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Healthcare Fraud Analytics Market - Global Outlook & Forecast 2023-2028

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  • 300 Pages
  • May 2023
  • Region: Global
  • Arizton
  • ID: 5806804
UP TO OFF until Jun 30th 2024
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The global healthcare fraud analytics market is expected to grow at a CAGR of 20.45% from 2022-2028.


Increasing Healthcare Fraudulent Activities

Healthcare fraud has been an ongoing problem in the healthcare industry for a long time. The increase in healthcare costs, the rise in technological advances, and a greater reliance on electronic data have all contributed to an increase in healthcare fraud. The healthcare fraud analytics market helps combat this issue by identifying fraudulent activities and helping organizations take proactive measures to prevent future fraud. Healthcare fraud analytics uses various analytic techniques to analyze large datasets and detect suspicious behavior patterns. These techniques can detect billing and coding errors, improper payments, and other forms of fraud. Healthcare fraud analytics also helps organizations identify trends in healthcare fraud and proactively address areas of risk. The increasing prevalence of healthcare fraud is driving the demand for healthcare fraud analytics solutions. Organizations increasingly invest in healthcare fraud analytics solutions to detect and prevent fraudulent activities and protect their financial and reputational interests.

The Increasing Number of Patients Benefiting From Healthcare Insurance

Healthcare fraud analytics uses data analytics and artificial intelligence to detect fraud and patterns in healthcare claims and other activities related to healthcare fraud. With the increasing number of patients receiving healthcare insurance, the potential amount of fraud increases, making it essential to have a reliable fraud detection system. The healthcare fraud analytics market helps to detect fraudulent activities such as billing for services not rendered and incorrect coding. With the increasing number of healthcare policies, fraudulent activities also increase, making identifying and preventing them difficult. Healthcare fraud analytics helps to identify these activities quickly and accurately, thus reducing the risk of fraud.

The Increasing Number of Pharmacy Claims-Related Frauds

With the growth of the healthcare industry, fraud & abuse have become increasingly serious problems. Fraudsters are taking advantage of the complexity of the healthcare system and the lack of oversight to commit fraud. As a result, healthcare organizations are facing increasing pressure to protect their finances from fraudulent activities. The healthcare fraud analytics market is growing as healthcare organizations begin recognizing the need for advanced analytics solutions to detect and prevent fraud. Healthcare analytics solutions are used to identify suspicious transactions and activities that could indicate fraudulent behavior. These solutions help detect and prevent fraud by providing insights into fraud patterns, allowing organizations to take corrective action.

Investment in ICT

Investment in ICT is a new opportunity for the healthcare fraud analytics market. ICT solutions such as Artificial Intelligence (AI) and Machine Learning (ML) can be used to detect and prevent fraud in the healthcare industry. By leveraging these technologies, healthcare organizations can develop and deploy predictive analytics models to detect suspicious transactions, identity theft, and other fraudulent activities. This can help organizations reduce the risk of fraud, save money, and improve operational efficiency.

Advanced Technologies Offer Greater Potential to Secure Against Fraud

Advanced technologies offer greater potential to secure against fraud, and this is a new opportunity for the healthcare fraud analytics market. With the increasing sophistication of fraud attempts, the need for advanced analytics tools to detect, prevent, and investigate fraud is becoming more important. Advanced analytics tools can help detect and prevent fraud more quickly and efficiently while providing more detailed insights into fraud patterns. This can help healthcare organizations identify potential areas of fraud and take steps to reduce the risk. In addition, advanced analytics can help healthcare organizations detect and investigate fraud more effectively, which can help reduce the financial losses associated with fraudulent activities.

AI in Healthcare Fraud Detection

AI in healthcare fraud detection is a new opportunity for the healthcare fraud analytics market. AI can detect and prevent fraud more quickly and accurately than traditional methods, reducing financial and administrative costs. AI can identify patterns in large amounts of data that would be impossible to find using manual methods and identify suspicious behavior that would be difficult to detect using traditional methods. AI can also help organizations identify and address fraud risk areas more quickly, as well as help them develop strategies to prevent future fraud from occurring.



