Artificial Intelligence For Healthcare Payer Market Size, Share & Trends Analysis Report By Component (Software, Services), By Deployment, By Application, By Technology, By Region, And Segment Forecasts, 2025 - 2033
Description
Artificial Intelligence For Healthcare Payer Market Summary
Artificial intelligence helps in automating administrative tasks, reducing operational costs, and enhancing the accuracy of claims processing, thereby improving overall efficiency. In addition, the growing volume of healthcare data necessitates advanced analytics, which AI can provide, offering deeper insights into patient care and outcomes. Moreover, regulatory changes and the increasing focus on value-based care models push payers to adopt artificial intelligence to improve patient engagement and satisfaction while managing risks more effectively.
Advancements in machine learning and natural language processing enable artificial intelligence systems to understand better and analyze complex medical data, leading to more informed decision-making. The continuous innovation in artificial intelligence technologies, along with the strategic partnerships between AI companies and healthcare payers, also plays a crucial role in market expansion. For instance, in April 2024, Pager, Inc., announced the development of three new applications utilizing Google Cloud's advanced generative AI (GenAI) capabilities. These GenAI applications aim to support the fourfold aim of care by alleviating the administrative burden on multidisciplinary care teams and fostering a more personalized and conversational experience for members. This enhanced interaction aids members in navigating, coordinating, and taking the next best steps in their healthcare journey.
Furthermore, fraudulent activities in healthcare claims are a significant concern for payers, leading to substantial financial losses. Artificial intelligence and machine learning algorithms excel in detecting anomalous patterns indicative of fraud. These technologies analyze historical claims data to identify suspicious activities in real time, allowing for prompt investigation and mitigation. By reducing fraud, healthcare payers are able to safeguard their financial resources and ensure that funds are appropriately allocated to genuine claims, thereby maintaining the integrity of the healthcare system.
The shift from fee-for-service to value-based care models places a greater emphasis on patient outcomes and cost efficiency. Artificial intelligence plays a pivotal role in this transition by enabling healthcare payers to analyze patient data and measure the effectiveness of care interventions. Predictive analytics help identify patients who would benefit most from preventive care and early interventions, thereby reducing hospital readmissions and improving long-term health outcomes. By supporting value-based care initiatives, artificial intelligence helps payers align with industry trends and deliver better value to members. For instance, in January 2025, Innovaccer acquired Humbi AI, an actuarial software and analytics firm, to enhance its Healthcare Intelligence Cloud with advanced actuarial capabilities. This move brought significant benefits, including enhanced contract performance, improved payer benchmarking, and optimized provider network management. These factors collectively contribute to the rapid growth of artificial intelligence in the healthcare payer market, promising improved efficiency, reduced costs, and enhanced patient care.
Global Artificial Intelligence (AI) for Healthcare Payer Market Report Segmentation
This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented the global artificial intelligence (AI) for healthcare payer market report based on the component, deployment, technology, application, and region.
Artificial intelligence helps in automating administrative tasks, reducing operational costs, and enhancing the accuracy of claims processing, thereby improving overall efficiency. In addition, the growing volume of healthcare data necessitates advanced analytics, which AI can provide, offering deeper insights into patient care and outcomes. Moreover, regulatory changes and the increasing focus on value-based care models push payers to adopt artificial intelligence to improve patient engagement and satisfaction while managing risks more effectively.
Advancements in machine learning and natural language processing enable artificial intelligence systems to understand better and analyze complex medical data, leading to more informed decision-making. The continuous innovation in artificial intelligence technologies, along with the strategic partnerships between AI companies and healthcare payers, also plays a crucial role in market expansion. For instance, in April 2024, Pager, Inc., announced the development of three new applications utilizing Google Cloud's advanced generative AI (GenAI) capabilities. These GenAI applications aim to support the fourfold aim of care by alleviating the administrative burden on multidisciplinary care teams and fostering a more personalized and conversational experience for members. This enhanced interaction aids members in navigating, coordinating, and taking the next best steps in their healthcare journey.
Furthermore, fraudulent activities in healthcare claims are a significant concern for payers, leading to substantial financial losses. Artificial intelligence and machine learning algorithms excel in detecting anomalous patterns indicative of fraud. These technologies analyze historical claims data to identify suspicious activities in real time, allowing for prompt investigation and mitigation. By reducing fraud, healthcare payers are able to safeguard their financial resources and ensure that funds are appropriately allocated to genuine claims, thereby maintaining the integrity of the healthcare system.
