Artificial Intelligence in Insurance Market Size, Share & Industry Analysis

Artificial Intelligence in Insurance Market Size & Forecast

Artificial Intelligence in Insurance Market is expected to witness significant growth, increasing from US$ 7.96 Billion in 2025 to US$ 83.43 Billion by 2034, at a CAGR of 29.84% during the forecast period of 2026–2034. Growth is driven by the increasing adoption of AI for claims processing, fraud detection, underwriting, customer service, and risk assessment. Insurers are leveraging machine learning, predictive analytics, and automation to enhance operational efficiency, improve customer experiences, reduce costs, and support data-driven decision-making across insurance operations.

Artificial Intelligence in Insurance Market Overviews

Artificial Intelligence (AI) in insurance refers to the use of advanced technologies such as machine learning, natural language processing, computer vision, and predictive analytics to automate and improve insurance operations. AI enables insurers to analyze large volumes of data, identify patterns, assess risks, and make faster and more accurate decisions. It is widely used in underwriting, claims processing, fraud detection, customer service, pricing, and risk management. For example, AI-powered systems can evaluate policy applications, detect suspicious claims, and provide instant responses through virtual assistants and chatbots.

AI has become increasingly popular worldwide because insurance companies are under pressure to improve efficiency, reduce operational costs, and deliver better customer experiences. The growing availability of digital data from smartphones, connected devices, telematics, and wearable technologies has further accelerated AI adoption. In developed markets, insurers are using AI to personalize policies and streamline claims settlements, while emerging markets are leveraging AI to expand access to insurance services. The technology also helps companies comply with regulatory requirements and improve decision-making through real-time analytics. As digital transformation continues across the global insurance industry, AI is expected to play a central role in creating smarter, faster, and more customer-focused insurance services.

AI In The Insurance Industry Statistics

• •85% of insurance leaders believe AI will transform their workforce
• •70% of insurance companies integrating AI into digital transformation strategies
• •AI can reduce insurance claims processing costs by 20-30%
• •60% of insurance executives view AI as critical for operational efficiency
• •AI-powered underwriting can reduce turnaround times by up to 80%
• •75% of insurance companies plan to increase AI investment for risk management
• •AI spending by insurance sector projected to reach $11B by 2025
• •Machine learning adopted by 75% of insurance firms for experimentation or deployment
• •AI-driven personalization in marketing can increase conversion rates by 20%
• •60% of insurance CEOs believe AI will have more impact than any other technology
• •AI-powered claims adjusters process simple claims 3x faster than human-only processes
• •Fraud detection systems using AI reduce false positives by 10-15%
• •80% adoption rate of AI expected among top-tier carriers by 2025

Digital transformation in insurance with AI integration

• 70% of insurance companies are integrating AI into their core digital transformation strategies.
• Cloud-based AI solutions are being adopted by 80% of insurers for scalability and agility.
• Machine learning is the most commonly adopted AI technology in insurance, with 75% of firms experimenting or deploying it.
• Data analytics capabilities, enhanced by AI, are a top investment priority for 90% of insurance CIOs.
• The adoption of Generative AI in insurance is expected to reach 40% by 2026 for content creation and personalized communication.
• Cybersecurity spending focused on protecting AI systems in insurance is forecast to increase by 20% annually.
• API integration for AI-powered solutions is a key focus for 60% of insurers to connect disparate systems.
• Low-code/no-code platforms, often incorporating AI capabilities, are being used by 30% of insurers to accelerate application development.
• Investment in AI ethics and responsible AI frameworks has grown by 50% in the past year among leading insurers.
• 80% of insurance companies are establishing dedicated AI centers of excellence or innovation labs.
• The average data quality improvement for insurers implementing AI-driven data cleansing tools is 15%.
• Over 50% of insurers are leveraging AI for advanced analytics beyond traditional BI, focusing on predictive and prescriptive insights.
• The use of explainable AI (XAI) is becoming a priority for 45% of insurers to ensure transparency and trust in critical decision-making.
• AI-driven RPA (Robotic Process Automation) adoption in insurance grew by 25% in the last year.
• Blockchain integration with AI for secure data sharing and smart contracts in insurance is being explored by 20% of the market.
• The migration of legacy systems to AI-compatible cloud environments is a strategic goal for 70% of large insurers by 2025.
• AI platforms are reducing the development time for new digital products in insurance by an average of 30%.
• Investment in conversational AI (chatbots, virtual assistants) by insurers is projected to grow by 18% annually through 2025.

