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Global AI in Insurance Market to reach USD XX million by 2028.

Global AI in Insurance Market Size study, By Offering (Hardware, Software, Service), By Deployment Model (On-premise, Cloud), By Technology (Machine Learning, Natural Language Processing, Computer Vision, Others), By Application (Fraud Detection and Credit Analysis, Customer Profiling and Segmentation, Product and Policy Design, Underwriting and Claims Assessment), and Regional Forecasts 2022-2028

Product Code: BFBFSI-86557392
Publish Date: 6-09-2022
Page: 200

Global AI in Insurance Market is valued at approximately USD XX million in 2021 and is anticipated to grow with a healthy growth rate of more than XX% over the forecast period 2022-2028. AI (Artificial Intelligence) is supporting insurance companies by eliminating various repeatable tasks from operational teams and helps efficiently carry on more complex actions. It also helps in optimizing the services, which are offered by insurers to their brokers, customers, and other external third parties. Factors such as a growing inclination toward personalized insurance services, rising investment by insurance companies in AI & machine learning, and an increase in government initiatives for supporting digitalization are driving the global market growth. For instance, in June 2022, MS&AD announced the company partnership with Akur8. The objective of this partnership is to improve innovation development processes by automating risk modeling, by the usage of transparent artificial intelligence (AI) proprietary technology, which helps insurers to boost their speed-to-accuracy for higher market reactivity, predictive performance, and immediate business impact, along with maintaining complete control and transparency on the models created. Consequentially, the rising number of strategic initiatives by key market players is burgeoning the market growth across the globe. However, the dearth of skilled labor and high deployment cost of AI, and advanced machine learning impede the growth of the market over the forecast period of 2022-2028. Also, the rise in the number of collaborative agreements between insurance companies and AI & machine learning solution companies and the growing need to automate the operational process are anticipated to act as catalyzing factors for the market demand during the forecast period.

The key regions considered for the global AI in Insurance Market study include Asia Pacific, North America, Europe, Latin America, and the Rest of the World. North America is the leading region across the world in terms of market share owing to the growing investments in solutions such as 5G and IoT technologies, along with increasing adoption of advanced solutions. Whereas, Asia-Pacific is anticipated to exhibit the highest CAGR over the forecast period 2022-2028. Factors such as the growing demand for 5G, IoT technologies, and other technology services, as well as the rising government initiatives supporting digitalization, would create lucrative growth prospects for the AI in Insurance Market across the Asia-Pacific region.

Major market players included in this report are:
Applied Systems
IBM Corporation
Microsoft Corporation
OpenText Corporation
Oracle Corporation
Pegasystems Inc.
Quantemplate
Salesforce, Inc.
SAP SE
SAS Institute Inc.

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:
By Offering:
Hardware
Software
Service
By Deployment Model:
On-premise
Cloud
By Technology:
Machine Learning
Natural Language Processing
Computer Vision
Others
By Application:
Fraud Detection and Credit Analysis
Customer Profiling and Segmentation
Product and Policy Design
Underwriting and Claims Assessment
By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2018, 2019, 2020
Base year – 2021
Forecast period – 2022 to 2028

Target Audience of the Global AI in Insurance Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers
Investors

