Aug 11: The global insurance industry is entering a new era in which data, artificial intelligence (AI) and automation are changing how insurers assess risk, underwrite policies, process claims and interact with customers.
Insurance has always been a data-driven business. What has changed is the scale and speed at which insurers can collect, analyse and act on information. AI can now process large volumes of structured and unstructured data, while automation can connect multiple stages of an insurance workflow with less manual intervention.
The transformation comes as insurers face a more complicated risk environment. Climate-related losses, geopolitical tensions, cyber threats, changing demographics and evolving customer expectations are forcing insurance providers to rethink traditional operating models.
According to McKinsey, global insurance gross written premiums reached an estimated $8.3 trillion in 2025, while profits before tax were approximately $580 billion. The consultancy expects AI to potentially reshape the economics and competitive dynamics of the insurance industry.
Global Insurance Industry Faces a Changing Risk Landscape
The global insurance market remains resilient, but growth is becoming more closely tied to changing economic and risk conditions.
Swiss Re’s July 2026 outlook expects global insurance premiums to grow by 1.3% in real terms in 2026, down from 3.9% in 2025. Global non-life real premium growth is forecast at 0.6% in 2026, while life insurance growth is expected to remain stronger at 2.3%.
For insurers, slower growth does not necessarily mean fewer opportunities. It increases the importance of improving productivity, controlling costs and finding better ways to understand emerging risks.
Technology is becoming an important part of that equation.
AI can help insurers process information faster, while automation can reduce repetitive work across areas such as underwriting, claims and customer servicing. Together, these technologies are creating the possibility of a more responsive insurance operating model.
Data Is Becoming the Foundation of Modern Insurance
Insurance providers have traditionally relied on information such as customer profiles, claims history, property characteristics, vehicle details and financial information.
The data available to insurers today is much broader.
Connected devices, telematics, satellite imagery, geospatial information, digital transactions and other external data sources can provide additional insight into individual and commercial risks.
For a property insurer, for example, satellite and weather information can help identify exposure to floods, storms and wildfires. Motor insurers can use telematics to understand driving patterns, while commercial insurers can combine multiple sources of information to assess business risks.
This is gradually moving insurance from a largely retrospective model towards predictive risk assessment.
Instead of asking only what happened previously, insurers can increasingly look for signals that indicate what might happen next.
AI Is Changing Insurance Underwriting
Underwriting is one of the most important areas where AI and automation are making an impact.
Traditional underwriting can require employees to review extensive documents, financial information, claims histories and risk characteristics. AI systems can process much of this information more rapidly and highlight factors that deserve attention.
Natural language processing can extract relevant information from contracts, reports and other documents. Machine-learning models can analyse historical information to identify patterns associated with particular risks.
The benefit is not necessarily the elimination of the human underwriter.
Instead, AI can act as a decision-support system, helping professionals spend less time collecting and reviewing information and more time assessing complex risks.
Swiss Re has highlighted the growing role of AI in underwriting, including predictive underwriting models that can support risk assessment while keeping responsible use and human expertise at the centre.
Claims Automation Could Improve Customer Experience
Claims are often the most important interaction between an insurer and its customer.
A conventional claim may involve document collection, policy verification, damage assessment, fraud checks, approvals and settlement. Each additional manual step can add time to the process.
Automation can connect these stages and reduce repetitive administrative work.
AI can extract information from claim forms and supporting documents. Computer vision can assist with analysing images of damaged vehicles or property. Automated workflows can then route simple claims through faster processes while sending complicated cases to experienced claims professionals.
The potential benefit is significant for both insurers and customers.
Customers can receive faster updates and settlements, while insurers can reduce the amount of employee time spent on routine processing.
The technology is already moving beyond small-scale experiments. McKinsey’s 2026 technology research cites UK insurer Aviva’s deployment of more than 80 AI models across its claims journey, with improvements including a reduction in liability-assessment time and greater routing accuracy.
AI Is Making Fraud Detection More Sophisticated
Insurance fraud is another area where data analytics can provide an advantage.
Traditional fraud detection often depends on predefined rules. While these rules remain useful, sophisticated fraud can involve relationships and patterns that are difficult to identify manually.
Machine-learning systems can examine large datasets to identify unusual connections between claims, customers, service providers, locations and previous incidents.
The aim is not to automatically label every unusual claim as fraudulent. Instead, AI can help investigators prioritise cases that require closer examination.
This can make fraud teams more efficient and allow experienced investigators to focus their attention where it is most needed.
Generative AI Is Expanding Insurance Automation
Generative AI is taking automation beyond traditional rules-based processes.
Insurance employees can use generative AI to summarise documents, search internal knowledge bases, draft communications and assist with underwriting and claims workflows.
The opportunity is particularly significant because insurance involves enormous volumes of documents and information.
McKinsey describes the insurance sector as particularly suited to AI-driven transformation because of its extensive use of data and the workflow inefficiencies present across the industry.
However, insurers cannot treat generative AI as a simple plug-and-play technology.
An incorrect AI-generated response may be inconvenient in some industries, but an incorrect interpretation of an insurance policy could have serious financial consequences. Human review, secure data environments and appropriate governance therefore remain essential.
Insurance AI Investment Is Growing
The growing interest in AI is also reflected in insurers’ technology spending.
Swiss Re estimates that insurers globally allocated approximately 3% to 8% of their IT budgets to AI capabilities in 2025. However, fewer than 5% of insurers in its sample of 187 major insurers had disclosed a financial impact from these investments.
This highlights one of the biggest challenges facing the industry: moving from experimentation to measurable business value.