Growth Drivers of the Artificial Intelligence (AI) in Insurance Market

Increasing Demand for Automated Claims Processing and Operational Efficiency

The growing need for faster and more efficient insurance operations is a major driver of the Artificial Intelligence (AI) in insurance market. Insurance companies process millions of claims, policy applications, and customer inquiries every year, making manual operations both time-consuming and expensive. AI-powered automation enables insurers to streamline repetitive tasks such as document verification, claims assessment, policy issuance, and customer communication. Machine learning algorithms can quickly analyze claim information, identify inconsistencies, and recommend settlement decisions with greater speed and accuracy. This significantly reduces processing time, operational costs, and human errors while improving customer satisfaction. AI also helps insurers allocate resources more effectively by allowing employees to focus on complex cases instead of routine administrative work. As insurers continue their digital transformation initiatives and seek to improve productivity, investments in AI-driven automation solutions are expected to increase, making operational efficiency one of the strongest growth drivers for the global AI in insurance market.

Rising Adoption of AI for Fraud Detection and Risk Assessment

Insurance fraud remains one of the industry’s biggest financial challenges, leading to billions of dollars in losses each year. Artificial intelligence provides insurers with advanced tools to identify suspicious activities by analyzing vast amounts of structured and unstructured data in real time. AI models can detect unusual claim patterns, assess behavioral anomalies, and identify fraudulent transactions that may not be easily recognized through traditional methods. Additionally, AI enhances underwriting by improving risk assessment using predictive analytics, historical data, demographic information, telematics, and external data sources. This enables insurers to price policies more accurately while minimizing underwriting risks. AI continuously learns from new data, improving its detection capabilities over time and helping insurers stay ahead of evolving fraud techniques. As regulatory compliance requirements become more stringent and insurers prioritize financial stability, the adoption of AI-driven fraud detection and risk management solutions continues to expand across both developed and emerging insurance markets.

Growing Digital Transformation and Demand for Personalized Customer Experiences

The rapid digital transformation of the insurance industry is significantly driving the adoption of artificial intelligence. Customers increasingly expect personalized insurance products, instant policy approvals, digital interactions, and 24/7 customer support. AI enables insurers to analyze customer behavior, preferences, purchase history, and lifestyle data to develop customized insurance policies and pricing models. Intelligent chatbots and virtual assistants provide immediate responses to customer inquiries, improving engagement while reducing service costs. AI-powered recommendation systems also help insurers identify cross-selling and upselling opportunities based on individual customer profiles. Furthermore, integration with connected devices, wearable technologies, smartphones, and telematics generates continuous streams of data that AI uses to offer dynamic pricing and proactive risk management. As competition intensifies in the insurance industry, companies are increasingly investing in AI technologies to strengthen customer relationships, improve retention rates, and deliver seamless digital experiences. This growing emphasis on customer-centric insurance services continues to accelerate global AI adoption across the insurance sector.

