection Software , by Region USD Million (2018-2023)
  • Table 12. Healthcare Fraud Detection: by End Users(USD Million)
  • Table 13. Healthcare Fraud Detection Private Insurance Payers , by Region USD Million (2018-2023)
  • Table 14. Healthcare Fraud Detection Public/Government Agencies , by Region USD Million (2018-2023)
  • Table 15. Healthcare Fraud Detection Third-Party Service Providers , by Region USD Million (2018-2023)
  • Table 16. Healthcare Fraud Detection Employers , by Region USD Million (2018-2023)
  • Table 17. Healthcare Fraud Detection Others , by Region USD Million (2018-2023)
  • Table 18. Healthcare Fraud Detection: by Delivery Mode(USD Million)
  • Table 19. Healthcare Fraud Detection On-Premises Delivery Models , by Region USD Million (2018-2023)
  • Table 20. Healthcare Fraud Detection On-Demand Delivery Models , by Region USD Million (2018-2023)
  • Table 21. South America Healthcare Fraud Detection, by Country USD Million (2018-2023)
  • Table 22. South America Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 23. South America Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 24. South America Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 25. South America Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 26. South America Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 27. Brazil Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 28. Brazil Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 29. Brazil Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 30. Brazil Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 31. Brazil Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 32. Argentina Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 33. Argentina Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 34. Argentina Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 35. Argentina Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 36. Argentina Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 37. Rest of South America Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 38. Rest of South America Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 39. Rest of South America Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 40. Rest of South America Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 41. Rest of South America Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 42. Asia Pacific Healthcare Fraud Detection, by Country USD Million (2018-2023)
  • Table 43. Asia Pacific Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 44. Asia Pacific Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 45. Asia Pacific Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 46. Asia Pacific Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 47. Asia Pacific Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 48. China Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 49. China Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 50. China Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 51. China Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 52. China Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 53. Japan Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 54. Japan Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 55. Japan Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 56. Japan Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 57. Japan Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 58. India Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 59. India Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 60. India Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 61. India Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 62. India Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 63. South Korea Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 64. South Korea Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 65. South Korea Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 66. South Korea Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 67. South Korea Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 68. Taiwan Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 69. Taiwan Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 70. Taiwan Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 71. Taiwan Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 72. Taiwan Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 73. Australia Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 74. Australia Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 75. Australia Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 76. Australia Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 77. Australia Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 78. Rest of Asia-Pacific Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 79. Rest of Asia-Pacific Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 80. Rest of Asia-Pacific Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 81. Rest of Asia-Pacific Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 82. Rest of Asia-Pacific Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 83. Europe Healthcare Fraud Detection, by Country USD Million (2018-2023)
  • Table 84. Europe Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 85. Europe Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 86. Europe Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 87. Europe Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 88. Europe Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 89. Germany Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 90. Germany Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 91. Germany Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 92. Germany Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 93. Germany Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 94. France Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 95. France Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 96. France Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 97. France Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 98. France Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 99. Italy Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 100. Italy Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 101. Italy Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 102. Italy Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 103. Italy Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 104. United Kingdom Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 105. United Kingdom Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 106. United Kingdom Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 107. United Kingdom Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 108. United Kingdom Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 109. Netherlands Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 110. Netherlands Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 111. Netherlands Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 112. Netherlands Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 113. Netherlands Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 114. Rest of Europe Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 115. Rest of Europe Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 116. Rest of Europe Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 117. Rest of Europe Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 118. Rest of Europe Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 119. MEA Healthcare Fraud Detection, by Country USD Million (2018-2023)
