Insurtech has entered a more mature phase in 2026. Gallagher Re’s Global InsurTech Report found that global insurtech investment rose 19.5% during 2025 to 5.08 billion dollars, marking the first annual increase since 2021, and that reinsurers and insurers made 162 private technology investments into insurtechs, more than in any prior year, a shift the report characterizes as a changing of the guard toward strategic industry investment.
Insurance companies are still investing in artificial intelligence, automation, cloud platforms, and data infrastructure, with roughly two-thirds of that funding going to AI-centered insurtechs. The bigger shift is where those technologies are being applied, as insurtech moves deeper into underwriting, claims, risk management, distribution, and the development of entirely new insurance products.
The focus is also becoming more commercial, with insurers and insurtechs under greater pressure to prove that technology can improve efficiency, strengthen risk selection, respond faster to customers, and support sustainable growth.
AI Is Moving Into Core Insurance Workflows
AI is becoming part of insurance professionals’ day-to-day work. A February 2026 report from the European Insurance and Occupational Pensions Authority, based on 347 insurers across 25 countries, found that nearly two-thirds of undertakings are already actively using generative AI, with 64% of reported use cases targeting back-end productivity tasks such as extracting data from invoices, audio recordings, and medical reports, along with underwriting assistants.
Underwriting is one of the clearest examples. Commercial underwriters can spend considerable time reviewing submissions, extracting information from documents, requesting missing data, and completing repetitive administrative tasks.
AI can take on more of this work, giving underwriters more time to focus on complex risks, portfolio decisions, and customer relationships. The same shift is taking place in claims, where AI can help classify claims, extract information, identify inconsistencies, and support faster decisions.
AI Is Creating New Insurance Risks
AI is also creating new risks for insurers and their customers. Businesses are using AI to make decisions, generate content, interact with customers, and perform increasingly complex tasks. When something goes wrong, traditional liability models may not always provide an obvious answer. Who is responsible when an AI system causes financial damage?
What happens when an automated system makes a decision that creates a regulatory or legal problem? How should insurers price risks when historical loss data is limited? Empirical legal analysis published by the American Bar Association captures this uncertainty, describing how silent AI, meaning AI exposures not explicitly included or excluded in traditional liability policies, exposes coverage gaps, and how existing lines such as cyber insurance and technology errors and omissions help but may not be sufficient over the long term.
These questions are creating opportunities for specialized insurance products covering AI-related liability and other emerging exposures, some of which already underwrite in ways that depart from the traditional reliance on historical data. The challenge for insurtech companies is significant because they need to model risks that can change faster than traditional actuarial datasets, making real-time monitoring, better data, and dynamic risk assessment increasingly important.
Embedded Insurance Is Becoming More Sophisticated
Embedded insurance is evolving from a distribution concept into a broader insurance model. Instead of requiring customers to visit an insurer or broker separately, insurance can be built directly into a product, service, transaction, or digital platform. The experience can feel like a natural part of the purchase rather than a separate step.
Actuarial analysis from the Society of Actuaries notes that embedded distribution works best when the purchase of a product or service is closely linked to the buyer’s risk of loss, and that insurers must carefully consider the effects on ratemaking and reserving. For insurtech companies, this creates opportunities to connect insurance products to platforms through APIs and automated processes, so coverage can be offered at the point where a relevant risk or purchase occurs while underwriting and claims happen in the background.
The key question in 2026 has shifted from whether insurance can be embedded to whether the model works economically. Successful embedded insurance still depends on accurate pricing, reliable data, effective claims handling, and clearly defined roles between insurers, technology providers, platforms, and customers.
Parametric Insurance Is Expanding
Parametric insurance remains one of the most interesting areas of insurtech because it changes how certain risks can be covered.
The model does come with challenges. A 2026 peer-reviewed study of parametric insurance finds that basis risk remains a major concern, alongside modeling that depends heavily on the availability and quality of external data and the greater pricing complexity that follows. Trigger design, data quality, and basis risk therefore all need careful consideration. The opportunity lies in finding risks where the event itself can be measured clearly enough to support a useful insurance product.
Cyber Insurance Is Becoming More Data-Driven
Cyber insurance continues to evolve as the underlying risk becomes more complex. Businesses now rely on increasingly connected technology environments, creating more potential entry points for attacks and more complicated consequences when incidents occur.
Industry analysis reported at the end of 2025 argued that cyber risk in 2026 will be defined less by isolated breaches and more by hidden interdependencies that drive correlated, systemic loss, rooted in shared software, common vulnerabilities, and concentrated cloud reliance. This is pushing insurers toward better ways of measuring cyber exposure, as static questionnaires and periodic assessments give way to approaches that incorporate more continuous information about an organization’s technology environment and security posture.
AI adds another layer of complexity. Autonomous systems can introduce risks that don’t fit neatly into traditional cyber definitions, and an AI system could cause damage without a conventional breach, raising new questions about coverage and liability. For insurtech companies, this creates opportunities in continuous risk monitoring, automated assessments, threat intelligence, and more dynamic cyber insurance products.
Data and Infrastructure Are Becoming the Insurtech Battleground
Behind almost every major insurtech trend is the same fundamental issue: data. AI cannot perform well when critical information is fragmented across legacy systems. Automated underwriting becomes difficult when data cannot move between platforms.
Digital claims experiences suffer when policy, customer, and claims information remain isolated. Industry survey data reflects the scale of the problem: a 2025 study of 200 C-suite insurance leaders found that 95% of insurance professionals face significant challenges with legacy systems, and that data-related issues are the most pressing, led by data security and privacy, data quality, and integration difficulties.
That is why modernization is becoming a central part of the insurtech conversation. Insurers are investing in APIs, cloud infrastructure, data platforms, core system modernization, and stronger integration capabilities. These technologies may be less exciting than a new AI application, but they are often what makes those applications possible. This creates opportunities for insurtech companies that solve infrastructure problems as well as customer-facing ones.
Insurtech Beyond 2027
As insurtech moves beyond 2027, its role will extend well beyond digitizing existing insurance processes. The next phase will be defined by how effectively technology changes the way insurers assess risk, design coverage, distribute products, and respond to loss.
AI will become more deeply embedded in underwriting and claims. Real-time data will support more dynamic risk assessment. Parametric products will expand into new areas where traditional coverage is difficult to apply. Embedded insurance will become a more established distribution model, while emerging exposures such as AI liability will create demand for entirely new forms of coverage.
The bigger shift will be toward insurance built around continuously changing risk. Insurtech companies that combine data, automation, analytics, and modern infrastructure will be better positioned to help insurers price risk with greater precision, respond faster to disruption, and develop products for exposures that traditional models struggle to address.
Beyond 2027, insurtech will increasingly become part of the core insurance value chain, shaping how risk is understood, transferred, and managed.