Major Artificial Intelligence (AI) in Insurance Launches Worldwide

• Microsoft – Azure AI for Insurance (2024): Microsoft expanded its Azure AI capabilities for insurers, enabling automated claims processing, intelligent document analysis, fraud detection, predictive underwriting, and AI-powered customer engagement through cloud-based services.
• Salesforce – Einstein for Financial Services & Insurance (2024): Salesforce enhanced its Einstein AI platform with new generative AI capabilities for insurance companies, supporting customer service automation, policy recommendations, claims assistance, and agent productivity.
• Guidewire – Guidewire Intelligent Automation (2024): Guidewire introduced AI-powered automation features for policy administration and claims management, helping insurers improve operational efficiency, workflow automation, and risk assessment.
• Duck Creek Technologies – Duck Creek Clarity AI (2024): Duck Creek launched AI-enhanced analytics and reporting capabilities that enable insurers to gain real-time business insights, optimize underwriting decisions, and improve claims performance.
• CCC Intelligent Solutions – AI Claims Platform (2024): CCC expanded its AI-driven insurance claims platform with computer vision technology that automates vehicle damage assessment, repair estimation, and digital claims processing.
• Shift Technology – AI Fraud Detection Solutions (2024): Shift Technology introduced advanced AI solutions for insurance fraud detection, claims investigation, and underwriting risk analysis using machine learning and predictive analytics.
• Insurity – AI-Powered Risk Analytics Platform (2024): Insurity launched enhanced AI capabilities that improve underwriting accuracy, automate policy workflows, and strengthen risk management for property and casualty insurers.
• Zurich Insurance – Generative AI Customer Service Platform (2024): Zurich Insurance implemented generative AI solutions to assist customer service teams, automate policy inquiries, accelerate claims support, and improve customer experience.
• Allianz – AI-Based Claims Automation System (2024): Allianz introduced advanced AI technologies to automate claims handling, detect fraudulent claims, optimize settlement processes, and improve operational efficiency across insurance services.
• Lemonade – AI Claims and Underwriting Enhancements (2024): Lemonade expanded its AI-powered insurance platform with enhanced underwriting models, instant claims processing, personalized policy recommendations, and improved customer support through conversational AI.

Challenges of the Artificial Intelligence (AI) in Insurance Market

Data Privacy, Security, and Regulatory Compliance Concerns

One of the major challenges facing the Artificial Intelligence in insurance market is ensuring data privacy, cybersecurity, and compliance with evolving regulations. AI systems require access to large volumes of sensitive customer information, including personal details, financial records, medical histories, and behavioral data. Protecting this information from cyberattacks, unauthorized access, and data breaches remains a significant concern for insurance companies. Additionally, different countries have varying data protection laws and regulatory frameworks governing the use of artificial intelligence and personal data. Insurers must ensure that AI algorithms comply with privacy regulations while maintaining transparency in automated decision-making processes. Ethical concerns regarding algorithmic bias, fairness, and explainability also require careful management. Failure to address these issues can result in legal penalties, reputational damage, and reduced customer trust. Consequently, insurers must invest heavily in cybersecurity infrastructure, governance frameworks, and regulatory compliance programs before achieving the full benefits of AI adoption.

High Implementation Costs and Integration with Legacy Systems

The implementation of artificial intelligence solutions often requires significant financial investment, making adoption challenging for many insurance providers, particularly small and medium-sized companies. AI deployment involves expenditures on advanced software platforms, cloud infrastructure, data management systems, skilled professionals, and ongoing model training and maintenance. In addition, many insurers continue to operate on legacy IT systems that were not designed to support modern AI technologies. Integrating AI with these outdated infrastructures can be technically complex, time-consuming, and costly. Data quality issues, inconsistent records, and fragmented databases further complicate AI implementation and reduce algorithm performance. Organizations must also invest in employee training and change management to ensure successful adoption across business operations. Without proper planning and modernization strategies, AI projects may experience delays, budget overruns, or limited return on investment. These implementation and integration challenges remain significant barriers to widespread AI adoption within the global insurance industry.

Customer Service AI in Insurance Market Overview

The Customer Service AI in Insurance Market is experiencing significant growth as insurance providers increasingly adopt artificial intelligence to improve customer engagement, streamline support services, and enhance operational efficiency. AI-powered chatbots, virtual assistants, and conversational platforms enable insurers to provide 24/7 customer support, instantly respond to policy inquiries, assist with claims filing, and guide customers through policy selection. These intelligent systems use natural language processing and machine learning to understand customer intent and deliver personalized responses with minimal human intervention. AI also helps reduce call center workloads, shorten response times, and improve customer satisfaction by offering faster issue resolution. Insurers are integrating AI into websites, mobile applications, and messaging platforms to provide seamless digital experiences across multiple communication channels. Continuous learning capabilities allow AI systems to improve service quality over time by analyzing customer interactions and feedback. As customer expectations for fast, convenient, and personalized services continue to rise, the adoption of AI-driven customer service solutions is expected to expand across the global insurance industry.

