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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallInsurance technology companies, usually called insurtech, are changing insurance across the whole customer and operating cycle: how policies are sold and serviced, how risk is measured and priced, how losses are prevented, and how claims are handled. The main enablers are big data, connected devices, mobile tools, artificial intelligence, and automation. The change is real but uneven. Industry surveys published in 2026 show that scaled, production use of AI is still the exception, so the new model is best understood as a shift in capabilities and business relationships rather than a finished transformation that every insurer has completed.
Where insurtech changes the insurance lifecycle
Insurtech is not simply an app or an online quote form. The National Association of Insurance Commissioners (NAIC) describes its effects on distribution and policy service as well as on underwriting and claims. The table below shows what changes at each stage, from the insurer’s side and the customer’s side.
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| Lifecycle stage | What changes for the insurer | What changes for the customer |
|---|---|---|
| Distribution and policy service | Chatbots can answer routine billing, policy, and claim questions without a staff member | Routine questions and some servicing tasks can be handled in a mobile app or chat window |
| Risk measurement and pricing | Machine learning produces risk scores and rate-factor relativities; device data can be added to rating | Pricing can reflect individual driving or usage behavior, but only where the customer enrolls in such a program |
| Loss prevention | Sensor data can flag leaks, smoke, or unusual activity earlier than a claim would reveal them | An early alert gives the policyholder a chance to act before damage grows |
| Claims | Accident-image analysis and settlement-value estimation can speed handling; fraud detection can flag suspicious files | Documents and photos can be submitted from a phone, and claim status can be tracked digitally |
| Life and health | AI supports marketing, policy issuance, underwriting, prior authorization, claims adjudication, and risk adjustment | Some offerings use wearables in wellness programs, where they are offered |
Connected devices as new data inputs
Connected devices are the most visible new source of information in insurance programs. The NAIC gives three consumer-facing examples. Each is a use case, not a guarantee that a particular insurer offers it, that a customer qualifies, or that a discount follows.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesTelematics and usage-based auto insurance
Usage-based auto insurance uses telematics to track driving habits and tailor pricing to them. The insurer collects data on how the vehicle is driven, and that data becomes part of the rating process for participating drivers. Whether a given driver’s premium falls, rises, or stays the same depends on the program’s rules and the driver’s record, so the customer should ask the insurer how the program is scored before enrolling.
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Smart-home sensors and loss prevention
Smart-home devices can detect leaks, smoke, or unusual activity and support loss prevention. A smart water leak detector is the clearest example in this category: it alerts the homeowner to a leak while the damage is still small. Buying a sensor does not by itself change a policy’s eligibility, coverage, or premium. Those depend on the insurer’s program terms.
Wearables in wellness and insurance offerings
Some life and health offerings use wearables in wellness programs. The NAIC describes this as a use case within insurance products rather than a universal feature, so the terms of any specific program determine what data is collected and how it is used.
AI inside insurer operations
The NAIC describes AI uses in insurer operations that go well beyond customer-facing tools. These are reported applications, not evidence that every insurer uses them.
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Machine learning is used to produce risk scores and rate-factor relativities, which help insurers decide how much each risk characteristic should affect a price. Because these models learn from historical data, their outputs are only as fair and accurate as the data and the monitoring behind them.
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Claims and fraud
In claims, AI is used for accident-image analysis, for estimating claim settlement values, and for fraud detection. Image analysis can speed the first assessment of a vehicle or property loss, while fraud models flag files for a human to review. Neither replaces claims adjusters on complex or disputed claims, and the flag itself is not a finding of fraud.
Life and health uses
In life insurance, the NAIC identifies AI uses in marketing, policy issuance, and underwriting. In health insurance, it lists prior authorization, claims adjudication, fraud detection, and risk adjustment. Health-related decisions carry the highest consequence for policyholders, which is why the governance questions in the regulation section below matter most in these lines.
How far has adoption gone?