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2028 (USD Million)
1.2.1. AI in Insurance Market, by Region, 2020-2028 (USD Million)
1.2.2. AI in Insurance Market, by Offering, 2020-2028 (USD Million)
1.2.3. AI in Insurance Market, by Deployment Model, 2020-2028 (USD Million)
1.2.4. AI in Insurance Market, by Technology, 2020-2028 (USD Million)
1.2.5. AI in Insurance Market, by Application, 2020-2028 (USD Million)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global AI in Insurance Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global AI in Insurance Market Dynamics
3.1. AI in Insurance Market Impact Analysis (2020-2028)
3.1.1. Market Drivers
3.1.1.1. Growing inclination towards the personalized insurance services
3.1.1.2. Rising investment by insurance companies in AI & machine learning
3.1.2. Market Challenges
3.1.2.1. Dearth of skilled labour
3.1.2.2. High deployment cost of AI, advanced machine learning
3.1.3. Market Opportunities
3.1.3.1. Rise in number of collaborative agreements between insurance companies and AI & machine learning solution company
3.1.3.2. Growing need to automate the operational process
Chapter 4. Global AI in Insurance Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2028)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
4.5. Top investment opportunity
4.6. Top winning strategies
Chapter 5. Risk Assessment: COVID-19 Impact
5.1.1. Assessment of the overall impact of COVID-19 on the industry
5.1.2. Pre COVID-19 and post COVID-19 market scenario
Chapter 6. Global AI in Insurance Market, by Offering
6.1. Market Snapshot
6.2. Global AI in Insurance Market by Offering, Performance – Potential Analysis
6.3. Global AI in Insurance Market Estimates & Forecasts by Offering, 2018-2028 (USD Million)
6.4. AI in Insurance Market, Sub Segment Analysis
6.4.1. Hardware
6.4.2. Software
6.4.3. Service
Chapter 7. Global AI in Insurance Market, by Deployment Model
7.1. Market Snapshot
7.2. Global AI in Insurance Market by Deployment Model, Performance – Potential Analysis
7.3. Global AI in Insurance Market Estimates & Forecasts by Deployment Model, 2018-2028 (USD Million)
7.4. AI in Insurance Market, Sub Segment Analysis
7.4.1. On-premise
7.4.2. Cloud
Chapter 8. Global AI in Insurance Market, by Technology
8.1. Market Snapshot
8.2. Global AI in Insurance Market by Technology, Performance – Potential Analysis
8.3. Global AI in Insurance Market Estimates & Forecasts by Technology, 2018-2028 (USD Million)
8.4. AI in Insurance Market, Sub Segment Analysis
8.4.1. Machine Learning
8.4.2. Natural Language Processing
8.4.3. Computer Vision
8.4.4. Others
Chapter 9. Global AI in Insurance Market, by Application
9.1. Market Snapshot
9.2. Global AI in Insurance Market by Application, Performance – Potential Analysis
9.3. Global AI in Insurance Market Estimates & Forecasts by Application, 2018-2028 (USD Million)
9.4. AI in Insurance Market, Sub Segment Analysis
9.4.1. Fraud Detection and Credit Analysis
9.4.2. Customer Profiling and Segmentation
9.4.3. Product and Policy Design
9.4.4. Underwriting and Claims Assessment
Chapter 10. Global AI in Insurance Market, Regional Analysis
10.1. AI in Insurance Market, Regional Market Snapshot
10.2. North America AI in Insurance Market
10.2.1. U.S. AI in Insurance Market
10.2.1.1. Offering breakdown estimates & forecasts, 2018-2028
10.2.1.2. Deployment Model breakdown estimates & forecasts, 2018-2028
10.2.1.3. Technology breakdown estimates & forecasts, 2018-2028
10.2.1.4. Application breakdown estimates & forecasts, 2018-2028
10.2.2. Canada AI in Insurance Market
10.3. Europe AI in Insurance Market Snapshot
10.3.1. U.K. AI in Insurance Market
10.3.2. Germany AI in Insurance Market
10.3.3. France AI in Insurance Market
10.3.4. Spain AI in Insurance Market
10.3.5. Italy AI in Insurance Market
10.3.6. Rest of Europe AI in Insurance Market
10.4. Asia-Pacific AI in Insurance Market Snapshot
10.4.1. China AI in Insurance Market
10.4.2. India AI in Insurance Market
10.4.3. Japan AI in Insurance Market
10.4.4. Australia AI in Insurance Market
10.4.5. South Korea AI in Insurance Market
10.4.6. Rest of Asia Pacific AI in Insurance Market
10.5. Latin America AI in Insurance Market Snapshot
10.5.1. Brazil AI in Insurance Market
10.5.2. Mexico AI in Insurance Market
10.6. Rest of The World AI in Insurance Market

Chapter 11. Competitive Intelligence
11.1. Top Market Strategies
11.2. Company Profiles
11.2.1. Applied Systems
11.2.1.1. Key Information
11.2.1.2. Overview
11.2.1.3. Financial (Subject to Data Availability)
11.2.1.4. Product Summary
11.2.1.5. Recent Developments
11.2.2. IBM Corporation
11.2.3. Microsoft Corporation
11.2.4. OpenText Corporation
11.2.5. Oracle Corporation
11.2.6. Pegasystems Inc.
11.2.7. Quantemplate
11.2.8. Salesforce, Inc.
11.2.9. SAP SE
11.2.10. SAS Institute Inc.
Chapter 12. Research Process
12.1. Research Process
12.1.1. Data Mining
12.1.2. Analysis
12.1.3. Market Estimation
12.1.4. Validation
12.1.5. Publishing
12.2. Research Attributes
12.3. Research Assumption

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Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
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Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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