Insurers may launch numerous AI pilots, but the real test is whether those projects improve underwriting productivity, reduce claims costs, increase customer satisfaction, strengthen fraud detection or create measurable revenue opportunities.
The next stage of insurance technology will therefore be less about experimenting with AI and more about integrating it into core business processes.
Customer Service Is Becoming Digital-First
Customer expectations have changed across financial services.
Consumers increasingly expect to purchase products online, receive instant updates, access documents digitally and complete routine transactions through mobile devices.
Insurance providers are responding with digital onboarding, chatbots, virtual assistants, automated policy servicing and online claims platforms.
These tools can handle routine enquiries and reduce pressure on customer-service teams.
But digital-first does not necessarily mean human-free.
Customers dealing with major claims, complicated policies or sensitive financial circumstances may still want to speak with an experienced professional.
The likely direction for the industry is therefore a combination of automated convenience and human support.
Personalised Insurance Is Becoming More Practical
Data and automation are also changing how insurers approach personalisation.
Instead of treating large groups of customers in broadly similar ways, insurers can increasingly use behavioural and contextual information to develop more tailored products.
Telematics-based motor insurance is one example. Driving behaviour can be analysed to support usage-based or behaviour-based insurance models.
Connected devices can also help identify risks in homes and businesses. A sensor that detects a water leak, for example, could potentially allow preventive action before a minor issue becomes a major claim.
This points towards an important evolution in insurance: moving from simply compensating customers after losses to helping prevent losses in the first place.
India Offers a Significant Growth Opportunity
The global technology transformation is particularly relevant to India.
Swiss Re expects India’s insurance market to record 6.9% annual real premium growth between 2026 and 2030, placing the country among the fastest-growing major insurance markets.
India’s expanding digital economy provides an important foundation for this growth. Digital payments, mobile applications, online distribution and electronic documentation are making it easier for insurers to reach customers and manage policies digitally.
Technology could also help insurers address the economics of reaching a much larger customer base.
This is particularly relevant to segments such as health, motor, life, agriculture and microinsurance, where digital onboarding, automated underwriting and efficient claims management can help expand access.
For India, therefore, AI and automation are not only tools for improving efficiency. They could also become important enablers of insurance inclusion and market expansion.
The Legacy Technology Problem
Despite the opportunities, many insurers still face a fundamental obstacle: legacy technology.
Large insurance companies may have systems that have evolved over decades. Customer, policy and claims information can be spread across different applications and databases.
This makes it difficult to create a unified data environment for AI.
Data quality is equally important. AI models depend on reliable information. Fragmented, outdated or inconsistent data can produce unreliable results regardless of how sophisticated the underlying model is.
For insurers, transformation therefore requires more than purchasing AI software. It involves modernising technology infrastructure, integrating data, redesigning workflows and establishing effective data governance.
Agentic AI is also beginning to attract attention as a possible tool for modernising insurance technology. McKinsey says agentic AI could help insurers coordinate discrete technology-modernisation tasks while retaining auditable outputs and human-in-the-loop controls.
Human Expertise Will Remain Important
Despite rapid advances in automation, insurance will remain a business where human judgement matters.
Complex underwriting decisions, disputed claims, unusual risks and sensitive customer situations cannot always be reduced to a standard workflow.
AI can process information at a scale that humans cannot. People, however, remain essential for context, judgement, empathy and accountability.
Swiss Re’s research similarly indicates that insurers are primarily pursuing workforce augmentation rather than complete automation, using AI to support professionals rather than simply replace them.
The future is therefore likely to be human-led and technology-enabled insurance.
Responsible AI Will Become a Competitive Requirement
As insurers depend more heavily on AI, questions around trust and governance will become increasingly important.
Insurance providers must consider:
- Data privacy and consent
- Cybersecurity
- Algorithmic bias
- Model accuracy
- Explainability
- Regulatory compliance
- Human oversight
These considerations are particularly important because insurance decisions can directly affect people’s finances, health coverage, businesses and assets.
A fast automated decision is not necessarily a good decision if customers cannot understand it or if the underlying model produces unfair outcomes.
For an industry built on trust, responsible AI could become as important as technological capability itself.
Key Trends Shaping Insurance Technology
Several technology trends are likely to influence the global insurance industry over the coming years:
- AI-powered underwriting: Faster analysis of customer and risk information.
- Automated claims: Reduced paperwork and faster processing of straightforward claims.
- Predictive analytics: Earlier identification of risks and potential losses.
- Telematics: More personalised motor insurance based on actual behaviour.
- Generative AI: Automated document analysis, communication and knowledge management.
- Agentic AI: Greater automation and coordination of complex workflows.
- Fraud analytics: More sophisticated identification of unusual claims and relationships.
- Digital distribution: Easier policy purchases and servicing through online channels.
- Preventive insurance: Using connected devices and real-time data to reduce losses before they occur.
The Next Era of Global Insurance
The insurance industry is moving beyond basic digitisation. Data, AI and automation are becoming strategic components of the insurance operating model, influencing everything from risk assessment and underwriting to claims, fraud detection and customer engagement.
The global market is entering this transformation from a position of resilience, while emerging markets such as India offer substantial growth opportunities. At the same time, insurers are under pressure to demonstrate that technology investments deliver measurable improvements.
The winners are unlikely to be the companies that simply adopt the most AI tools. They will be the insurers that combine high-quality data, modern infrastructure, intelligent automation, responsible AI and human expertise.
The future of insurance will not be about machines replacing people. It will be about using technology to help insurers understand risk faster, make better decisions and respond to customers more effectively.
As the risk landscape becomes more complex, the ability to turn data into timely and trustworthy decisions could become one of the industry’s most important competitive advantages.