The global healthcare fraud analytics market by solution type is segmented into descriptive, predictive, and prescriptive analytics. Descriptive analytics is a form of data analysis that seeks to summarize past events and identify patterns in data. It is a process that involves collecting, organizing, and analyzing data to gain insights that can be used to inform future decisions or strategies. This type of analytics is especially useful for businesses, as it can better understand customer behaviors, sales trends, and performance metrics. Descriptive fraud analytics is the process of analyzing data to detect patterns of fraud and other suspicious activities. It is a type of analytics that helps organizations identify and understand fraud-related activities and detect fraud before it occurs.

However, predictive analytics is expected to grow at a CAGR in the global healthcare fraud analytics market during the forecast period. Predictive analytics can also be used to detect trends in healthcare fraud. By analyzing the data from various sources, such as patient records, medical records, and healthcare billing systems, the predictive model can identify patterns of fraud that may not be easily visible.

Segmentation by Solution Type

  • Descriptive Analytics
  • Predictive Analytics
  • Prescriptive Analytics


The global healthcare fraud analytics market by delivery mode is segmented into on-premises and cloud-based. The on-premises segment dominated the market, accounting for over 52% share in 2022, and is anticipated to retain its dominance during the forecast period. On-premises service allows companies to verify customers and store data on their servers. No third party can access the customers’ data, the service provider, or vendors. This service ensures that their customer onboarding process is secure and the information collected stays safe from criminal activities. Several on-premises benefits also significantly contribute to why healthcare organizations are still hesitant to embrace the cloud. The biggest benefit to on-premises applications is that the IT department has full control over the data stored on them.

Segmentation by Delivery Mode

  • On-premises
  • Cloud-based


The medical provider fraud application segment holds the largest global healthcare fraud analytics market share. Medical provider fraud occurs when a health care provider, such as a doctor, nurse, or therapist, defrauds a medical insurance provider for reimbursement for services that were never provided or for services of a lower quality than what was promised. Both individuals and organizations can perpetrate this type of fraud, which can be difficult to detect due to the complexity of medical billing systems. The most common form of medical provider fraud is billing for never provided services. This may include billing for an office visit that never occurred or for procedures that were not done. This type of fraud can also occur when providers bill for services at a higher rate than what was performed or for more expensive treatments than what was actually given.

Segmentation by Application

  • Medical Provider Fraud
  • Patient Fraud
  • Prescription Fraud
  • General Healthcare Fraud


The global healthcare fraud analytics market by end-user is segmented into public health insurance companies, private health insurance companies, third-party service providers, and other end users. The public health insurance companies segment accounted for a major share in 2022. Public health insurance companies play an integral role in the health and well-being of individuals and the country. By providing financial coverage for medical costs and preventive care, these companies can help keep individuals healthy while reducing healthcare costs. Public health insurance companies have a key role in the global healthcare fraud analytics market. They are responsible for providing healthcare coverage to citizens and are the main funding source for healthcare services. With the rising healthcare costs and the prevalence of fraud and abuse, public health insurance companies must take a proactive approach to combat fraud and abuse. Public health insurance companies can help reduce fraud and abuse by using healthcare fraud analytics tools to identify suspicious activity and detect fraud.

Segmentation by End-user

  • Public Health Insurance Companies
  • Private Health Insurance Companies
  • Third-party Service Providers
  • Others


North America accounted for a major share of the global healthcare fraud analytics market in 2022, accounting for nearly 43%. The presence of a large patient population and better adoption of digital healthcare with the latest advancements in artificial intelligence (AI) is the primary factor for its high market share. The presence of key healthcare IT players is another reason for the high uptake of healthcare fraud analytics in North America. The use of healthcare fraud analytics is becoming increasingly common in the United States and Canada. In the United States, the Department of Health and Human Services (HHS) uses healthcare fraud analytics to identify fraud in Medicare and Medicaid.