The shift from fee-for-service to value-based care models places a greater emphasis on patient outcomes and cost efficiency. Artificial intelligence plays a pivotal role in this transition by enabling healthcare payers to analyze patient data and measure the effectiveness of care interventions. Predictive analytics help identify patients who would benefit most from preventive care and early interventions, thereby reducing hospital readmissions and improving long-term health outcomes. By supporting value-based care initiatives, artificial intelligence helps payers align with industry trends and deliver better value to members. For instance, in January 2025, Innovaccer acquired Humbi AI, an actuarial software and analytics firm, to enhance its Healthcare Intelligence Cloud with advanced actuarial capabilities. This move brought significant benefits, including enhanced contract performance, improved payer benchmarking, and optimized provider network management. These factors collectively contribute to the rapid growth of artificial intelligence in the healthcare payer market, promising improved efficiency, reduced costs, and enhanced patient care.
Global Artificial Intelligence (AI) for Healthcare Payer Market Report Segmentation
This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented the global artificial intelligence (AI) for healthcare payer market report based on the component, deployment, technology, application, and region.
- Component Outlook (Revenue, USD Million, 2021 - 2033)
- Software
- Services
- Deployment Outlook (Revenue, USD Million, 2021 - 2033)
- Cloud
- On-Premises
- Technology Outlook (Revenue, USD Million, 2021 - 2033)
- Machine Learning (ML)
- Natural Language Processing (NLP)
- Robotic Process Automation (RPA)
- Predictive Analytics
- Generative AI
- Application Outlook (Revenue, USD Million, 2021 - 2033)
- Claims Processing Optimization
- Fraud Detection and Prevention
- Revenue Management and Billing
- Member Engagement and Personalization
- Risk Adjustment & Predictive Analytics
- Administrative Workflow Automation
- Others
- Regional Outlook (Revenue, USD Million, 2021 - 2033)
- North America
- U.S.
- Canada
- Mexico
- Europe
- UK
- Germany
- France
- Italy
- Spain
- Norway
- Sweden
- Denmark
- Asia Pacific
- Japan
- China
- India
- Thailand
- Australia
- South Korea
- Latin America
- Brazil
- Argentina
- Middle East and Africa
- Saudi Arabia
- South Africa
- UAE
- Kuwait
Table of Contents
120 Pages
- Chapter 1. Methodology and Scope
- 1.1. Market Segmentation & Scope
- 1.2. Research Methodology
- 1.3. Information Procurement
- 1.3.1. Purchased database
- 1.3.2. GVR’s internal database
- 1.3.3. Secondary sources
- 1.3.4. Primary research
- 1.4. Information or Data Analysis
- 1.4.1. Data analysis models
- 1.5. Market Formulation & Data Validation
- 1.6. Model Details
- 1.6.1. Commodity flow analysis (Model 1)
- 1.6.2. Approach 1: Commodity flow approach
- 1.7. List of Secondary Sources
- 1.8. List of Primary Sources
- 1.9. Objectives
- Chapter 2. Executive Summary
- 2.1. Market Outlook
- 2.2. Segment Outlook
- 2.2.1. Component outlook
- 2.2.2. Deployment outlook
- 2.2.3. Technology outlook
- 2.2.4. Application outlook
- 2.2.5. Regional outlook
- 2.3. Competitive Insights
- Chapter 3. Artificial Intelligence (AI) for Healthcare Payer Market Variables, Trends & Scope
- 3.1. Market Lineage Outlook
- 3.1.1. Parent market outlook
- 3.1.2. Related/ancillary market outlook
- 3.2. Market Dynamics
- 3.2.1. Market driver analysis
- 3.2.2. Market restraint analysis
- 3.2.3. Market opportunity analysis
- 3.2.4. Market challenges analysis
- 3.3. Artificial Intelligence (AI) for Healthcare Payer Market Analysis Tools
- 3.3.1. Industry Analysis - Porter’s
- 3.3.1.1. Supplier power