Cloud AI in Insurance Market Overview

The Cloud AI in Insurance Market is expanding rapidly as insurance companies increasingly migrate their operations to cloud-based platforms that support artificial intelligence applications. Cloud AI enables insurers to access scalable computing resources, advanced analytics, and machine learning capabilities without investing heavily in on-premises infrastructure. Cloud-based AI solutions improve underwriting, claims processing, fraud detection, customer relationship management, and predictive risk analysis by enabling real-time data processing and secure information sharing. The flexibility of cloud deployment allows insurers to quickly implement AI models, update algorithms, and integrate new digital services across multiple business functions. Cloud AI also facilitates collaboration between insurers, brokers, healthcare providers, and third-party service providers through centralized data management. Enhanced cybersecurity features, automated software updates, and disaster recovery capabilities further strengthen cloud adoption. As insurance companies continue their digital transformation initiatives, cloud-based AI platforms are becoming essential for improving operational efficiency, reducing costs, supporting innovation, and delivering more responsive customer services.

Large Enterprise AI in Insurance Market Overview

The Large Enterprise AI in Insurance Market represents a significant share of global AI adoption, as major insurance companies possess the financial resources, extensive customer databases, and technological infrastructure necessary for large-scale AI implementation. Large insurers utilize artificial intelligence across underwriting, claims management, fraud detection, customer service, regulatory compliance, and risk assessment to improve operational performance and business decision-making. AI enables these organizations to process vast amounts of structured and unstructured data quickly, allowing faster policy approvals, accurate pricing models, and personalized insurance offerings. Large enterprises also leverage predictive analytics to identify emerging risks, optimize investment strategies, and improve customer retention. Integration with cloud computing, robotic process automation, and advanced data analytics further enhances AI capabilities across enterprise operations. Continuous investment in research and development, digital transformation, and cybersecurity enables large insurers to maintain competitive advantages while improving customer experiences. As AI technologies continue to mature, large enterprises are expected to remain key contributors to market growth and innovation.

Machine Learning AI in Insurance Market Overview

The Machine Learning AI in Insurance Market is growing steadily as insurers increasingly adopt machine learning algorithms to improve decision-making, automate business processes, and enhance predictive capabilities. Machine learning enables insurance companies to analyze large datasets, identify hidden patterns, and continuously improve model accuracy through ongoing learning from new information. It is widely applied in underwriting, claims prediction, fraud detection, customer segmentation, policy pricing, and risk evaluation. By analyzing historical claims data, customer behavior, telematics, medical records, and financial information, machine learning models help insurers make more accurate and consistent business decisions. These algorithms also detect unusual claim activities, reducing fraud-related losses while improving operational efficiency. Machine learning supports personalized insurance products by identifying individual customer preferences and risk profiles. As insurers continue investing in digital technologies, the adoption of machine learning is expected to accelerate due to its ability to improve accuracy, reduce processing time, lower operational costs, and support data-driven innovation throughout the insurance value chain.

United States AI in Insurance Market

The United States AI in Insurance Market is one of the most advanced and mature markets globally, driven by strong digital transformation initiatives, high technology adoption, and significant investments in artificial intelligence. Leading insurance providers are increasingly implementing AI to automate underwriting, accelerate claims processing, improve fraud detection, and deliver personalized customer experiences. The widespread availability of cloud computing, big data analytics, and machine learning platforms enables insurers to process large volumes of customer information efficiently while improving decision accuracy. The growing adoption of telematics, wearable devices, and Internet of Things (IoT) technologies further enhances AI-powered risk assessment and usage-based insurance models. Customer service has also improved through AI-powered chatbots and virtual assistants that provide round-the-clock assistance. Additionally, increasing cybersecurity investments and regulatory focus on responsible AI deployment encourage insurers to adopt transparent and secure AI solutions. Continuous innovation by technology companies and insurance providers is expected to support sustained market expansion across both personal and commercial insurance segments. July 2026, BMO Insurance today announced the launch of SmartDecision, an innovative underwriting solution that uses advanced analytics and AI-enhanced capabilities to deliver real-time decisions to clients and advisors.