Segmentation by Geography

  • North America
  • US
  • Canada
  • Europe
  • Germany
  • UK
  • France
  • Italy
  • Spain
  • APAC
  • China
  • Japan
  • South Korea
  • India
  • Australia
  • Latin America
  • Brazil
  • Mexico
  • Argentina
  • Middle East & Africa
  • Turkey
  • Saudi Arabia
  • South Africa


The global healthcare fraud analytics market is a rapidly growing industry since fraud and abuse in the healthcare system is an ongoing problem resulting in billions of dollars in losses to insurers and patients. The market is driven by rising healthcare costs, increasing consumer demand for transparency and accountability, and the need to reduce fraud and abuse. The global healthcare fraud analytics market is emerging, with global, regional, and local players recommending a broad range of conventional and latest-generation artificial intelligence (AI) technologies for end-users. The key vendors in the global healthcare fraud analytics market include IBM, LexisNexis Risk Solution, Optum, SAS Institute, Verisk Analytics, and Wipro, based on factors such as digital healthcare platforms, patient management, and clinical advancements. These companies have a broad geographic presence, diverse product portfolios, and a strong focus on product innovation, R&D, and business expansion activities.

Key Company Profiles

  • IBM
  • LexisNexis Risk Solutions
  • Optum
  • SAS Institute
  • Verisk Analytics
  • Wipro

Other Prominent Vendors

  • Alivia Analytics
  • CGI
  • Codoxo
  • Conduent
  • FraudLens
  • Healthcare Fraud Shield
  • Northrop Grumman Corporation
  • OSP
  • Qlarant
  • Qualetics Data Machines
  • Sharecare


  • How big is the global healthcare fraud analytics market?
  • What is the growth rate of the healthcare fraud analytics market?
  • What are the growing trends in the healthcare fraud analytics market?
  • Which region holds the most significant global healthcare fraud analytics market share?
  • Who are the key players in the global healthcare fraud analytics market?