  • Table 120. MEA Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 121. MEA Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 122. MEA Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 123. MEA Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 124. MEA Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 125. Middle East Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 126. Middle East Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 127. Middle East Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 128. Middle East Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 129. Middle East Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 130. Africa Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 131. Africa Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 132. Africa Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 133. Africa Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 134. Africa Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 135. North America Healthcare Fraud Detection, by Country USD Million (2018-2023)
  • Table 136. North America Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 137. North America Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 138. North America Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 139. North America Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 140. North America Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 141. United States Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 142. United States Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 143. United States Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 144. United States Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 145. United States Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 146. Canada Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 147. Canada Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 148. Canada Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 149. Canada Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 150. Canada Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 151. Mexico Healthcare Fraud Detection, by Type USD Million (2018-2023)
  • Table 152. Mexico Healthcare Fraud Detection, by Application USD Million (2018-2023)
  • Table 153. Mexico Healthcare Fraud Detection, by Components USD Million (2018-2023)
  • Table 154. Mexico Healthcare Fraud Detection, by End Users USD Million (2018-2023)
  • Table 155. Mexico Healthcare Fraud Detection, by Delivery Mode USD Million (2018-2023)
  • Table 156. Company Basic Information, Sales Area and Its Competitors
  • Table 157. Company Basic Information, Sales Area and Its Competitors
  • Table 158. Company Basic Information, Sales Area and Its Competitors
  • Table 159. Company Basic Information, Sales Area and Its Competitors
  • Table 160. Company Basic Information, Sales Area and Its Competitors
  • Table 161. Company Basic Information, Sales Area and Its Competitors
  • Table 162. Company Basic Information, Sales Area and Its Competitors
  • Table 163. Company Basic Information, Sales Area and Its Competitors
  • Table 164. Company Basic Information, Sales Area and Its Competitors
  • Table 165. Company Basic Information, Sales Area and Its Competitors
  • Table 166. Healthcare Fraud Detection: by Type(USD Million)
  • Table 167. Healthcare Fraud Detection Descriptive Analytics , by Region USD Million (2025-2030)
  • Table 168. Healthcare Fraud Detection Predictive Analytics , by Region USD Million (2025-2030)
  • Table 169. Healthcare Fraud Detection Prescriptive Analytics , by Region USD Million (2025-2030)
  • Table 170. Healthcare Fraud Detection: by Application(USD Million)
  • Table 171. Healthcare Fraud Detection Insurance Claims Review , by Region USD Million (2025-2030)
  • Table 172. Healthcare Fraud Detection Payment Integrity , by Region USD Million (2025-2030)
  • Table 173. Healthcare Fraud Detection Other , by Region USD Million (2025-2030)
  • Table 174. Healthcare Fraud Detection: by Components(USD Million)
  • Table 175. Healthcare Fraud Detection Service , by Region USD Million (2025-2030)
  • Table 176. Healthcare Fraud Detection Software , by Region USD Million (2025-2030)
  • Table 177. Healthcare Fraud Detection: by End Users(USD Million)
  • Table 178. Healthcare Fraud Detection Private Insurance Payers , by Region USD Million (2025-2030)
  • Table 179. Healthcare Fraud Detection Public/Government Agencies , by Region USD Million (2025-2030)
  • Table 180. Healthcare Fraud Detection Third-Party Service Providers , by Region USD Million (2025-2030)
  • Table 181. Healthcare Fraud Detection Employers , by Region USD Million (2025-2030)
  • Table 182. Healthcare Fraud Detection Others , by Region USD Million (2025-2030)
  • Table 183. Healthcare Fraud Detection: by Delivery Mode(USD Million)
  • Table 184. Healthcare Fraud Detection On-Premises Delivery Models , by Region USD Million (2025-2030)
  • Table 185. Healthcare Fraud Detection On-Demand Delivery Models , by Region USD Million (2025-2030)
  • Table 186. South America Healthcare Fraud Detection, by Country USD Million (2025-2030)
  • Table 187. South America Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 188. South America Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 189. South America Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 190. South America Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 191. South America Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 192. Brazil Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 193. Brazil Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 194. Brazil Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 195. Brazil Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 196. Brazil Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 197. Argentina Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 198. Argentina Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 199. Argentina Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 200. Argentina Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 201. Argentina Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 202. Rest of South America Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 203. Rest of South America Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 204. Rest of South America Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 205. Rest of South America Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 206. Rest of South America Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 207. Asia Pacific Healthcare Fraud Detection, by Country USD Million (2025-2030)
