Healthcare Fraud Analytics
Healthcare Fraud Analytics Market Forecasts to 2030 - Global Analysis By Solution Type (Predictive Analytics, Prescriptive Analytics, Descriptive Analytics and Other Solution Types), Deployment, Application, End User and By Geography
|
Years Covered |
2021-2030 |
|
Estimated Year Value (2023) |
US $2.3 BN |
|
Projected Year Value (2030) |
US $10.9 BN |
|
CAGR (2023 - 2030) |
24.7% |
|
Regions Covered |
North America, Europe, Asia Pacific, South America, and Middle East & Africa |
|
Countries Covered |
US, Canada, Mexico, Germany, UK, Italy, France, Spain, Japan, China, India, Australia, New Zealand, South Korea, Rest of Asia Pacific, South America, Argentina, Brazil, Chile, Middle East & Africa, Saudi Arabia, UAE, Qatar, and South Africa |
|
Largest Market |
Asia Pacific |
|
Highest Growing Market |
North America |
According to Stratistics MRC, the Global Healthcare Fraud Analytics Market is accounted for $2.3 billion in 2023 and is expected to reach $10.9 billion by 2030 growing at a CAGR of 24.7% during the forecast period. The term ""Healthcare Fraud Analytics Market"" describes the emerging segment of the healthcare business that uses cutting-edge technology and analytics to detect, prevent, and lessen fraudulent activity. Robust fraud detection procedures are becoming more and more necessary as the healthcare landscape grows more complicated and involves a growing amount of data generated from several sources, such as electronic health records, billing systems, and claims.
According to the OIG, Medicaid data is frequently incomplete and inaccurate, affecting the process of detecting fraudulent claims and resulting in the waste of billions of dollars due to FWA.
Market Dynamics:
Driver:
Increasing adoption of electronic health records
There are both potential and challenges when healthcare systems move to digital platforms and make enormous volumes of patient data available. The use of electronic health records (EHRs) makes it possible to create a more extensive and centralized database of medical records, which offers an opportunity for fraud. Additionally, in order to prevent this, healthcare institutions are using advanced analytics tools to closely examine electronic health data in order to search for irregularities and trends that may indicate fraud.
Restraint:
Complexity of integration
The integration of advanced fraud analytics systems into pre-existing healthcare infrastructures is a common implementation task that can be complex and time-consuming. The complexity is increased by different information formats, inconsistent standards among healthcare institutions, and compatibility problems with outdated systems. It is difficult to achieve seamless integration when dealing with institutions that have diverse IT systems, as it is necessary to ensure efficient data flow and real-time analysis. However, staff members used to traditional workflows may oppose healthcare providers and cause operational interruptions.
Opportunity:
Advancements in technology
The healthcare sector's ability to prevent fraud has been transformed by the ongoing development of analytical tools, machine learning algorithms, and artificial intelligence. These technological advancements process enormous volumes of healthcare data in real time, enabling more complex and effective fraud detection techniques. Advanced analytics improve the accuracy and speed of fraud detection by detecting complex patterns, anomalies, and suspicious measures. Moreover, by incorporating cutting-edge technologies, healthcare companies may minimize financial losses and maintain the integrity of their systems while staying ahead of ever more sophisticated fraud schemes.
Threat:
Data security and privacy concerns
Concerns regarding security breaches and privacy violations are raised by the management of enormous amounts of sensitive patient data, which is a concern for healthcare companies as they use advanced analytics to combat fraud in increasing numbers. Because the healthcare industry is heavily regulated, there is a significant risk of unauthorized access, data leaks, or cyberattacks. Achieving a complicated problem requires strict compliance with privacy rules such as HIPAA (Health Insurance Portability and Accountability Act) while also collecting important insights from patient data in an equitable manner.