- 3.3.1.2. Buyer power
- 3.3.1.3. Substitution threat
- 3.3.1.4. Threat of new entrant
- 3.3.1.5. Competitive rivalry
- 3.3.2. PESTEL Analysis
- 3.3.2.1. Political landscape
- 3.3.2.2. Technological landscape
- 3.3.2.3. Economic landscape
- 3.3.2.4. Social landscape
- 3.3.2.5. Legal landscape
- 3.4. Case Studies
- 3.5. Technology Overview
- Chapter 4. Artificial Intelligence (AI) for Healthcare Payer Market: Component Estimates & Trend Analysis
- 4.1. Definitions and Scope
- 4.1. Segment Dashboard
- 4.3. Artificial Intelligence (AI) for Healthcare Payer Market Component Movement Analysis
- 4.4. Artificial Intelligence (AI) for Healthcare Payer Market Size & Trend Analysis, by Component, 2021 to 2033 (USD Million)
- 4.4.1. Software
- 4.4.1.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- 4.4.2. Services
- 4.4.2.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- Chapter 5. Artificial Intelligence (AI) for Healthcare Payer Market: Deployment Estimates & Trend Analysis
- 5.1. Definitions and Scope
- 5.2. Segment Dashboard
- 5.3. Artificial Intelligence (AI) for Healthcare Payer Market Movement Analysis
- 5.4. Artificial Intelligence (AI) for Healthcare Payer Market Size & Trend Analyses, by Deployment, 2021 to 2033 (USD Million)
- 5.5.1. Cloud
- 5.5.1.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- 5.5.2. On-Premises
- 5.5.2.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- Chapter 6. Artificial Intelligence (AI) for Healthcare Payer Market: Technology Estimates & Trend Analysis
- 6.1. Definitions and Scope
- 6.2. Segment Dashboard
- 6.3. Artificial Intelligence (AI) for Healthcare Payer Market Technology Movement Analysis
- 6.4. Artificial Intelligence (AI) for Healthcare Payer Market Size & Trend Analysis, by Technology, 2021 to 2033 (USD Million)
- 6.4.1. Machine Learning (ML)
- 6.4.1.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- 6.4.2. Natural Language Processing (NLP)
- 6.4.2.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- 6.4.3. Robotic Process Automation (RPA)
- 6.4.3.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- 6.4.4. Predictive Analytics
- 6.4.4.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- 6.4.5. Generative AI
- 6.4.5.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- Chapter 7. Artificial Intelligence (AI) for Healthcare Payer Market: Application Estimates & Trend Analysis
- 7.1. Definitions and Scope
- 7.2. Segment Dashboard
- 7.3. Artificial Intelligence (AI) for Healthcare Payer Market Movement Analysis
- 7.4. Artificial Intelligence (AI) for Healthcare Payer Market Size & Trend Analyses, by Application, 2021 to 2033 (USD Million)
- 7.4.1. Claims Processing Optimization
- 7.4.1.1. Market estimates and forecast, 2021 to 2033, (USD million)
- 7.4.2. Fraud Detection and Prevention
- 7.4.2.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- 7.4.3. Revenue Management and Billing
- 7.4.3.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- 7.4.4. Member Engagement and Personalization
- 7.4.4.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- 7.4.5. Risk Adjustment & Predictive Analytics
- 7.4.5.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- 7.4.6. Administrative Workflow Automation
- 7.4.6.1. Market estimates and forecast, 2021 to 2033 (USD Million)
- 7.4.7. Others
- 7.4.7.1. Market estimates and forecast, 2021 to 2033, (USD Million
- Chapter 8. Artificial Intelligence (AI) for Healthcare Payer Market: Regional Estimates & Trend Analysis by Component, Deployment, & Application
- 8.1. Regional Market Dashboard
- 8.2. Global Regional Market Snapshot
- 8.3. Market Size & Forecasts Trend Analysis, 2021 to 2033
- 8.4. North America
- 8.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 8.6.2. U.S.
