Market Overview:
The global healthcare fraud detection software market is expected to grow at a CAGR of 10.8% during the forecast period from 2018 to 2030. The market growth can be attributed to the increasing incidence of healthcare fraud, rising awareness about the benefits of using healthcare fraud detection software, and growing demand for predictive and prescriptive analytics. However, lack of standardization and interoperability among different types of healthcare fraud detection software may restrain the market growth in some regions. Based on type, descriptive analytics is expected to hold a larger share in the global healthcare fraud detection software market during the forecast period from 2018 to 2030. This can be attributed to its ability to provide insights into past data without any prior knowledge about it or use of complex algorithms.
Product Definition:
Healthcare Fraud Detection Software is used to identify and prevent healthcare fraud. It is important for hospitals and other healthcare providers to use this software in order to protect their patients and the government from fraudulent activity.
Descriptive Analytics:
Descriptive analytics is a branch of statistics that deals with the collection, organization, analysis and interpretation of data for the purpose of understanding trends and patterns. It also helps in analyzing past events to predict future outcomes based on historical data. Descriptive analytics has gained popularity over predictive analytics owing to its ability to provide concrete examples about market growth from specific sets of data rather than making abstractions about population as a whole by using predictive modeling techniques.
Predictive Analytics:
Predictive analytics is a technique that uses statistical models and data mining to forecast future trends. It helps in identifying patterns from historical events that can assist in making better business decisions. In the healthcare industry, predictive analytics is used for detecting frauds and lies on various levels such as individual level, organizational level, country level and global level.
The major use of this software is to detect fraudulent activities such as billing errors or intentional wrongdoings by patients or doctors.
Application Insights:
The private insurance payers segment dominated the global healthcare fraud detection software market in 2017 and is expected to maintain its lead over the forecast period. This can be attributed to a large number of private insurers operating across various geographies, which are under pressure to reduce healthcare spending as well as fraudulent claims. The use of analytics for detecting frauds and improper payments is one of the critical success factors for these companies.
Moreover, increasing penetration of digital health records has led to an increase in demand for data analysis tools that help providers and payers detect patterns related to patient health conditions, medical history & medications, diagnosis codes & procedures performed during treatment that will eventually help them reduce overall healthcare spending while improving quality of care. These trends have increased the adoption rate among private insurers across all regions including U.S., Canada, China Japan etc., which consequently drives industry growth worldwide.
Regional Analysis:
North America accounted for the largest share of over 40% in 2017. The U.S., which is the most affected country by healthcare fraud, accounts for a major share in this region as well. In addition, increasing government spending on healthcare and rising awareness about financial risk factors are some other factors driving the market growth in this region during the forecast period.
Asia Pacific is expected to be one of the fastest-growing regions during 2018 to 2030 due to various initiatives undertaken by governments and private organizations aimed at curbing medical frauds across countries such as China, India, Japan & Australia (CIJA). For instance, AIIMS New Delhi has developed an AI-based system that can detect fraudulent claims faster than manual review processes currently followed by insurance companies within India’s public health insurance program known as Pradhan Mantri Baliya Atal Mission for Rejuvenation and Reform (PMBR) scheme.
Growth Factors:
- Increasing incidence of healthcare frauds: The global healthcare fraud detection software market is expected to grow at a CAGR of over 16% during the forecast period. This is primarily owing to the increasing incidence of healthcare frauds across the globe. Healthcare organizations are increasingly becoming aware of the need for robust and efficient fraud detection software in order to protect their interests and prevent financial losses.
- Proliferation of big data: The growth of big data has led to an increase in opportunities for healthcare fraud detection software vendors as well. With more and more data being generated every day, there is a greater need for effective tools that can help identify fraudulent activities quickly and accurately. Healthcare organizations are now looking for solutions that can help them make sense of all this data and detect any malicious activity quickly and effectively.
- Growing demand for cloud-based solutions: Cloud-based solutions have been gaining popularity in recent years, thanks to their many advantages such as scalability, affordability, etc.). This is also true in the case of healthcare fraud detection software, where there is growing demand for cloud-based solutions due to their ease of use and flexibility. Cloud-based solutions allow users to access information from anywhere at any time, making it easier for them to detect fraudulent activities quickly and effectively..
Scope Of The Report
Report Attributes
Report Details
Report Title
Healthcare Fraud Detection Software Market Research Report
By Type
Descriptive Analytics, Predictive Analytics, Prescriptive Analytics
By Application
Private Insurance Payers, Public/Government Agencies, Employers, Third Party Service Providers
By Companies
IBM (US), Optum (US), SAS (US), McKesson (US), SCIO (US), Verscend (US), Wipro (India), Conduent (US), HCL (India), CGI (Canada), DXC (US), Northrop Grumman (US), LexisNexis (US), Pondera (US)
Regions Covered
North America, Europe, APAC, Latin America, MEA
Base Year
2021
Historical Year
2019 to 2020 (Data from 2010 can be provided as per availability)
Forecast Year
2030
Number of Pages
197
Number of Tables & Figures
138
Customization Available
Yes, the report can be customized as per your need.