United Kingdom AI in Insurance Market

The United Kingdom AI in Insurance Market is witnessing steady growth as insurers increasingly embrace artificial intelligence to modernize business operations and improve customer engagement. Insurance companies are integrating AI into underwriting, claims management, fraud prevention, customer support, and policy administration to increase operational efficiency and reduce processing time. Predictive analytics and machine learning enable insurers to evaluate risks more accurately while offering customized insurance products that meet individual customer requirements. Digital transformation initiatives have encouraged insurers to adopt cloud-based AI platforms that support real-time data analysis and automated decision-making. AI-powered virtual assistants and conversational platforms are improving customer service by providing instant responses and faster claims assistance. The market also benefits from increasing collaboration between insurance companies and technology providers to develop innovative AI solutions. As customer expectations for digital services continue to rise, insurers are investing in advanced analytics, automation, and responsible AI practices to improve competitiveness while enhancing transparency, operational resilience, and long-term business performance. February 2026, Jointly AI, a UK-based insurance technology company focused on automating brokerage operations, has announced the release of Jointly AI Broker, an end-to-end autonomous AI platform developed for personal lines brokers in the United Kingdom.

India AI in Insurance Market

The India AI in Insurance Market is expanding rapidly due to increasing digitalization, growing internet penetration, rising smartphone usage, and government initiatives promoting digital financial services. Insurance companies are adopting artificial intelligence to automate underwriting, streamline claims processing, improve fraud detection, and deliver personalized insurance solutions to a diverse customer base. AI-powered chatbots and virtual assistants help insurers provide multilingual customer support, enabling faster communication and improved accessibility across urban and rural regions. Machine learning algorithms analyze customer behavior, medical records, financial information, and risk profiles to support accurate policy pricing and efficient risk assessment. The rapid growth of health insurance, life insurance, and digital insurance platforms has further accelerated AI adoption throughout the industry. Collaboration between insurers, technology startups, and cloud service providers is fostering innovation in predictive analytics and intelligent automation. As insurance penetration continues to increase and digital transformation accelerates, AI is expected to play a central role in improving operational efficiency, financial inclusion, and customer satisfaction.

Saudi Arabia AI in Insurance Market

The Saudi Arabia AI in Insurance Market is experiencing significant growth as the country accelerates its digital transformation and expands the adoption of advanced technologies across the financial services sector. Insurance providers are increasingly implementing artificial intelligence to improve underwriting accuracy, automate claims management, strengthen fraud detection, and enhance customer service. AI-powered analytics enable insurers to process large volumes of customer and risk data efficiently, resulting in faster policy issuance and more informed decision-making. The growing adoption of cloud computing, digital payment systems, and mobile insurance platforms is supporting wider implementation of AI-based insurance solutions. Health insurance and motor insurance remain major application areas where AI is improving operational efficiency and customer engagement through automated services and predictive risk assessment. Investments in smart technologies, digital infrastructure, and innovation are encouraging insurers to modernize their operations while improving regulatory compliance and service quality. These developments are expected to support sustained growth in the Saudi Arabian AI in Insurance Market over the coming years.

Research Methodology for the Artificial Intelligence in Insurance Market

1. Market Definition and Scope

The Artificial Intelligence in Insurance Market should be defined as the revenue generated from AI technologies, platforms, software, solutions, and related services deployed by insurance companies to automate, augment, or support insurance operations.

The scope should cover machine learning, deep learning, natural language processing, computer vision, generative AI, predictive analytics, and other AI technologies. Applications should include underwriting, pricing and risk assessment, claims processing, fraud detection, customer service, policy administration, sales and marketing, actuarial analytics, and risk management.

The market should be segmented by technology, application, insurance type, deployment model, organization size, and geography. AI used internally by insurers, AI platforms supplied by technology companies, and AI-enabled insurance software should be included according to the defined revenue boundary.