Table of Contents

1 Research Methodology2 Research Objectives3 Research Process
4 Scope & Coverage
4.1 Market Definition
4.1.1 Inclusions
4.1.2 Exclusions
4.1.3 Market Estimation Caveats
4.2 Base Year
4.3 Scope of the Study
4.3.1 Market Segmentation by Solution Type
4.3.2 Market Segmentation by Delivery Mode
4.3.3 Market Segmentation by Application
4.3.4 Market Segmentation by End-User
4.3.5 Market Segmentation by Geography
5 Report Assumptions & Caveats
5.1 Key Caveats
5.2 Currency Conversion
5.3 Market Derivation
6 Market at a Glance
7 Premium Insights
7.1 Overview
8 Introduction
8.1 Overview
9 Market Opportunities & Trends
9.1 Investment in Information & Communication Technology (Ict)
9.2 Advanced Technologies Offer Great Potential to Secure Against Fraud
9.3 Ai in Healthcare Fraud Detection
10 Market Growth Enablers
10.1 Increasing Healthcare Fraudulent Activities
10.2 Increasing Number of Patients Benefiting from Healthcare Insurance
10.3 Rising Number of Pharmacy Claim-Related Frauds
11 Market Restraints
11.1 Change in Fraud Patterns
11.2 Security & Privacy Risks With Fraud Analytics Solutions
11.3 Time-Consuming Deployment and Need for Frequent Upgrades
12 Market Landscape
12.1 Market Overview
12.2 Market Size & Forecast
12.2.1 Geography Insights
12.2.2 Solution Type Insights
12.2.3 Delivery Mode Insights
12.2.4 Application Insights
12.2.5 End-User Insights
12.3 Five Forces Analysis
12.3.1 Threat of New Entrants
12.3.2 Bargaining Power of Suppliers
12.3.3 Bargaining Power of Buyers
12.3.4 Threat of Substitutes
12.3.5 Competitive Rivalry
13 Solution Type
13.1 Market Snapshot & Growth Engine
13.2 Market Overview
13.3 Descriptive Analytics
13.3.1 Market Overview
13.3.2 Market Size & Forecast
13.3.3 Market by Geography
13.4 Predictive Analytics
13.4.1 Market Overview
13.4.2 Market Size & Forecast
13.4.3 Market by Geography
13.5 Prescriptive Analytics
13.5.1 Market Overview
13.5.2 Market Size & Forecast
13.5.3 Market by Geography
14 Delivery Mode
14.1 Market Snapshot & Growth Engine
14.2 Market Overview
14.3 On-Premises
14.3.1 Market Overview
14.3.2 Market Size & Forecast
14.3.3 Market by Geography
14.4 Cloud-Based
14.4.1 Market Overview
14.4.2 Market Size & Forecast
14.4.3 Market by Geography
15 Application
15.1 Market Snapshot & Growth Engine
15.2 Market Overview
15.3 Medical Provider Fraud
15.3.1 Market Overview
15.3.2 Market Size & Forecast
15.3.3 Market by Geography
15.4 Patient Fraud
15.4.1 Market Overview
15.4.2 Market Size & Forecast
15.4.3 Market by Geography
15.5 Prescription Fraud
15.5.1 Market Overview
15.5.2 Market Size & Forecast
15.5.3 Market by Geography
15.6 General Healthcare Fraud
15.6.1 Market Overview
15.6.2 Market Size & Forecast
15.6.3 Market by Geography
16 End-User
16.1 Market Snapshot & Growth Engine
16.2 Market Overview
16.3 Public Health Insurance Companies
16.3.1 Market Overview
16.3.2 Market Size & Forecast
16.3.3 Market by Geography
16.4 Private Health Insurance Companies
16.4.1 Market Overview
16.4.2 Market Size & Forecast
16.4.3 Market by Geography
16.5 Third-Party Service Providers
16.5.1 Market Overview
16.5.2 Market Size & Forecast
16.5.3 Market by Geography
16.6 Others
16.6.1 Market Overview
16.6.2 Market Size & Forecast
16.6.3 Market by Geography
17 Geography
17.1 Market Snapshot & Growth Engine
17.2 Geographic Overview
18 North America
18.1 Market Overview
18.2 Market Size & Forecast
18.2.1 Market by Solution Type
18.2.2 Market by Delivery Mode
18.2.3 Market by Application
18.2.4 Market by End-User
18.3 Key Countries
18.3.1 Us: Market Size & Forecast
18.3.2 Canada: Market Size & Forecast
19 Europe
19.1 Market Overview
19.2 Market Size & Forecast
19.2.1 Market by Solution Type
19.2.2 Market by Delivery Mode
19.2.3 Market by Application
19.2.4 Market by End-User
19.3 Key Countries
19.3.1 Germany: Market Size & Forecast
19.3.2 Uk: Market Size & Forecast
19.3.3 France: Market Size & Forecast
19.3.4 Italy: Market Size & Forecast
19.3.5 Spain: Market Size & Forecast
20 Apac
20.1 Market Overview
20.2 Market Size & Forecast
20.2.1 Market by Solution Type
20.2.2 Market by Delivery Mode
20.2.3 Market by Application
20.2.4 Market by End-User
20.3 Key Countries
20.3.1 China: Market Size & Forecast
20.3.2 Japan: Market Size & Forecast
20.3.3 South Korea: Market Size & Forecast
20.3.4 India: Market Size & Forecast
20.3.5 Australia: Market Size & Forecast
21 Latin America
21.1 Market Overview
21.2 Market Size & Forecast
21.2.1 Market by Solution Type
21.2.2 Market by Delivery Mode
21.2.3 Market by Application
21.2.4 Market by End-User
21.3 Key Countries
21.3.1 Brazil: Market Size & Forecast
21.3.2 Mexico: Market Size & Forecast
21.3.3 Argentina: Market Size & Forecast
