Summary

Global healthcare fraud analytics market is valued approximately at USD 1.2 billion in 2020 and is anticipated to grow with a healthy growth rate of about 26.7% over the forecast period 2021-2027. Healthcare fraud analytics is the fraud detection solutions and software that assist in early detection of frauds in healthcare sector such as errors in claim submissions, duplication of claims, prescription fraud by pharmacists and health insurance frauds. The global healthcare fraud analytics market is being driven by large number of fraudulent activities in healthcare and increased number of patients seeking health insurance. Furthermore, role of AI in healthcare fraud detection will provide new opportunities for the global healthcare fraud analytics industry. There has been a significant rise in the population seeking health insurance in different countries across the globe. For instance, as per Statista, 297 million people in the United States had health insurance, as of 2020, an increase from approximately 257 million health insured people in 2010. Also, health insurance sector market size in India was about USD 4.94 billion in 2018 which is expected to reach USD 26.72 billion by 2030. Such growth in the demand for health insurance is expected to increase the demand and adoption of healthcare fraud analytics which is likely to promote the marker growth. However, limitations in the data capturing process in Medicaid services may impede market growth over the forecast period of 2021-2027.

The regional analysis of the global healthcare fraud analytics market is considered for the key regions such as Asia Pacific, North America, Europe, Latin America, and Rest of the World. North America accounts for the largest share in terms of market revenue in the global healthcare fraud analytics market over the forecast period 2021-2027. Factors such as growing incidences of healthcare fraud, large number of people seeking health insurance, pressure to reduce healthcare costs, favorable government anti-fraud initiatives, greater service and product availability, technological advancements, etc. contribute towards the largest market share of the region in the forecast years.

Major market player included in this report are:
International Business Machines Corporation (IBM)
Optum, Inc. (Optum)
SAS Institute, Inc. (SAS)
Change Healthcare
EXL Service Holdings, Inc.
Cotiviti
Wipro Limited
Conduent, Inc.
Hindustan Computers Limited Technologies Limited (HCL)
Canadian Global Information Technology Group Inc. (CGI)

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Solution Type:
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
By Delivery Model:
On-premise
Cloud-based
By Application:
Insurance Claims Review
Pharmacy Billing Misuse
Payment Integrity
Other applications
By End User:
Public & Government Agencies
Private Insurance Payers
Third-party service providers
Employers
By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2018, 2019
Base year – 2020
Forecast period – 2021 to 2027.

Target Audience of the Global Healthcare Fraud Analytics Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers
Investors