2. Estimate the Number of Insurance Companies Using AI

The first step should be to establish the addressable insurer base by country and insurance line, including life, health, property and casualty, auto, commercial, specialty, and reinsurance companies.

Insurance regulator databases, industry associations, annual reports, insurer disclosures, technology surveys, and AI adoption studies should be used to determine the proportion of insurers that currently use, are implementing, or are planning to adopt AI.

For example, NAIC surveys have documented AI/ML usage or exploration across U.S. auto, homeowners, life, and health insurers, demonstrating that adoption varies considerably by line of business.

3. Estimate AI Adoption Rate

AI adoption should then be estimated for each insurance segment.

The adoption rate should consider:

• Percentage of insurers using AI
• Percentage implementing AI
• Percentage conducting AI pilots
• Number of AI use cases per insurer
• AI maturity level
• Size of insurer
• Technology investment intensity

Large insurers should generally be modelled separately from small and medium insurers because their AI expenditure and number of deployments can differ substantially.

4. Estimate AI Spending per Insurance Company

The annual AI expenditure per insurer should be estimated using:

AI Spending = Software + Cloud/Computing + AI Platforms + Data + Implementation + Consulting + Maintenance

Spending benchmarks should be developed separately for:

• Large insurers
• Mid-sized insurers
• Small insurers
• Regional insurers
• Reinsurers

Company annual reports, technology budgets, vendor contracts, AI implementation announcements, and enterprise software expenditure benchmarks should be used to establish reasonable spending ranges.

5. Estimate Market by AI Technology

The market should be divided into major AI technologies:

• Machine Learning
• Deep Learning
• Natural Language Processing
• Computer Vision
• Generative AI
• Predictive Analytics
• Robotic/Intelligent Process Automation
• Other AI technologies

Each technology should be estimated according to its penetration within insurance workflows and the corresponding software, infrastructure, and service expenditure.

Generative AI should be modelled separately because its insurance applications and adoption trajectory differ from traditional machine-learning systems. Global insurance supervisors have identified claims handling, external chatbots, fraud detection, process optimization, complaints handling, and information retrieval among the leading GenAI use cases.

6. Estimate AI Spending by Insurance Function

AI expenditure should be allocated across major insurance functions:

Underwriting and Risk Assessment

Estimate spending on automated risk assessment, predictive underwriting, external-data analysis, risk scoring, and accelerated underwriting.

Claims Management

Estimate AI spending on claims automation, document processing, damage assessment, image analysis, settlement estimation, and claims triage.

Fraud Detection

Estimate spending on anomaly detection, behavioral analytics, suspicious-claim identification, and fraud investigation.

Customer Service

Include AI chatbots, virtual assistants, voice AI, personalized communication, and automated customer support.

Pricing and Actuarial Analytics

Include predictive pricing models, risk-factor analysis, portfolio analytics, loss forecasting, and actuarial modelling.

Sales and Marketing

Include customer segmentation, personalized offers, lead scoring, recommendation engines, and targeted marketing.

Policy Administration

Include automated document processing, policy issuance, renewals, servicing, and data extraction.

NAIC documentation confirms that AI is being used across underwriting, pricing, claims, marketing, customer service, and fraud detection, providing a framework for functional segmentation.

7. Calculate the Bottom-Up Market Size

The primary market estimate should be calculated using insurer-level AI expenditure.

AI in Insurance Market = Σ (Number of AI-Adopting Insurers × Average Annual AI Spending per Insurer)

The calculation should be performed separately for each country, insurance line, insurer size category, and AI application and then aggregated.

For example:

Large Insurers × Average AI Spending + Mid-sized Insurers × Average AI Spending + Small Insurers × Average AI Spending

This provides the core bottom-up market estimate.

8. Estimate AI Software Revenue

AI software should be separated from broader IT expenditure.

The analysis should include:

• AI underwriting platforms
• Claims AI software
• Fraud analytics platforms
• AI customer-service platforms
• Predictive analytics
• Computer-vision solutions
• GenAI platforms
• AI model-management software
• AI-enabled insurance administration platforms

Revenue should be estimated from vendor disclosures, contract values, customer counts, average contract values, and insurer adoption.