  • Table 208. Asia Pacific Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 209. Asia Pacific Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 210. Asia Pacific Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 211. Asia Pacific Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 212. Asia Pacific Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 213. China Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 214. China Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 215. China Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 216. China Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 217. China Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 218. Japan Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 219. Japan Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 220. Japan Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 221. Japan Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 222. Japan Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 223. India Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 224. India Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 225. India Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 226. India Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 227. India Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 228. South Korea Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 229. South Korea Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 230. South Korea Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 231. South Korea Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 232. South Korea Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 233. Taiwan Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 234. Taiwan Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 235. Taiwan Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 236. Taiwan Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 237. Taiwan Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 238. Australia Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 239. Australia Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 240. Australia Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 241. Australia Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 242. Australia Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 243. Rest of Asia-Pacific Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 244. Rest of Asia-Pacific Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 245. Rest of Asia-Pacific Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 246. Rest of Asia-Pacific Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 247. Rest of Asia-Pacific Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 248. Europe Healthcare Fraud Detection, by Country USD Million (2025-2030)
  • Table 249. Europe Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 250. Europe Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 251. Europe Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 252. Europe Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 253. Europe Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 254. Germany Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 255. Germany Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 256. Germany Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 257. Germany Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 258. Germany Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 259. France Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 260. France Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 261. France Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 262. France Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 263. France Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 264. Italy Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 265. Italy Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 266. Italy Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 267. Italy Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 268. Italy Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 269. United Kingdom Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 270. United Kingdom Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 271. United Kingdom Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 272. United Kingdom Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 273. United Kingdom Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 274. Netherlands Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 275. Netherlands Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 276. Netherlands Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 277. Netherlands Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 278. Netherlands Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 279. Rest of Europe Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 280. Rest of Europe Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 281. Rest of Europe Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 282. Rest of Europe Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 283. Rest of Europe Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 284. MEA Healthcare Fraud Detection, by Country USD Million (2025-2030)
  • Table 285. MEA Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 286. MEA Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 287. MEA Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 288. MEA Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 289. MEA Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 290. Middle East Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 291. Middle East Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 292. Middle East Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 293. Middle East Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 294. Middle East Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 295. Africa Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 296. Africa Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 297. Africa Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 298. Africa Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 299. Africa Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 300. North America Healthcare Fraud Detection, by Country USD Million (2025-2030)