22 Middle East & Africa
22.1 Market Overview
22.2 Market Size & Forecast
22.2.1 Market by Solution Type
22.2.2 Market by Delivery Mode
22.2.3 Market by Application
22.2.4 Market by End-User
22.3 Key Countries
22.3.1 Turkey: Market Size & Forecast
22.3.2 Saudi Arabia: Market Size & Forecast
22.3.3 South Africa: Market Size & Forecast
23 Competitive Landscape
23.1 Competition Overview
23.2 Market Share Analysis
23.2.1 Ibm
23.2.2 Lexisnexis Risk Solutions
23.2.3 Optum
23.2.4 Sas Institute
23.2.5 Verisk Analytics
23.2.6 Wipro
24 Key Company Profiles
24.1 Ibm
24.1.1 Business Overview
24.1.2 Product Offerings
24.1.3 Key Strategies
24.1.4 Key Strengths
24.1.5 Key Opportunities
24.2 Lexisnexis Risk Solutions
24.2.1 Business Overview
24.2.2 Product Offerings
24.2.3 Key Strategies
24.2.4 Key Strengths
24.2.5 Key Opportunities
24.3 Optum
24.3.1 Business Overview
24.3.2 Product Offerings
24.3.3 Key Strategies
24.3.4 Key Strengths
24.3.5 Key Opportunities
24.4 Sas Institute
24.4.1 Business Overview
24.4.2 Product Offerings
24.4.3 Key Strategies
24.4.4 Key Strengths
24.4.5 Key Opportunities
24.5 Verisk Analytics
24.5.1 Business Overview
24.5.2 Product Offerings
24.5.3 Key Strategies
24.5.4 Key Strengths
24.5.5 Key Opportunities
24.6 Wipro
24.6.1 Business Overview
24.6.2 Product Offerings
24.6.3 Key Strategies
24.6.4 Key Strengths
24.6.5 Key Opportunities
25 Other Prominent Vendors
25.1 Alivia Analytics
25.1.1 Business Overview
25.1.2 Product Offerings
25.2 Cgi
25.2.1 Business Overview
25.2.2 Product Offerings
25.3 Codoxo
25.3.1 Business Overview
25.3.2 Product Offerings
25.4 Conduent
25.4.1 Business Overview
25.4.2 Product Offerings
25.5 Cotiviti
25.5.1 Business Overview
25.5.2 Product Offerings
25.6 Fraudlens
25.6.1 Business Overview
25.6.2 Product Offerings
25.7 Friss
25.7.1 Business Overview
25.7.2 Product Offerings
25.8 Healthcare Fraud Shield
25.8.1 Business Overview
25.8.2 Product Offerings
25.9 Northrop Grumman Corporation
25.9.1 Business Overview
25.9.2 Product Offerings
25.10 Osp
25.10.1 Business Overview
25.10.2 Product Offerings
25.11 Qlarant
25.11.1 Business Overview
25.11.2 Product Offerings
25.12 Qualetics Data Machines
25.12.1 Business Overview
25.12.2 Product Offerings
25.13 Sharecare
25.13.1 Business Overview
25.13.2 Product Offerings
26 Report Summary
26.1 Key Takeaways
26.2 Strategic Recommendations
27 Quantitative Summary
27.1 Market by Solution Type
27.1.1 North America: Market by Solution Type
27.1.2 Europe: Market by Solution Type
27.1.3 Apac: Market by Solution Type
27.1.4 Latin America: Market by Solution Type
27.1.5 Middle East & Africa: Market by Solution Type
27.2 Market by Delivery Mode
27.2.1 North America: Market by Delivery Mode
27.2.2 Europe: Market by Delivery Mode
27.2.3 Apac: Market by Delivery Mode
27.2.4 Latin America: Market by Delivery Mode
27.2.5 Middle East & Africa: Market by Delivery Mode
27.3 Market by Application
27.3.1 North America: Market by Application
27.3.2 Europe: Market by Application
27.3.3 Apac: Market by Application
27.3.4 Latin America: Market by Application
27.3.5 Middle East & Africa: Market by Application
27.4 Market by End-User
27.4.1 North America: Market by End-User
27.4.2 Europe: Market by End-User
27.4.3 Apac: Market by End-User
27.4.4 Latin America: Market by End-User
27.4.5 Middle East & Africa: Market by End-User
28 Appendix
28.1 Abbreviations

Companies Mentioned

  • IBM
  • LexisNexis Risk Solutions
  • Optum
  • SAS Institute
  • Verisk Analytics
  • Wipro
  • Alivia Analytics
  • CGI
  • Codoxo
  • Conduent
  • FraudLens
  • Healthcare Fraud Shield
  • Northrop Grumman Corporation
  • OSP
  • Qlarant
  • Qualetics Data Machines
  • Sharecare


Our research comprises a mix of primary and secondary research. The secondary research sources that are typically referred to include, but are not limited to, company websites, annual reports, financial reports, company pipeline charts, broker reports, investor presentations and SEC filings, journals and conferences, internal proprietary databases, news articles, press releases, and webcasts specific to the companies operating in any given market.

Primary research involves email interactions with the industry participants across major geographies. The participants who typically take part in such a process include, but are not limited to, CEOs, VPs, business development managers, market intelligence managers, and national sales managers. We primarily rely on internal research work and internal databases that we have populated over the years. We cross-verify our secondary research findings with the primary respondents participating in the study.



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