9. Estimate AI Services and Implementation Revenue

The market should also include services associated with implementing and operating AI systems.

These should cover:

• AI consulting
• Model development
• Data preparation
• System integration
• AI implementation
• Model validation
• AI governance
• Training
• Maintenance and support

This is particularly important because many insurers use a combination of internally developed models and third-party AI solutions. NAIC survey findings indicate that third-party vendors are particularly relevant in some insurance functions, while pricing and underwriting models are often developed internally by auto and homeowners’ insurers.

10. Estimate AI Infrastructure and Cloud Spending

A portion of AI expenditure should be allocated to:

• Cloud computing
• AI computing infrastructure
• Data storage
• GPU/accelerated computing
• Model hosting
• Data platforms
• AI development environments

However, only the portion directly attributable to insurance AI applications should be included to avoid overstating the market with general IT infrastructure expenditure.

11. Validate Through Insurance Company Disclosures

Major insurers should be analyzed individually to identify:

• AI investments
• AI-related technology expenditure
• Number of AI applications
• AI partnerships
• GenAI deployments
• Claims automation
• Underwriting automation
• Fraud detection systems
• AI workforce initiatives

The company-level estimates should then be aggregated and compared with the bottom-up industry estimate.

12. Validate Through AI Vendor Revenue

AI vendors and insurance technology providers should be analyzed from the supply side.

The assessment should cover vendors providing:

• AI software
• InsurTech platforms
• Predictive analytics
• Fraud detection
• Claims automation
• Computer vision
• NLP
• GenAI
• Cloud AI
• AI consulting and integration

Vendor revenue attributable specifically to insurance should be separated from revenue generated from banking, healthcare, retail, and other industries.

13. Use Insurance Premium and IT Expenditure as a Top-Down Benchmark

A top-down model should be developed using:

Total Insurance Industry IT Spending × AI Share of IT Spending

Alternatively:

Insurance Industry Operating Expenditure × Technology Spending Ratio × AI Allocation

This should be calculated by insurance line and geography because AI adoption differs substantially between life, health, P&C, auto, and specialty insurance.

The top-down estimate should be used as a validation benchmark rather than simply being treated as the final market value.

14. Validate Through AI Adoption Surveys

AI adoption surveys should be used to validate:

• Percentage of insurers using AI
• Number of AI use cases
• AI implementation maturity
• Planned AI investment
• GenAI adoption
• Third-party versus internally developed AI
• AI spending priorities

NAIC's continuing insurer surveys and AI oversight work provide useful evidence for estimating adoption and governance by insurance line.

15. Estimate the Market by Insurance Type

The market should be separately calculated for:

• Life Insurance
• Health Insurance
• Property Insurance
• Casualty Insurance
• Auto Insurance
• Commercial Insurance
• Specialty Insurance
• Reinsurance

AI intensity should be estimated separately because applications such as accelerated underwriting may be particularly relevant to life insurance, while image-based claims assessment and fraud detection can be important in P&C and auto insurance.

16. Estimate the Market by Deployment Model

The market should be segmented into:

• Cloud-based AI
• On-premises AI
• Hybrid AI

Cloud expenditure should include AI-as-a-service and cloud-hosted models, while on-premises expenditure should include internally deployed AI infrastructure and software.

Hybrid deployment should account for insurers maintaining sensitive data or critical models internally while using external cloud AI capabilities.

17. Estimate AI Market by Organization Size

Insurance companies should be divided into:

• Large insurers
• Mid-sized insurers
• Small insurers

Large insurers should receive separate treatment because they typically operate larger technology budgets, more extensive data environments, multiple insurance lines, and a greater number of AI applications.

Small and mid-sized insurers can be modelled using adoption rates and average AI expenditure per company.

18. Validate Through Regulatory and Governance Requirements

AI governance expenditure should be included where it directly relates to AI deployment.