  • Table 301. North America Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 302. North America Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 303. North America Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 304. North America Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 305. North America Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 306. United States Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 307. United States Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 308. United States Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 309. United States Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 310. United States Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 311. Canada Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 312. Canada Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 313. Canada Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 314. Canada Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 315. Canada Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 316. Mexico Healthcare Fraud Detection, by Type USD Million (2025-2030)
  • Table 317. Mexico Healthcare Fraud Detection, by Application USD Million (2025-2030)
  • Table 318. Mexico Healthcare Fraud Detection, by Components USD Million (2025-2030)
  • Table 319. Mexico Healthcare Fraud Detection, by End Users USD Million (2025-2030)
  • Table 320. Mexico Healthcare Fraud Detection, by Delivery Mode USD Million (2025-2030)
  • Table 321. Research Programs/Design for This Report
  • Table 322. Key Data Information from Secondary Sources
  • Table 323. Key Data Information from Primary Sources
List of Figures
  • Figure 1. Porters Five Forces
  • Figure 2. Supply/Value Chain
  • Figure 3. PESTEL analysis
  • Figure 4. Global Healthcare Fraud Detection: by Type USD Million (2018-2023)
  • Figure 5. Global Healthcare Fraud Detection: by Application USD Million (2018-2023)
  • Figure 6. Global Healthcare Fraud Detection: by Components USD Million (2018-2023)
  • Figure 7. Global Healthcare Fraud Detection: by End Users USD Million (2018-2023)
  • Figure 8. Global Healthcare Fraud Detection: by Delivery Mode USD Million (2018-2023)
  • Figure 9. South America Healthcare Fraud Detection Share (%), by Country
  • Figure 10. Asia Pacific Healthcare Fraud Detection Share (%), by Country
  • Figure 11. Europe Healthcare Fraud Detection Share (%), by Country
  • Figure 12. MEA Healthcare Fraud Detection Share (%), by Country
  • Figure 13. North America Healthcare Fraud Detection Share (%), by Country
  • Figure 14. Global Healthcare Fraud Detection share by Players 2023 (%)
  • Figure 15. Global Healthcare Fraud Detection share by Players (Top 3) 2023(%)
  • Figure 16. Global Healthcare Fraud Detection share by Players (Top 5) 2023(%)
  • Figure 17. BCG Matrix for key Companies
  • Figure 18. IBM (United States) Revenue, Net Income and Gross profit
  • Figure 19. IBM (United States) Revenue: by Geography 2023
  • Figure 20. Optum (United States) Revenue, Net Income and Gross profit
  • Figure 21. Optum (United States) Revenue: by Geography 2023
  • Figure 22. HCL Technologies Limited (India) Revenue, Net Income and Gross profit
  • Figure 23. HCL Technologies Limited (India) Revenue: by Geography 2023
  • Figure 24. CGI Group Inc. (Canada) Revenue, Net Income and Gross profit
  • Figure 25. CGI Group Inc. (Canada) Revenue: by Geography 2023
  • Figure 26. RELX Group plc (United Kingdom) Revenue, Net Income and Gross profit
  • Figure 27. RELX Group plc (United Kingdom) Revenue: by Geography 2023
  • Figure 28. Wipro Limited (India) Revenue, Net Income and Gross profit
  • Figure 29. Wipro Limited (India) Revenue: by Geography 2023
  • Figure 30. SAS (US) Revenue, Net Income and Gross profit
  • Figure 31. SAS (US) Revenue: by Geography 2023
  • Figure 32. McKesson (United States) Revenue, Net Income and Gross profit
  • Figure 33. McKesson (United States) Revenue: by Geography 2023
  • Figure 34. SCIO (United Stats) Revenue, Net Income and Gross profit
  • Figure 35. SCIO (United Stats) Revenue: by Geography 2023
  • Figure 36. Verscend (United States) Revenue, Net Income and Gross profit
  • Figure 37. Verscend (United States) Revenue: by Geography 2023
  • Figure 38. Global Healthcare Fraud Detection: by Type USD Million (2025-2030)
  • Figure 39. Global Healthcare Fraud Detection: by Application USD Million (2025-2030)
  • Figure 40. Global Healthcare Fraud Detection: by Components USD Million (2025-2030)
  • Figure 41. Global Healthcare Fraud Detection: by End Users USD Million (2025-2030)
  • Figure 42. Global Healthcare Fraud Detection: by Delivery Mode USD Million (2025-2030)
  • Figure 43. South America Healthcare Fraud Detection Share (%), by Country
  • Figure 44. Asia Pacific Healthcare Fraud Detection Share (%), by Country
  • Figure 45. Europe Healthcare Fraud Detection Share (%), by Country
  • Figure 46. MEA Healthcare Fraud Detection Share (%), by Country
  • Figure 47. North America Healthcare Fraud Detection Share (%), by Country
List of companies from research coverage that are profiled in the study
  • IBM (United States)
  • Optum (United States)
  • HCL Technologies Limited (India)
  • CGI Group Inc. (Canada)
  • RELX Group plc (United Kingdom)
  • Wipro Limited (India)
  • SAS (US)
  • McKesson (United States)
  • SCIO (United Stats)
  • Verscend (United States)
Additional players considered in the study are as follows:
ExlService Holdings Inc. (Ireland) , DXC (United States) , Northrop Grumman (United States) , LexisNexis (United States) , Pondera (United States)
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Key Highlights of Report


May 2024 244 Pages 70 Tables Base Year: 2023 Coverage: 15+ Companies; 18 Countries

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Global Healthcare Fraud Detection Quantitative research analysis
Global Healthcare Fraud Detection Qualitative Industry Analysis
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Frequently Asked Questions (FAQ):

The standard version of the report profiles players such as IBM (United States), Optum (United States), HCL Technologies Limited (India), CGI Group Inc. (Canada), RELX Group plc (United Kingdom), Wipro Limited (India), SAS (US), McKesson (United States), SCIO (United Stats) and Verscend (United States) etc.
The Study can be customized subject to feasibility and data availability. Please connect with our sales representative for further information.
"Increasing use of data mining and machine learning techniques to detect fraud." is seen as one of major influencing trends for Healthcare Fraud Detection Market during projected period 2023-2030.
The Healthcare Fraud Detection market study includes a random mix of players, including both market leaders and some top growing emerging players. Connect with our sales executive to get a complete company list in our research coverage.

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