BANKING, FINANCIAL SERVICES & INSURANCE
Global Generative AI in Insurance Market - Industry Trends and Forecast to 2032
REPORT OVERVIEW
Global Generative AI in Insurance Market, By Component (Solution, Service), Technology (Generative Adversarial Networks (GANs), Transformers, Variational Auto-encoders, Diffusion Networks, Others), Application (Personalized Insurance Policies, Automated Underwriting, Claims Processing Automation, Fraud Detection and Prevention, Virtual Assistants and Customer Support, Others), Region (North America, Europe, Asia-Pacific, South America, Middle East and Africa) – Industry Trends and Forecast to 2032.
Market Insights
Market Dynamics
- Fraud Detection and Risk Mitigation
- Risk Modeling and Scenario Analysis
- Data Security and Privacy
- Ethical and Regulatory Concerns
- Improved Risk Assessment and Underwriting
- Innovative Product Development
- High Computational Resources
- Integration with Existing Systems
SEGMENTATION
- Component
- Solution
- Services
- Technology
- Generative Adversarial Networks (GANs)
- Transformers
- Variational Auto-encoders
- Diffusion Networks
- Others
- Application
- Personalized Insurance Policies
- Automated Underwriting
- Claims Processing Automation
- Fraud Detection and Prevention
- Virtual Assistants and Customer Support
- Others
- North America
- U.S.
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- U.K.
- France
- Italy
- Spain
- Russia
- The Netherlands
- Belgium
- Turkey
- Rest of Europe
- Asia-Pacific
- China
- India
- Japan
- South Korea
- Singapore
- Malaysia
- Australia
- Thailand
- Philippines
- Rest of Asia-Pacific
- South America
- Brazil
- Argentina
- Chile
- Colombia
- Rest of South America
- Middle East and Africa
- Kingdom of Saudi Arabia
- South Africa
- U.A.E.
- Egypt
- Rest of Middle East and Africa
KEY MARKET PLAYERS
- DataRobot, Inc
- Amazon Web Services, Inc
- Avaamo
- IBM Corporation
- Microsoft Corporation
- LeewayHertz
- Persado, Inc.
- Aisera
- Shift Technology
- AlphaChat
Table OF CONTENTS
- SECTION 1 - INTRODUCTION
- 1.1 Taxonomy
- 1.2 Market Overview
- 1.3 Currency and Limitations
- 1.3.1 Currency
- 1.3.2 Limitations
- 1.4 Key Competitors
- SECTION 2 - RESEARCH METHODOLOGY
- 2.1 Research Approach
- 2.2 Data Collection and Validation
- 2.2.1 Secondary Research
- 2.2.2 Primary Research
- 2.3 Market Assessment
- 2.3.1 Market Size Estimation
- 2.3.2 Bottom-up Approach
- 2.3.3 Top-down Approach
- 2.3.4 Growth Forecast
- 2.4 Market Study Assumptions
- 2.5 Data Sources
- SECTION 3 - EXECUTIVE SUMMARY
- 3.1 Global Generative AI in Insurance Market, by Component
- 3.2 Global Generative AI in Insurance Market, by Technology
- 3.3 Global Generative AI in Insurance Market, by Application
- 3.4 Global Generative AI in Insurance Market, by Geography
- 3.5 Market Position Grid
- SECTION 4 - PREMIUM INSIGHTS
- 4.1 Regulatory Framework
- 4.1.1 Standards
- 4.1.2 Regulatory Landscape
- 4.2 Value Chain Analysis
- 4.3 Supply Chain Analysis
- 4.4 COVID-19 Impact
- 4.5 Russia-Ukraine War Impact
- 4.6 PORTER's Five Force Analysis
- 4.7 PESTLE Analysis
- 4.8 SWOT Analysis
- 4.9 Go to Market Strategy
- 4.10 Opportunity Orbit
- 4.11 Multivariate Modelling
- 4.12 Pricing Analysis
- SECTION 5 - MARKET DYNAMICS
- 5.1 Trends
- 5.1.1 Trend 1
- 5.1.2 Trend 2
- 5.1.3 Trend 3
- 5.2 Drivers
- 5.2.1 Fraud Detection and Risk Mitigation
- 5.2.2 Risk Modeling and Scenario Analysis
- 5.2.3 Driver 3
- 5.2.4 Driver 4
- 5.3 Restraints
- 5.3.1 Data Security and Privacy
- 5.3.2 Ethical and Regulatory Concerns
- 5.3.3 Restraint 3
- 5.4 Opportunities
- 5.4.1 Improved Risk Assessment and Underwriting
- 5.4.2 Innovative Product Development
- 5.4.3 Opportunity 3
- 5.4.4 Opportunity 4
- 5.5 Challenges