This may include:

• Model validation
• Explainability
• Bias testing
• Data governance
• AI risk management
• Compliance monitoring
• Human oversight
• Third-party model assessment
• AI documentation

This is increasingly relevant because regulators expect insurers to maintain governance, risk management, transparency, fairness, and compliance around AI-supported decisions.

19. Account for Third-Party AI Models and Data

The estimation should separately identify expenditure on external:

• Data providers
• Predictive models
• AI platforms
• Foundation models
• Cloud AI services
• Model APIs
• Analytics platforms

This prevents third-party AI expenditure from being missed when insurers do not develop models internally.

NAIC has specifically established work around third-party data and models because external providers can influence insurer decision-making and require appropriate governance and oversight.

20. Estimate Country-Level Markets

The market should be estimated country by country using:

Number of Insurers × AI Adoption Rate × Average AI Spending

Countries should then be grouped into:

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East
• Africa

Country estimates should reflect local insurance penetration, insurer size, technology expenditure, regulatory environment, digital maturity, and AI adoption.

21. Estimate Historical Market Size

Historical market sizes should be calculated by applying historical AI adoption rates and AI expenditure to the insurer base for each year.

Historical changes should consider:

• AI adoption
• Machine-learning deployment
• Cloud migration
• InsurTech investment
• Claims automation
• Accelerated underwriting
• Fraud analytics
• Generative AI introduction

Historical estimates should be recalculated consistently rather than simply applying a single CAGR backward from the current-year estimate.

22. Forecast the Artificial Intelligence in Insurance Market

The forecast should incorporate:

• Growth in insurer AI adoption
• Increasing AI spending per insurer
• Generative AI adoption
• Automated underwriting
• AI-based claims processing
• Fraud detection
• Digital customer service
• Cloud migration
• Data availability
• AI regulatory requirements
• InsurTech investment
• AI model and infrastructure costs

The forecast can be calculated using:

Future Market Size = Base-Year Market Size × (1 + CAGR) ^n

A driver-based forecast should then be used to test whether the resulting CAGR is consistent with insurer adoption and technology spending trends.

23. Third Validation and Market Triangulation

The final market size should be triangulated through three independent approaches:

Demand-Side

Insurer Count × AI Adoption Rate × Average AI Expenditure

Supply-Side

AI Vendor Revenue Attributable to Insurance + AI Services Revenue + Insurance-Specific AI Infrastructure Revenue

Top-Down

Insurance IT Expenditure × AI Allocation

The three estimates should not simply be averaged. Differences should be investigated based on market definition, internal versus third-party AI, software versus services, double counting, insurer size, and geographic coverage. The final estimate should be selected after reconciling these differences.

Core Formula

Artificial Intelligence in Insurance Market = Σ [(Number of Insurance Companies × AI Adoption Rate) × Average Annual AI Spending per Adopting Insurer]

Alternative Supply-Side Formula

AI in Insurance Market = Insurance-Specific AI Software Revenue + AI Services Revenue + AI Infrastructure Revenue + AI Data/Model Revenue

Market Segmentation

Application

• Claims Processing
• Customer Service
• Underwriting
• Fraud Detection
• Others

Deployment

• Cloud
• On Premise

Enterprise Type

• Large Enterprise
• SMEs

Technology

• Machine Learning
• Natural Language Processing (NLP)
• Computer Vision
• Others

Countries

North America

• United States
• Canada

Europe

• France
• Germany
• Italy
• Spain
• United Kingdom
• Belgium
• Netherlands
• Turkey

Asia Pacific

• China
• Japan
• India
• South Korea
• Thailand
• Malaysia
• Indonesia
• Australia
• New Zealand

Latin America

• Brazil
• Mexico
• Argentina

Middle East & Africa

• Saudi Arabia
• UAE
• South Africa

Rest of the World

All companies have been covered with 5 Viewpoints

• Overviews
• Key Person
• Recent Developments
• SWOT Analysis
• Revenue Analysis

Key Players Analysis

• Lemonade, Inc.
• Tractable
• ZestyAI
• FurtherAI, Inc.
• Afiniti
• Metromile, Inc.
• Counterforce Health
• STS Software
• Root Insurance Company
• Next Insurance

Report Details: