Report Content
Chapter 1 Methodology & Scope
1.1 Market scope & definitions
1.2 Research design
1.2.1 Research approach
1.2.2 Data collection methods
1.3 Base estimates & calculations
1.3.1 Base year calculation
1.3.2 Key trends for market estimation
1.4 Forecast model
1.5 Primary research and validation
1.5.1 Primary sources
1.5.2 Data mining sources
Chapter 2 Executive Summary
2.1 Industry 3600 synopsis
Chapter 3 Industry Insights
3.1 Industry ecosystem analysis
3.2 Industry impact forces
3.2.1 Growth drivers
3.2.1.1 Rising incidence of healthcare fraud
3.2.1.2 Growing awareness and focus on fraud prevention
3.2.1.3 Technological advancements
3.2.2 Industry pitfalls & challenges
3.2.2.1 High implementation costs
3.2.2.2 Lack of skilled professionals
3.3 Growth potential analysis
3.4 Regulatory landscape
3.5 Reimbursement scenario
3.6 Porter's analysis
3.7 PESTEL analysis
3.8 Policy outlook
3.9 Gap analysis
3.10 Future market trends
Chapter 4 Competitive Landscape, 2023
4.1 Introduction
4.2 Company matrix analysis
4.3 Competitive analysis of major market players
4.4 Competitive positioning matrix
4.5 Strategy dashboard
Chapter 5 Market Estimates and Forecast, By Solution Type, 2021 – 2032 ($ Mn)
5.1 Key trends
5.2 Descriptive analytics
5.3 Prescriptive analytics
5.4 Predictive analytics
Chapter 6 Market Estimates and Forecast, By Deployment Mode, 2021 – 2032 ($ Mn)
6.1 Key trends
6.2 On-premises
6.3 Cloud-based
Chapter 7 Market Estimates and Forecast, By Application, 2021 – 2032 ($ Mn)
7.1 Key trends
7.2 Insurance claims review
7.2.1 Postpayment review
7.2.2 Prepayment review
7.3 Pharmacy billing issue
7.4 Payment integrity
7.5 Other applications
Chapter 8 Market Estimates and Forecast, By End-Use, 2021 – 2032 ($ Mn)
8.1 Key trends
8.2 Healthcare providers
8.3 Insurance companies
8.4 Government organizations
8.5 Other end-users
Chapter 9 Market Estimates and Forecast, By Region, 2021 – 2032 ($ Mn)
9.1 Key trends
9.2 North America
9.2.1 U.S.
9.2.2 Canada
9.3 Europe
9.3.1 Germany
9.3.2 UK
9.3.3 France
9.3.4 Spain
9.3.5 Italy
9.3.6 Netherlands
9.3.7 Rest of Europe
9.4 Asia Pacific
9.4.1 China
9.4.2 Japan
9.4.3 India
9.4.4 Australia
9.4.5 South Korea
9.4.6 Rest of Asia Pacific
9.5 Latin America
9.5.1 Brazil
9.5.2 Mexico
9.5.3 Argentina
9.5.4 Rest of Latin America
9.6 Middle East and Africa
9.6.1 South Africa
9.6.2 Saudi Arabia
9.6.3 UAE
9.6.4 Rest of Middle East and Africa
Chapter 10 Company Profiles
10.1 CGI Inc.
10.2 Change Healthcare
10.3 Conduent Incorporated
10.4 Cotiviti, Inc.
10.5 DXC Technology Company
10.6 EPIC
10.7 ExlService Holdings, Inc.
10.8 Fair Isaac Corporation
10.9 HCL Technologies Limited
10.10 IBM Corporation
10.11 LexisNexis Risk Solutions.
10.12 Optum Inc.
10.13 Qlarant Commercial Solutions, Inc.
10.14 SAS Institute Inc.
10.15 WIPRO LIMITED
Healthcare Fraud Analytics Market Size
Healthcare Fraud Analytics Market size was valued at USD 2.3 billion in 2023 and is expected to exhibit growth at a CAGR of 24.1% from 2024 and 2032. High market growth can be attributed to the ongoing advancements in data analytics, rising incidence of healthcare fraud, increased healthcare spending and complexity, and increasing adoption of digital health solutions, among other contributing factors.
Moreover, the growing incidence of healthcare fraud, including fraudulent claims, billing schemes, identity theft, and prescription fraud, is a significant driver for the market. For instance, according to data from the U.S. Sentencing Commission, in 2022, there were 431 healthcare fraud offenders, representing 8.4% of all theft, property destruction, and fraud offenses. This marks a 1.4% increase in healthcare fraud offenders since 2018. Fraudulent activities result in substantial financial losses for healthcare providers, insurers, and governments, creating a strong demand for analytics solutions that can detect and prevent fraud.
Furthermore, as healthcare spending continues to rise, so does the complexity of healthcare systems and transactions. This complexity provides more opportunities for fraudulent activities to occur. Healthcare fraud analytics tools help manage this complexity by analyzing large volumes of data and identifying suspicious patterns or anomalies.
Healthcare fraud analytics refers to the use of data analysis techniques, including statistical methods, machine learning, and artificial intelligence, to detect, prevent, and investigate fraudulent activities in healthcare. This includes identifying patterns, anomalies, and suspicious behavior in claims, billing, and other healthcare-related data to mitigate financial losses and ensure compliance with regulations.
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