- 5.5.1 High Computational Resources
- 5.5.2 Integration with Existing Systems
- 5.5.3 Challenge 3
- SECTION 6 - GLOBAL GENERATIVE AI IN INSURANCE MARKET, BY COMPONENT
- 6.1 Component Summary
- 6.2 Market Attractive Index
- 6.3 Global Generative AI in Insurance Market, by Component (2019-2032)
- SECTION 7 - GLOBAL GENERATIVE AI IN INSURANCE MARKET, BY TECHNOLOGY
- 7.1 Technology Summary
- 7.2 Market Attractive Index
- 7.3 Global Generative AI in Insurance Market, by Technology (2019-2032)
- SECTION 8 - GLOBAL GENERATIVE AI IN INSURANCE MARKET, BY APPLICATION
- 8.1 Application Summary
- 8.2 Market Attractive Index
- 8.3 Global Generative AI in Insurance Market, by Application (2019-2032)
- SECTION 9 - GLOBAL GENERATIVE AI IN INSURANCE MARKET, BY GEOGRAPHY
- 9.1 Regional Summary
- 9.2 Market Attractive Index
- 9.3 Global Generative AI in Insurance Market, by Geography (2019-2032)
- SECTION 10 - NORTH AMERICA GENERATIVE AI IN INSURANCE MARKET
- 10.1 North America Summary
- 10.2 Market Attractive Index
- 10.3 North America Generative AI in Insurance Market, by Component (2019-2032)
- 10.4 North America Generative AI in Insurance Market, by Technology (2019-2032)
- 10.5 North America Generative AI in Insurance Market, by Application (2019-2032)
- 10.6 North America Generative AI in Insurance Market, by Country (2019-2032)
- 10.6.1 U.S.
- 10.6.2 Canada
- 10.6.3 Mexico
- 10.6.4 Rest of North America
- SECTION 11 - EUROPE GENERATIVE AI IN INSURANCE MARKET
- 11.1 Europe Summary
- 11.2 Market Attractive Index
- 11.3 Europe Generative AI in Insurance Market, by Component (2019-2032)
- 11.4 Europe Generative AI in Insurance Market, by Technology (2019-2032)
- 11.5 Europe Generative AI in Insurance Market, by Application (2019-2032)
- 11.6 Europe Generative AI in Insurance Market, by Country (2019-2032)
- 11.6.1 Germany
- 11.6.2 U.K.
- 11.6.3 France
- 11.6.4 Italy
- 11.6.5 Spain
- 11.6.6 Russia
- 11.6.7 The Netherlands
- 11.6.8 Belgium
- 11.6.9 Turkey
- 11.6.10 Rest of Europe
- SECTION 12 - ASIA-PACIFIC GENERATIVE AI IN INSURANCE MARKET
- 12.1 Asia-Pacific Summary
- 12.2 Market Attractive Index
- 12.3 Asia-Pacific Generative AI in Insurance Market, by Component (2019-2032)
- 12.4 Asia-Pacific Generative AI in Insurance Market, by Technology (2019-2032)
- 12.5 Asia-Pacific Generative AI in Insurance Market, by Application (2019-2032)
- 12.6 Asia-Pacific Generative AI in Insurance Market, by Country (2019-2032)
- 12.6.1 China
- 12.6.2 India
- 12.6.3 Japan
- 12.6.4 South Korea
- 12.6.5 Singapore
- 12.6.6 Malaysia
- 12.6.7 Australia
- 12.6.8 Thailand
- 12.6.9 Philippines
- 12.6.10 Rest of Asia-Pacific
- SECTION 13 - SOUTH AMERICA GENERATIVE AI IN INSURANCE MARKET
- 13.1 South America Summary
- 13.2 Market Attractive Index
- 13.3 South America Generative AI in Insurance Market, by Component (2019-2032)
- 13.4 South America Generative AI in Insurance Market, by Technology (2019-2032)
- 13.5 South America Generative AI in Insurance Market, by Application (2019-2032)
- 13.6 South America Generative AI in Insurance Market, by Country (2019-2032)
- 13.6.1 Brazil
- 13.6.2 Argentina
- 13.6.3 Chile
- 13.6.4 Colombia
- 13.6.5 Rest of South America
- SECTION 14 - MIDDLE EAST AND AFRICA GENERATIVE AI IN INSURANCE MARKET
- 14.1 Middle East and Africa Summary
- 14.2 Market Attractive Index
- 14.3 Middle East and Africa Generative AI in Insurance Market, by Component (2019-2032)
- 14.4 Middle East and Africa Generative AI in Insurance Market, by Technology (2019-2032)
- 14.5 Middle East and Africa Generative AI in Insurance Market, by Application (2019-2032)
- 14.6 Middle East and Africa Generative AI in Insurance Market, by Country (2019-2032)
- 14.6.1 Kingdom of Saudi Arabia
- 14.6.2 South Africa
- 14.6.3 U.A.E.