Covid-19 Impact:
Fraud analytics solutions are more important than ever because of the growing pressure on healthcare systems throughout the world to allocate resources efficiently and prevent fraud. On the other hand, the epidemic has also caused disruptions in the healthcare system, diverting resources and rapid attention to remedies. The quick adoption of new healthcare services and the surge in transactions associated with COVID-19 have made fraud detection systems more challenging. Furthermore, the pandemic's economic effects could promote further false claims.
The predictive analytics segment is expected to be the largest during the forecast period
Predictive analytics segment is expected to be the largest during the forecast period. Predictive analytics analyzes prior information, identifies trends, and projects future fraudulent activity using sophisticated algorithms and machine learning models. Healthcare businesses can prevent financial losses and safeguard the integrity of healthcare systems by adopting a proactive approach and staying ahead of emerging fraud schemes. Furthermore, predictive analytics improves the effectiveness of fraud detection by analyzing large datasets in real time and increasing the accuracy of spotting suspicious behavior while reducing false positives.
The pharmacy billing issue segment is expected to have the highest CAGR during the forecast period
Pharmacy billing issue segment is expected to have the highest CAGR. Pharmacy billing problems, like overbilling, unbundling, or charging for fraudulent prescriptions, have emerged as major avenues for fraud in the healthcare industry. The need for specialist analytics solutions designed to identify anomalies and discrepancies in pharmacy billing data has increased due to the rise in these fraudulent activities. Real-time fraud analytics tools such as predictive modeling and machine learning algorithms are being used to examine pharmacy billing transactions.
Region with largest share:
Due to the region's rapid modernization and digital transformation, many of its nations have adopted electronic health records (EHRs) and other digital health technologies, the Asia-Pacific area accounted for the largest percentage. Healthcare payers and providers in Asia Pacific are investing in advanced analytics solutions as a result of rising healthcare costs and growing penalties associated with fraud. In addition, there is an apparent rise in regulatory actions in the Asia-Pacific area that are intended to improve accountability and transparency in healthcare systems.
Region with highest CAGR:
Because of the complex healthcare infrastructure and sophisticated reimbursement system, the North American region is better positioned to continue profitable expansion. Because of the growing financial damage that healthcare fraud causes, regulatory agencies have enacted extensive laws, such as the False Claims Act and the Health Insurance Portability and Accountability Act (HIPAA) in the United States, to prevent fraud in the healthcare industry. Moreover, the adoption of advanced analytics solutions is urged by these regulatory measures, which need more transparency, data protection, and fraud detection capabilities.
Key players in the market
Some of the key players in Healthcare Fraud Analytics market include Conduent Inc, Cotiviti Inc, DXC Technology, EXL Service Holdings Inc, HCL Technologies Limited, IBM, Optum Inc., OSP Labs, SAS Institute Inc and Wipro Limited.
Key Developments:
In November 2023, IBM launches new sustainability initiatives for global climate action. IBM’s operations span a broad spectrum of technological fields, from AI and cloud computing to cybersecurity and data analytics.
In July 2023, HCLTech, the third largest IT services company in India, has acquired a 100 per cent equity stake in German automotive engineering services provider ASAP Group for €251 million ($279.72 million).