- 8.4.2.1. U.S. country dynamics
- 8.4.2.2. Competitive landscape
- 8.4.2.3. Regulatory framework
- 8.4.2.4. U.S. market estimates and forecast, 2021 - 2033 (USD Million)
- 8.4.3. Canada
- 8.4.3.1. Canada country dynamics
- 8.4.3.2. Competitive landscape
- 8.4.3.3. Regulatory framework
- 8.4.3.4. Canada market estimates and forecast, 2021 - 2033 (USD Million)
- 8.4.4. Mexico
- 8.4.4.1. Mexico
- 8.4.4.2. Competitive landscape
- 8.4.4.3. Regulatory framework
- 8.4.4.4. Mexico market estimates and forecast, 2021 - 2033 (USD Million)
- 8.5. Europe
- 8.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 8.5.2. UK
- 8.5.2.1. UK country dynamics
- 8.5.2.2. Competitive landscape
- 8.5.2.3. Regulatory framework
- 8.5.2.4. UK market estimates and forecast, 2021 - 2033 (USD Million)
- 8.5.3. Germany
- 8.5.3.1. Germany country dynamics
- 8.5.3.2. Competitive landscape
- 8.5.3.3. Regulatory framework
- 8.5.3.4. Germany market estimates and forecast, 2021 - 2033 (USD Million)
- 8.5.4. France
- 8.5.4.1. France country dynamics
- 8.5.4.2. Competitive landscape
- 8.5.4.3. Regulatory framework
- 8.5.4.4. France market estimates and forecast, 2021 - 2033 (USD Million)
- 8.5.5. Italy
- 8.5.5.1. Italy country dynamics
- 8.5.5.2. Competitive landscape
- 8.5.5.3. Regulatory framework
- 8.5.5.4. Italy market estimates and forecast, 2021 - 2033 (USD Million)
- 8.5.6. Spain
- 8.5.6.1. Spain country dynamics
- 8.5.6.2. Competitive landscape
- 8.5.6.3. Regulatory framework
- 8.5.6.4. Spain market estimates and forecast, 2021 - 2033 (USD Million)
- 8.5.7. Norway
- 8.5.7.1. Norway country dynamics
- 8.5.7.2. Competitive landscape
- 8.5.7.3. Regulatory framework
- 8.5.7.4. Norway market estimates and forecast, 2021 - 2033 (USD Million)
- 8.5.8. Sweden
- 8.5.8.1. Sweden
- 8.5.8.2. Competitive landscape
- 8.5.8.3. Regulatory framework
- 8.5.8.4. Sweden market estimates and forecast, 2021 - 2033 (USD Million)
- 8.5.9. Denmark
- 8.5.9.1. Denmark
- 8.5.9.2. Competitive landscape
- 8.5.9.3. Regulatory framework
- 8.5.9.4. Denmark market estimates and forecast, 2021 - 2033 (USD Million)
- 8.6. Asia Pacific
- 8.6.1. Market estimates and forecasts 2021 to 2033 (USD Million)
- 8.6.2. Japan
- 8.6.2.1. Japan country dynamics
- 8.6.2.2. Competitive landscape
- 8.6.2.3. Regulatory framework
- 8.6.2.4. Japan market estimates and forecast, 2021 - 2033 (USD Million)
- 8.6.3. China
- 8.6.3.1. China country dynamics
- 8.6.3.2. Competitive landscape
- 8.6.3.3. Regulatory framework
- 8.6.3.4. China market estimates and forecast, 2021 - 2033 (USD Million)
- 8.6.4. India
- 8.6.4.1. India country dynamics
- 8.6.4.2. Competitive landscape
- 8.6.4.3. Regulatory framework
- 8.6.4.4. India market estimates and forecast, 2021 - 2033 (USD Million)
- 8.6.5. Australia
- 8.6.5.1. Australia country dynamics
- 8.6.5.2. Competitive landscape
- 8.6.5.3. Regulatory framework
- 8.6.5.4. Australia market estimates and forecast, 2021 - 2033 (USD Million)
- 8.6.6. South Korea
- 8.6.6.1. South Korea
- 8.6.6.2. Competitive landscape
- 8.6.6.3. Regulatory framework
- 8.6.6.4. South Korea market estimates and forecast, 2021 - 2033 (USD Million)
- 8.6.7. Thailand
- 8.6.7.1. Thailand
- 8.6.7.2. Competitive landscape
- 8.6.7.3. Regulatory framework
- 8.6.7.4. Thailand market estimates and forecast, 2021 - 2033 (USD Million)
- 8.7. Latin America