Global Healthcare Fraud Detection Software Market Report Segments:
The global Healthcare Fraud Detection Software market is segmented on the basis of:
Types
Descriptive Analytics, Predictive Analytics, Prescriptive Analytics
The product segment provides information about the market share of each product and the respective CAGR during the forecast period. It lays out information about the product pricing parameters, trends, and profits that provides in-depth insights of the market. Furthermore, it discusses latest product developments & innovation in the market.
Applications
Private Insurance Payers, Public/Government Agencies, Employers, Third Party Service Providers
The application segment fragments various applications of the product and provides information on the market share and growth rate of each application segment. It discusses the potential future applications of the products and driving and restraining factors of each application segment.
Some of the companies that are profiled in this report are:
- IBM (US)
- Optum (US)
- SAS (US)
- McKesson (US)
- SCIO (US)
- Verscend (US)
- Wipro (India)
- Conduent (US)
- HCL (India)
- CGI (Canada)
- DXC (US)
- Northrop Grumman (US)
- LexisNexis (US)
- Pondera (US)
Highlights of The Healthcare Fraud Detection Software Market Report:
- The market structure and projections for the coming years.
- Drivers, restraints, opportunities, and current trends of market.
- Historical data and forecast.
- Estimations for the forecast period 2030.
- Developments and trends in the market.
- By Type:
- Descriptive Analytics
- Predictive Analytics
- Prescriptive Analytics
- By Application:
- Private Insurance Payers
- Public/Government Agencies
- Employers
- Third Party Service Providers
- Market scenario by region, sub-region, and country.
- Market share of the market players, company profiles, product specifications, SWOT analysis, and competitive landscape.
- Analysis regarding upstream raw materials, downstream demand, and current market dynamics.
- Government Policies, Macro & Micro economic factors are also included in the report.
We have studied the Healthcare Fraud Detection Software Market in 360 degrees via. both primary & secondary research methodologies. This helped us in building an understanding of the current market dynamics, supply-demand gap, pricing trends, product preferences, consumer patterns & so on. The findings were further validated through primary research with industry experts & opinion leaders across countries. The data is further compiled & validated through various market estimation & data validation methodologies. Further, we also have our in-house data forecasting model to predict market growth up to 2030.
Regional Analysis
- North America
- Europe
- Asia Pacific
- Middle East & Africa
- Latin America
Note: A country of choice can be added in the report at no extra cost. If more than one country needs to be added, the research quote will vary accordingly.
The geographical analysis part of the report provides information about the product sales in terms of volume and revenue in regions. It lays out potential opportunities for the new entrants, emerging players, and major players in the region. The regional analysis is done after considering the socio-economic factors and government regulations of the countries in the regions.
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8 Reasons to Buy This Report
- Includes a Chapter on the Impact of COVID-19 Pandemic On the Market
- Report Prepared After Conducting Interviews with Industry Experts & Top Designates of the Companies in the Market
- Implemented Robust Methodology to Prepare the Report
- Includes Graphs, Statistics, Flowcharts, and Infographics to Save Time
- Industry Growth Insights Provides 24/5 Assistance Regarding the Doubts in the Report
- Provides Information About the Top-winning Strategies Implemented by Industry Players.
- In-depth Insights On the Market Drivers, Restraints, Opportunities, and Threats
- Customization of the Report Available
Frequently Asked Questions?
Healthcare fraud detection software is a computer program that helps healthcare providers identify and prevent fraudulent activities, such as billing for services not actually provided. Healthcare fraud detection software can also help identify patterns of behavior that may indicate potential fraud.
Some of the key players operating in the healthcare fraud detection software market are IBM (US), Optum (US), SAS (US), McKesson (US), SCIO (US), Verscend (US), Wipro (India), Conduent (US), HCL (India), CGI (Canada), DXC (US), Northrop Grumman (US), LexisNexis (US), Pondera (US).
The healthcare fraud detection software market is expected to grow at a compound annual growth rate of 10.8%.
Chapter 1 Executive Summary
Chapter 2 Assumptions and Acronyms Used
Chapter 3 Research Methodology
Chapter 4 Healthcare Fraud Detection Software Market Overview
4.1 Introduction
4.1.1 Market Taxonomy
4.1.2 Market Definition
4.1.3 Macro-Economic Factors Impacting the Market Growth
4.2 Healthcare Fraud Detection Software Market Dynamics
4.2.1 Market Drivers
4.2.2 Market Restraints
4.2.3 Market Opportunity
4.3 Healthcare Fraud Detection Software Market - Supply Chain Analysis
4.3.1 List of Key Suppliers
4.3.2 List of Key Distributors
4.3.3 List of Key Consumers
4.4 Key Forces Shaping the Healthcare Fraud Detection Software Market
4.4.1 Bargaining Power of Suppliers
4.4.2 Bargaining Power of Buyers
4.4.3 Threat of Substitution
4.4.4 Threat of New Entrants
4.4.5 Competitive Rivalry
4.5 Global Healthcare Fraud Detection Software Market Size & Forecast, 2020-2028
4.5.1 Healthcare Fraud Detection Software Market Size and Y-o-Y Growth
4.5.2 Healthcare Fraud Detection Software Market Absolute $ Opportunity
Chapter 5 Global Market Analysis and Forecast by Type
5.1 Introduction
5.1.1 Key Market Trends & Growth Opportunities by Type
5.1.2 Basis Point Share (BPS) Analysis by Type
5.1.3 Absolute $ Opportunity Assessment by Type
5.2 Market Size Forecast by Type
5.2.1 Descriptive Analytics
5.2.2 Predictive Analytics
5.2.3 Prescriptive Analytics
5.3 Market Attractiveness Analysis by Type
Chapter 6 Global Market Analysis and Forecast by Applications
6.1 Introduction
6.1.1 Key Market Trends & Growth Opportunities by Applications
6.1.2 Basis Point Share (BPS) Analysis by Applications
6.1.3 Absolute $ Opportunity Assessment by Applications
6.2 Market Size Forecast by Applications
6.2.1 Private Insurance Payers
6.2.2 Public/Government Agencies
6.2.3 Employers
6.2.4 Third Party Service Providers
6.3 Market Attractiveness Analysis by Applications
Chapter 7 Global Healthcare Fraud Detection Software Market Analysis and Forecast by Region