Solution Types Covered:
• Predictive Analytics
• Prescriptive Analytics
• Descriptive Analytics
• Other Solution Types
Deployments Covered:
• Cloud-Based
• On-Premises
Applications Covered:
• Payment Integrity
• Pharmacy Billing Issue
• Insurance Claims Review
• Other Applications
End Users Covered:
• Third Party Service Providers
• Private Insurance Payers
• Public & Government Agencies
• Other End Users
Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2021, 2022, 2023, 2026, and 2030
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Table of Contents
1 Executive Summary
2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions
3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Application Analysis
3.7 End User Analysis
3.8 Emerging Markets
3.9 Impact of Covid-19
4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry
5 Global Healthcare Fraud Analytics Market, By Solution Type
5.1 Introduction
5.2 Predictive Analytics
5.3 Prescriptive Analytics
5.4 Descriptive Analytics
5.5 Other Solution Types
6 Global Healthcare Fraud Analytics Market, By Deployment
6.1 Introduction
6.2 Cloud-Based
6.3 On-Premises
7 Global Healthcare Fraud Analytics Market, By Application
7.1 Introduction
7.2 Payment Integrity
7.3 Pharmacy Billing Issue
7.4 Insurance Claims Review
7.4.1 Prepayment Review
7.4.2 Postpayment Review
7.5 Other Applications
8 Global Healthcare Fraud Analytics Market, By End User
8.1 Introduction
8.2 Third Party Service Providers
8.3 Private Insurance Payers
8.4 Public & Government Agencies
8.5 Other End Users
9 Global Healthcare Fraud Analytics Market, By Geography
9.1 Introduction
9.2 North America
9.2.1 US
9.2.2 Canada
9.2.3 Mexico
9.3 Europe
9.3.1 Germany
9.3.2 UK
9.3.3 Italy
9.3.4 France
9.3.5 Spain
9.3.6 Rest of Europe
9.4 Asia Pacific
9.4.1 Japan
9.4.2 China
9.4.3 India
9.4.4 Australia
9.4.5 New Zealand
9.4.6 South Korea
9.4.7 Rest of Asia Pacific
9.5 South America
9.5.1 Argentina
9.5.2 Brazil
9.5.3 Chile
9.5.4 Rest of South America
9.6 Middle East & Africa
9.6.1 Saudi Arabia
9.6.2 UAE
9.6.3 Qatar
9.6.4 South Africa
9.6.5 Rest of Middle East & Africa
10 Key Developments
10.1 Agreements, Partnerships, Collaborations and Joint Ventures
10.2 Acquisitions & Mergers
10.3 New Product Launch
10.4 Expansions
10.5 Other Key Strategies
11 Company Profiling
11.1 Conduent Inc
11.2 Cotiviti Inc
11.3 DXC Technology
11.4 EXL Service Holdings Inc
11.5 HCL Technologies Limited
11.6 IBM
11.7 Optum Inc.
11.8 OSP Labs
11.9 SAS Institute Inc
11.10 Wipro Limited
List of Tables
1 Global Healthcare Fraud Analytics Market Outlook, By Region (2021-2030) ($MN)
2 Global Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
3 Global Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
4 Global Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
5 Global Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
6 Global Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
7 Global Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
8 Global Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
9 Global Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
10 Global Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
11 Global Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
12 Global Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
13 Global Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
14 Global Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
15 Global Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
16 Global Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
17 Global Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
18 Global Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
19 Global Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
20 Global Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
21 Global Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
22 North America Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
23 North America Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
24 North America Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
25 North America Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
26 North America Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
27 North America Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
28 North America Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
29 North America Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
30 North America Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
31 North America Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
32 North America Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
33 North America Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
34 North America Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
35 North America Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
36 North America Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
37 North America Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
38 North America Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
39 North America Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
40 North America Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
41 North America Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
42 North America Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
43 Europe Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
44 Europe Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
45 Europe Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
46 Europe Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
47 Europe Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
48 Europe Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
49 Europe Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
50 Europe Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
51 Europe Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
52 Europe Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
53 Europe Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
54 Europe Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
55 Europe Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
56 Europe Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
57 Europe Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
58 Europe Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
59 Europe Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
60 Europe Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
61 Europe Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
62 Europe Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
63 Europe Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
64 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
65 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
66 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
67 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
68 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
69 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
70 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
71 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
72 Asia Pacific Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
73 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
74 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
75 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
76 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
77 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
78 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
79 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
80 Asia Pacific Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
81 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
82 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
83 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
84 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
85 South America Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
86 South America Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
87 South America Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
88 South America Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
89 South America Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
90 South America Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
91 South America Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
92 South America Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
93 South America Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
94 South America Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
95 South America Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
96 South America Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
97 South America Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
98 South America Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
99 South America Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
100 South America Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
101 South America Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
102 South America Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
103 South America Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
104 South America Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
105 South America Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
106 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
107 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
108 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
109 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
110 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
111 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
112 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
113 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
114 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
115 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
116 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
117 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
118 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
119 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
120 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
121 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
122 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
123 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
124 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
125 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
126 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
List of Figures
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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