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Table of Contents

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2027 (USD Billion)
1.2.1. Healthcare Fraud Analytics Market, by Region, 2019-2027 (USD Billion)
1.2.2. Healthcare Fraud Analytics Market, by Solution Type, 2019-2027 (USD Billion)
1.2.3. Healthcare Fraud Analytics Market, by Delivery Model, 2019-2027 (USD Billion)
1.2.4. Healthcare Fraud Analytics Market, by Application, 2019-2027 (USD Billion)
1.2.5. Healthcare Fraud Analytics Market, by End User, 2019-2027 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Healthcare Fraud Analytics Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Healthcare Fraud Analytics Market Dynamics
3.1. Healthcare Fraud Analytics Market Impact Analysis (2019-2027)
3.1.1. Market Drivers
3.1.1.1. Large number of fraudulent activities in healthcare
3.1.1.2. Increased number of patients seeking health insurance
3.1.2. Market Restraint
3.1.2.1. Limitations in the data capturing process in Medicaid services
3.1.3. Market Opportunities
3.1.3.1. Role of AI in healthcare fraud detection
Chapter 4. Global Healthcare Fraud Analytics Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2027)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
Chapter 5. Global Healthcare Fraud Analytics Market, by Solution Type
a. Market Snapshot
5.1. Global Healthcare Fraud Analytics Market by Solution Type, Performance - Potential Analysis
5.2. Global Healthcare Fraud Analytics Market Estimates & Forecasts by Solution Type 2018-2027 (USD Billion)
5.3. Healthcare Fraud Analytics Market, Sub Segment Analysis
5.3.1. Descriptive Analytics
5.3.2. Predictive Analytics
5.3.3. Prescriptive Analytics
Chapter 6. Global Healthcare Fraud Analytics Market, by Delivery Model
b. Market Snapshot
6.1. Global Healthcare Fraud Analytics Market by Delivery Model, Performance - Potential Analysis
6.2. Global Healthcare Fraud Analytics Market Estimates & Forecasts by Delivery Model 2018-2027 (USD Billion)
6.3. Healthcare Fraud Analytics Market, Sub Segment Analysis
6.3.1. On-premise
6.3.2. Cloud-based
Chapter 7. Global Healthcare Fraud Analytics Market, by Application
c. Market Snapshot
7.1. Global Healthcare Fraud Analytics Market by Application, Performance - Potential Analysis
7.2. Global Healthcare Fraud Analytics Market Estimates & Forecasts by Application 2018-2027 (USD Billion)
7.3. Healthcare Fraud Analytics Market, Sub Segment Analysis
7.3.1. Insurance Claims Review
7.3.2. Pharmacy Billing Misuse
7.3.3. Payment Integrity
7.3.4. Other applications
Chapter 8. Global Healthcare Fraud Analytics Market, by End User
d. Market Snapshot
8.1. Global Healthcare Fraud Analytics Market by End User, Performance - Potential Analysis
8.2. Global Healthcare Fraud Analytics Market Estimates & Forecasts by End User 2018-2027 (USD Billion)
8.3. Healthcare Fraud Analytics Market, Sub Segment Analysis
8.3.1. Public & Government Agencies
8.3.2. Private Insurance Payers
8.3.3. Third-party service providers
8.3.4. Employers
Chapter 9. Global Healthcare Fraud Analytics Market, Regional Analysis
9.1. Healthcare Fraud Analytics Market, Regional Market Snapshot
9.2. North America Healthcare Fraud Analytics Market
9.2.1. U.S. Healthcare Fraud Analytics Market
9.2.1.1. Solution Type breakdown estimates & forecasts, 2018-2027
9.2.1.2. Delivery Model breakdown estimates & forecasts, 2018-2027
9.2.1.3. Application breakdown estimates & forecasts, 2018-2027
9.2.1.4. End User breakdown estimates & forecasts, 2018-2027
9.2.2. Canada Healthcare Fraud Analytics Market
9.3. Europe Healthcare Fraud Analytics Market Snapshot
9.3.1. U.K. Healthcare Fraud Analytics Market
9.3.2. Germany Healthcare Fraud Analytics Market
9.3.3. France Healthcare Fraud Analytics Market
9.3.4. Spain Healthcare Fraud Analytics Market
9.3.5. Italy Healthcare Fraud Analytics Market
9.3.6. Rest of Europe Healthcare Fraud Analytics Market
9.4. Asia-Pacific Healthcare Fraud Analytics Market Snapshot
9.4.1. China Healthcare Fraud Analytics Market
9.4.2. India Healthcare Fraud Analytics Market
9.4.3. Japan Healthcare Fraud Analytics Market
9.4.4. Australia Healthcare Fraud Analytics Market
9.4.5. South Korea Healthcare Fraud Analytics Market
9.4.6. Rest of Asia Pacific Healthcare Fraud Analytics Market
9.5. Latin America Healthcare Fraud Analytics Market Snapshot
9.5.1. Brazil Healthcare Fraud Analytics Market
9.5.2. Mexico Healthcare Fraud Analytics Market
9.6. Rest of The World Healthcare Fraud Analytics Market
Chapter 10. Competitive Intelligence
10.1. Top Market Strategies
10.2. Company Profiles
10.2.1. International Business Machines Corporation (IBM)
10.2.1.1. Key Information
10.2.1.2. Overview
10.2.1.3. Financial (Subject to Data Availability)
10.2.1.4. Product Summary
10.2.1.5. Recent Developments
10.2.2. Optum, Inc. (Optum)
10.2.3. SAS Institute, Inc. (SAS)
10.2.4. Change Healthcare
10.2.5. EXL Service Holdings, Inc.
10.2.6. Cotiviti
10.2.7. Wipro Limited
10.2.8. Conduent, Inc.
10.2.9. Hindustan Computers Limited Technologies Limited (HCL)
10.2.10. Canadian Global Information Technology Group Inc. (CGI)
Chapter 11. Research Process
11.1. Research Process
11.1.1. Data Mining
11.1.2. Analysis
11.1.3. Market Estimation
11.1.4. Validation
11.1.5. Publishing
11.2. Research Attributes
11.3. Research Assumption