- 8.7.1. Market estimates and forecasts 2021 to 2033 (USD Million)
- 8.7.2. Brazil
- 8.7.2.1. Brazil
- 8.7.2.2. Competitive landscape
- 8.7.2.3. Regulatory framework
- 8.7.2.4. Brazil market estimates and forecast, 2021 - 2033 (USD Million)
- 8.7.3. Argentina
- 8.7.3.1. Argentina country dynamics
- 8.7.3.2. Competitive landscape
- 8.7.3.3. Regulatory framework
- 8.7.3.4. Argentina market estimates and forecast, 2021 - 2033 (USD Million)
- 8.8. MEA
- 8.8.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
- 8.8.2. South Africa
- 8.8.2.1. South Africa country dynamics
- 8.8.2.2. Competitive landscape
- 8.8.2.3. Regulatory framework
- 8.8.2.4. South Africa market estimates and forecast, 2021 - 2033 (USD Million)
- 8.8.3. Saudi Arabia
- 8.8.3.1. Saudi Arabia country dynamics
- 8.8.3.2. Competitive landscape
- 8.8.3.3. Regulatory framework
- 8.8.3.4. Saudi Arabia market estimates and forecast, 2021 - 2033 (USD Million)
- 8.8.4. UAE
- 8.8.4.1. UAE country dynamics
- 8.8.4.2. Competitive landscape
- 8.8.4.3. Regulatory framework
- 8.8.4.4. UAE market estimates and forecast, 2021 - 2033 (USD Million)
- 8.8.5. Kuwait
- 8.8.5.1. Kuwait country dynamics
- 8.8.5.2. Competitive landscape
- 8.8.5.3. Regulatory framework
- 8.8.5.4. Kuwait market estimates and forecast, 2021 - 2033 (USD Million)
- Chapter 9. Competitive Landscape
- 9.1. Company/Competition Categorization
- 9.2. Strategy Mapping
- 9.3. Company Market Position Analysis, 2024
- 9.4. Company Profiles/Listing
- 9.4.1. Nuance Communications (Microsoft)
- 9.4.1.1. Company overview
- 9.4.1.2. Financial performance
- 9.4.1.3. Product benchmarking
- 9.4.1.4. Strategic initiatives
- 9.4.2. AiRo Digital Labs
- 9.4.2.1. Company overview
- 9.42.2. Financial performance
- 9.4.2.3. Product benchmarking
- 9.4.2.4. Strategic initiatives
- 9.4.3. Accenture
- 9.4.3.1. Company overview
- 9.4.3.2. Financial performance
- 9.4.3.3. Product benchmarking
- 9.4.3.4. Strategic initiatives
- 9.4.4. Cognizant
- 9.4.4.1. Company overview
- 9.4.4.2. Financial performance
- 9.4.4.3. Product benchmarking
- 9.4.4.4. Strategic initiatives
- 9.4.5. Codoxo
- 9.4.5.1. Company overview
- 9.4.5.2. Financial performance
- 9.4.5.3. Product benchmarking
- 9.4.5.4. Strategic initiatives
- 9.4.6. CareCloud, Inc.
- 9.4.6.1. Company overview
- 9.4.6.2. Financial performance
- 9.4.6.3. Product benchmarking
- 9.4.6.4. Strategic initiatives
- 9.4.7. Hexaware Technologies Limited
- 9.4.7.1. Company overview
- 9.4.7.2. Financial performance
- 9.4.7.3. Product benchmarking
- 9.4.7.4. Strategic initiatives
- 9.4.8. Oracle
- 9.4.8.1. Company overview
- 9.4.8.2. Financial performance
- 9.4.8.3. Product benchmarking
- 9.4.8.4. Strategic initiatives
- 9.4.9. MST Solutions, L.L.C.
- 9.4.9.1. Company overview
- 9.4.9.2. Financial performance
- 9.4.9.3. Product benchmarking
- 9.4.9.4. Strategic initiatives
- 9.4.10. Innovaccer, Inc.
- 9.4.10.1. Company overview
- 9.4.10.2. Financial performance
- 9.4.10.3. Product benchmarking
- 9.4.10.4. Strategic initiatives
- 9.4.11. Amelia US LLC
- 9.4.11.1. Company overview
- 9.4.11.2. Financial performance
- 9.4.11.3. Product benchmarking
- 9.4.11.4. Strategic initiatives
- 9.4.12. R1 RCM Inc
- 9.4.12.1. Company overview
- 9.4.12.2. Financial performance
- 9.4.12.3. Product benchmarking
- 9.4.12.4. Strategic initiatives
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