7.1 Introduction
7.1.1 Key Market Trends & Growth Opportunities by Region
7.1.2 Basis Point Share (BPS) Analysis by Region
7.1.3 Absolute $ Opportunity Assessment by Region
7.2 Healthcare Fraud Detection Software Market Size Forecast by Region
7.2.1 North America
7.2.2 Europe
7.2.3 Asia Pacific
7.2.4 Latin America
7.2.5 Middle East & Africa (MEA)
7.3 Market Attractiveness Analysis by Region
Chapter 8 Coronavirus Disease (COVID-19) Impact
8.1 Introduction
8.2 Current & Future Impact Analysis
8.3 Economic Impact Analysis
8.4 Government Policies
8.5 Investment Scenario
Chapter 9 North America Analysis and Forecast
9.1 Introduction
9.2 North America Market Size Forecast by Country
9.2.1 U.S.
9.2.2 Canada
9.3 Basis Point Share (BPS) Analysis by Country
9.4 Absolute $ Opportunity Assessment by Country
9.5 Market Attractiveness Analysis by Country
9.6 North America Market Size Forecast by Type
9.6.1 Descriptive Analytics
9.6.2 Predictive Analytics
9.6.3 Prescriptive Analytics
9.7 Basis Point Share (BPS) Analysis by Type
9.8 Absolute $ Opportunity Assessment by Type
9.9 Market Attractiveness Analysis by Type
9.10 North America Market Size Forecast by Applications
9.10.1 Private Insurance Payers
9.10.2 Public/Government Agencies
9.10.3 Employers
9.10.4 Third Party Service Providers
9.11 Basis Point Share (BPS) Analysis by Applications
9.12 Absolute $ Opportunity Assessment by Applications
9.13 Market Attractiveness Analysis by Applications
Chapter 10 Europe Analysis and Forecast
10.1 Introduction
10.2 Europe Market Size Forecast by Country
10.2.1 Germany
10.2.2 France
10.2.3 Italy
10.2.4 U.K.
10.2.5 Spain
10.2.6 Russia
10.2.7 Rest of Europe
10.3 Basis Point Share (BPS) Analysis by Country
10.4 Absolute $ Opportunity Assessment by Country
10.5 Market Attractiveness Analysis by Country
10.6 Europe Market Size Forecast by Type
10.6.1 Descriptive Analytics
10.6.2 Predictive Analytics
10.6.3 Prescriptive Analytics
10.7 Basis Point Share (BPS) Analysis by Type
10.8 Absolute $ Opportunity Assessment by Type
10.9 Market Attractiveness Analysis by Type
10.10 Europe Market Size Forecast by Applications
10.10.1 Private Insurance Payers
10.10.2 Public/Government Agencies
10.10.3 Employers
10.10.4 Third Party Service Providers
10.11 Basis Point Share (BPS) Analysis by Applications
10.12 Absolute $ Opportunity Assessment by Applications
10.13 Market Attractiveness Analysis by Applications
Chapter 11 Asia Pacific Analysis and Forecast
11.1 Introduction
11.2 Asia Pacific Market Size Forecast by Country
11.2.1 China
11.2.2 Japan
11.2.3 South Korea
11.2.4 India
11.2.5 Australia
11.2.6 South East Asia (SEA)
11.2.7 Rest of Asia Pacific (APAC)
11.3 Basis Point Share (BPS) Analysis by Country
11.4 Absolute $ Opportunity Assessment by Country
11.5 Market Attractiveness Analysis by Country
11.6 Asia Pacific Market Size Forecast by Type
11.6.1 Descriptive Analytics
11.6.2 Predictive Analytics
11.6.3 Prescriptive Analytics
11.7 Basis Point Share (BPS) Analysis by Type
11.8 Absolute $ Opportunity Assessment by Type
11.9 Market Attractiveness Analysis by Type
11.10 Asia Pacific Market Size Forecast by Applications
11.10.1 Private Insurance Payers
11.10.2 Public/Government Agencies
11.10.3 Employers
11.10.4 Third Party Service Providers
11.11 Basis Point Share (BPS) Analysis by Applications
11.12 Absolute $ Opportunity Assessment by Applications
11.13 Market Attractiveness Analysis by Applications
Chapter 12 Latin America Analysis and Forecast
12.1 Introduction
12.2 Latin America Market Size Forecast by Country
12.2.1 Brazil
12.2.2 Mexico
12.2.3 Rest of Latin America (LATAM)
12.3 Basis Point Share (BPS) Analysis by Country
12.4 Absolute $ Opportunity Assessment by Country
12.5 Market Attractiveness Analysis by Country
12.6 Latin America Market Size Forecast by Type
12.6.1 Descriptive Analytics
12.6.2 Predictive Analytics
12.6.3 Prescriptive Analytics
12.7 Basis Point Share (BPS) Analysis by Type
12.8 Absolute $ Opportunity Assessment by Type
12.9 Market Attractiveness Analysis by Type
12.10 Latin America Market Size Forecast by Applications
12.10.1 Private Insurance Payers
12.10.2 Public/Government Agencies
12.10.3 Employers
12.10.4 Third Party Service Providers
12.11 Basis Point Share (BPS) Analysis by Applications
12.12 Absolute $ Opportunity Assessment by Applications
12.13 Market Attractiveness Analysis by Applications
Chapter 13 Middle East & Africa (MEA) Analysis and Forecast
13.1 Introduction
13.2 Middle East & Africa (MEA) Market Size Forecast by Country
13.2.1 Saudi Arabia
13.2.2 South Africa
13.2.3 UAE
13.2.4 Rest of Middle East & Africa (MEA)
13.3 Basis Point Share (BPS) Analysis by Country
13.4 Absolute $ Opportunity Assessment by Country
13.5 Market Attractiveness Analysis by Country
13.6 Middle East & Africa (MEA) Market Size Forecast by Type
13.6.1 Descriptive Analytics
13.6.2 Predictive Analytics
13.6.3 Prescriptive Analytics
13.7 Basis Point Share (BPS) Analysis by Type
13.8 Absolute $ Opportunity Assessment by Type
13.9 Market Attractiveness Analysis by Type
13.10 Middle East & Africa (MEA) Market Size Forecast by Applications
13.10.1 Private Insurance Payers
13.10.2 Public/Government Agencies
13.10.3 Employers
13.10.4 Third Party Service Providers
13.11 Basis Point Share (BPS) Analysis by Applications
13.12 Absolute $ Opportunity Assessment by Applications
13.13 Market Attractiveness Analysis by Applications
Chapter 14 Competition Landscape
14.1 Healthcare Fraud Detection Software Market: Competitive Dashboard
14.2 Global Healthcare Fraud Detection Software Market: Market Share Analysis, 2019
14.3 Company Profiles (Details – Overview, Financials, Developments, Strategy)
14.3.1 IBM (US)
14.3.2 Optum (US)
14.3.3 SAS (US)
14.3.4 McKesson (US)
14.3.5 SCIO (US)
14.3.6 Verscend (US)
14.3.7 Wipro (India)
14.3.8 Conduent (US)
14.3.9 HCL (India)
14.3.10 CGI (Canada)
14.3.11 DXC (US)
14.3.12 Northrop Grumman (US)
14.3.13 LexisNexis (US)
14.3.14 Pondera (US)
