AI in Insurance Market Size is valued at USD 10.9 billion in 2023 and is predicted to reach USD 98.7 billion by the year 2031 at a 32.3% CAGR during the forecast period for 2024-2031.
AI in insurance is transforming how insurers operate by enhancing customer service, improving risk assessment, automating claims processing, and detecting fraud. AI-driven tools analyze large datasets to offer personalized insurance products and streamline operations. AI has the power to enhance decision-making in the insurance value chain in terms of precision, effectiveness, and client satisfaction. The insurance industry is seeing significant development and expansion due to various factors that are driving artificial intelligence (AI). These elements include the advancement of technology, the desire of customers for improved services, legal limitations, and competitive dynamics.
However, the high implementation cost of AI in insurance, technical challenges, and regulatory and compliance issues are factors that may slowdown the growth of the target market during the forecast period. Furthermore, increasing investment, novel product launches, and collaboration by prominent players are factors estimated to create lucrative development opportunities in terms of revenue for players operating in the AI in insurance market globally over the forecast period.
The AI in insurance market is segmented based on type, application and end user. Based on type, the market is segmented as machine learning, computer vision, natural language processing (NLP), expert systems and robotics. By application, the market is segmented into underwriting & risk assessment, claims processing & fraud detection, customer service and support, personalized marketing and sales and policy pricing and recommendations. The end user segment comprises insurance companies, insurance brokers and agents and reinsurance companies.
The Natural Language Processing (NLP) category is expected to lead with a large share in the global AI in Insurance market. Chatbots & virtual assistants with natural language processing (NLP) capabilities offer 24/7 customer service, managing standard questions, policy modifications, and claims submissions without requiring human assistance. Their ability to comprehend and reply to customer inquiries promptly guarantees expedited resolution times and amplifies customer contentment. Additionally, NLP can retrieve pertinent data from unstructured data sources like social media, medical records, and customer reviews, giving underwriters thorough risk profiles.
The claims processing and fraud detection segment is estimated to grow rapidly in the global AI in Insurance market. Insurance companies' revenue growth is greatly accelerated by the integration of AI in fraud detection and claims processing, which lowers costs, increases customer happiness, and increases efficiency. It is anticipated that as AI technologies advance, they will have a greater influence on the insurance industry, resulting in new developments and benefits over competitors.
The North America AI in Insurance market is expected to register the highest market revenue in the near future. As a result of growing investments and continuous technological breakthroughs, the AI insurance market in North America is predicted to continue growing rapidly. Furthermore, insurance companies are probably going to concentrate on creating AI apps that are better tailored to their customers, increasing operational effectiveness, and strengthening fraud detection systems. Expanding AI use in the insurance industry and spurring innovation will also be greatly aided by strategic alliances between insurtech startups, technology providers, and insurers. In addition, Asia Pacific is likely to grow rapidly in the global AI in Insurance market due to rising investment in advanced technology and focus on automation in insurance industry.
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Report Attribute |
Specifications |
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Market Size Value In 2023 |
USD 10.9 Bn |
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Revenue Forecast In 2031 |
USD 98.7 Bn |
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Growth Rate CAGR |
CAGR of 32.3% from 2024 to 2031 |
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Quantitative Units |
Representation of revenue in US$ Million and CAGR from 2024 to 2031 |
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Historic Year |
2019 to 2023 |
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Forecast Year |
2024-2031 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Product, Application, End-User |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
Lemonade, Tractable, Shift Technology, ZhongAn, Metromile, Slice Labs, Insurify, Trov, PolicyGenius, Hippo Insurance, Snapsheet, Atidot, SAP SE, IBM Corporation, Salesforce, Inc., Oracle Corporation, SAS Institute Inc., Microsoft Corporation, Applied Systems, Shift Technology, SimpleFinance, OpenText Corporation, Quantemplate, Slice Insurance Technologies, Pegasystems Inc., Vertafore, Inc., Zego, and Others. |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI in Insurance Market Snapshot
Chapter 4. Global AI in Insurance Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2019 & 2031
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Type:
5.2.1. Machine Learning
5.2.2. Natural Language Processing (NLP)
5.2.3. Computer Vision
5.2.4. Expert Systems
5.2.5. Robotics
5.2.6. Other Devices
Chapter 6. Market Segmentation 2: by End user Estimates & Trend Analysis
6.1. by End user & Market Share, 2019 & 2031
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by End user:
6.2.1. Insurance Companies
6.2.2. Insurance Brokers and Agents
6.2.3. Reinsurance Companies
Chapter 7. Market Segmentation 3: by Application Estimates & Trend Analysis
7.1. by Application & Market Share, 2019 & 2031
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Application:
7.2.1. Underwriting and Risk Assessment
7.2.2. Claims Processing and Fraud Detection
7.2.3. Customer Service and Support
7.2.4. Personalized Marketing and Sales
7.2.5. Policy Pricing and Recommendations
Chapter 8. AI in Insurance Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. North America
8.1.1. North America AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by Type, 2023-2031
8.1.2. North America AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by End user, 2023-2031
8.1.3. North America AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by Application, 2023-2031
8.1.4. North America AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by country, 2023-2031
8.2. Europe
8.2.1. Europe AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by Type, 2023-2031
8.2.2. Europe AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by End user, 2023-2031
8.2.3. Europe AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by Application, 2023-2031
8.2.4. Europe AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by country, 2023-2031
8.3. Asia Pacific
8.3.1. Asia Pacific AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by Type, 2023-2031
8.3.2. Asia Pacific AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by End user, 2023-2031
8.3.3. Asia-Pacific AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by Application, 2023-2031
8.3.4. Asia Pacific AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by country, 2023-2031
8.4. Latin America
8.4.1. Latin America AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by Type, 2023-2031
8.4.2. Latin America AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by End user, 2023-2031
8.4.3. Latin America AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by Application, 2023-2031
8.4.4. Latin America AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by country, 2023-2031
8.5. Middle East & Africa
8.5.1. Middle East & Africa AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by Type, 2023-2031
8.5.2. Middle East & Africa AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by End user, 2023-2031
8.5.3. Middle East & Africa AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by Application, 2023-2031
8.5.4. Middle East & Africa AI in Insurance Market Revenue (US$ Million) Estimates and Forecasts by country, 2023-2031
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. Lemonade
9.2.2. Tractable
9.2.3. Shift Technology
9.2.4. ZhongAn
9.2.5. Metromile
9.2.6. Slice Labs
9.2.7. Insurify
9.2.8. Trov
9.2.9. PolicyGenius
9.2.10. Hippo Insurance
9.2.11. Snapsheet
9.2.12. Atidot
AI in Insurance Market By Type-
AI in Insurance Market By Application-
AI in Insurance Market By End User-
AI in Insurance Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