- 14.6.4 Egypt
- 14.6.5 Rest of Middle East and Africa
- SECTION 15 - COMPANY SHARE ANALYSIS
- 15.1 Global Generative AI in Insurance Market, Company Share Analysis
- 15.2 North America Generative AI in Insurance Market, Company Share Analysis
- 15.3 Europe Generative AI in Insurance Market, Company Share Analysis
- 15.4 Asia-Pacific Generative AI in Insurance Market, Company Share Analysis
- SECTION 16 - COMPANY PROFILES
- 16.1 DataRobot, Inc
- 16.1.1 Company Snapshot
- 16.1.2 Financial Overview
- 16.1.3 Product Portfolio
- 16.1.4 Recent Developments
- 16.2 Amazon Web Services, Inc
- 16.2.1 Company Snapshot
- 16.2.2 Financial Overview
- 16.2.3 Product Portfolio
- 16.2.4 Recent Developments
- 16.3 Avaamo
- 16.3.1 Company Snapshot
- 16.3.2 Financial Overview
- 16.3.3 Product Portfolio
- 16.3.4 Recent Developments
- 16.4 IBM Corporation
- 16.4.1 Company Snapshot
- 16.4.2 Financial Overview
- 16.4.3 Product Portfolio
- 16.4.4 Recent Developments
- 16.5 Microsoft Corporation
- 16.5.1 Company Snapshot
- 16.5.2 Financial Overview
- 16.5.3 Product Portfolio
- 16.5.4 Recent Developments
- 16.6 LeewayHertz
- 16.6.1 Company Snapshot
- 16.6.2 Financial Overview
- 16.6.3 Product Portfolio
- 16.6.4 Recent Developments
- 16.7 Persado, Inc.
- 16.7.1 Company Snapshot
- 16.7.2 Financial Overview
- 16.7.3 Product Portfolio
- 16.7.4 Recent Developments
- 16.8 Aisera
- 16.8.1 Company Snapshot
- 16.8.2 Financial Overview
- 16.8.3 Product Portfolio
- 16.8.4 Recent Developments
- 16.9 Shift Technology
- 16.9.1 Company Snapshot
- 16.9.2 Financial Overview
- 16.9.3 Product Portfolio
- 16.9.4 Recent Developments
- 16.10 AlphaChat
- 16.10.1 Company Snapshot
- 16.10.2 Financial Overview
- 16.10.3 Product Portfolio
- 16.10.4 Recent Developments
- SECTION 17 - RELATED REPORTS
- SECTION 18 - DISCLAIMER
RESEARCH METHODOLOGY
RESEARCH AND DATA COLLECTION
- Research articles published on Technium
- Science and MDPI
- Research publications by government approved associations and societies
DATA PRE-PROCESSING
The term "data pre-processing" refers to the collection of procedures and methods used to clean, modify, and make ready for analysis the raw data gathered during research and data collection. The completion of this phase is necessary to guarantee that the data are reliable, consistent, and appropriate for statistical analysis and other data-driven tasks. The data pre-processing ensures that the information gathered from research and data collection is comparable and expressed in standard units, by the integration of missing data pointers and algorithmic approaches.
MODELING AND FORECASTING
QUALITY ASSURANCE AND OUTPUT
Quality assurance and output involves the process of validation, adjustments, further publications of key market indicators. Extensive plausibility and consistency tests are performed on derived time series to ensure the high degree of quality of our market analysis. This quality assurance procedure also includes rigorous inspection, validation, and editing by an experienced management team to assure the dependability of the published data.