Several 2026 publications measure different things: investment, funding, scaled use, and workforce use. The table reports each figure with its publisher, year, and population, because the figures are not comparable with one another and should not be combined into a single adoption rate.
| Publisher and year | Finding | Population or definition |
|---|---|---|
| Gallagher Re, 2026 report summary | USD 5.08 billion in global InsurTech investment in 2025, up 19.5% year over year; described as the first annual increase since 2021 | Investment flows into InsurTech companies; measures capital, not insurer adoption |
| Gallagher Re, 2026 report summary | 77.9% of Q4 2025 InsurTech funding went to AI-centered companies | Share of funding in one quarter; measures where investors put capital |
| Capgemini Research Institute, 2026 World Property & Casualty Insurance Report | 10% of P&C insurers had successfully scaled AI; 42% tracked no AI metrics; 60% remained in exploration or proof-of-concept stages | 344 senior insurance executives, 809 insurance employees, and 1,113 policyholders across the Americas, Europe, and Asia-Pacific |
| NTT DATA, 2026 report announcement | 22% of insurers had scaled AI to production; 66% of the insurance workforce had adopted AI tools | Industry report findings; the announcement does not describe a regulator survey, and it contrasts company-level scaling with individual workforce use |
| NAIC, March 2026 | The NAIC AI Systems Evaluation Tool was being piloted by 12 participating states | U.S. state insurance regulators participating in the pilot |
NTT DATA’s Global Head of Insurance, Bruno Abril, framed the report’s launch this way: “The insurance industry is facing structural shifts in the face of unprecedented market volatility and uncertainty. There are, however, clear opportunities for insurers to embrace AI-driven solutions to bolster trust and resilience.” That is a company report-launch statement, not an independent finding.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Benefits and risks for consumers and insurers
The same data practice that brings a benefit often carries a risk. The table pairs each insurtech practice with its main benefit and its main concern.
| Practice | Main benefit | Main risk or concern |
|---|---|---|
| Telematics for usage-based auto pricing | Pricing can track an individual’s driving rather than broad group averages | Collection of sensitive driving data; limited transparency about how it is used |
| Smart-home sensors | Early detection of leaks, smoke, or unusual activity can reduce losses | Sensitive household data; cybersecurity exposure of connected devices |
| Chatbots and mobile claim tools | Faster answers and simpler submission and tracking | Sensitive personal data passed through digital channels; cybersecurity exposure |
| AI in pricing, claims, and underwriting | Faster handling, and pricing that reflects more data | Potential bias in AI-supported decisions; limited transparency about how decisions are reached |
For insurers, the opportunity comes with compliance responsibility. The uses above must be run within the insurance laws, standards, and consumer-protection rules that already apply, and the operational gap between pilots and production use is the main practical challenge the 2026 publications describe.
Regulatory accountability when insurers use AI
The NAIC states that insurers remain responsible for complying with applicable insurance laws, standards, and consumer-protection rules when they use AI. Regulators may require an explanation of how AI informs underwriting, pricing, marketing, or claims decisions, and human oversight remains important throughout.
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- Model Bulletin on the Use of Artificial Intelligence by Insurance Companies. The NAIC reports that it was adopted in December 2023. It sets expectations for insurer AI governance and describes the information a state insurance department may request during an investigation or examination. A model bulletin is a template that individual state departments decide whether to adopt, so check the status in the state where a given insurer is licensed.
- AI Systems Evaluation Tool. As of March 2026, the NAIC said the tool was being piloted by 12 participating states. Its NAIC page described adoption as anticipated at the 2026 Fall National Meeting. Because that meeting may already have occurred by the time you read this, confirm the current status on the NAIC’s own site before treating the adoption as complete.
- Geographic scope. The NAIC material is U.S.-focused. It does not describe the insurance law of other countries, and rules in the European Union, the United Kingdom, or Asia-Pacific markets may differ.
How to compare insurance technology providers
If you are evaluating a specific insurtech provider or program, the NAIC’s consumer and regulatory material suggests a practical set of criteria. These are editorial comparison criteria drawn from the applications and governance issues the NAIC describes. They are not a ranked vendor list, and no performance claims for individual products are established here.
Quick Recap
- Job performed: distribution, policy administration, underwriting, claims, fraud detection, or loss prevention.
- Integration: how the tool connects to the insurer’s existing systems and to partners, and what breaks if one connection fails.
- Data: what data is collected, from which devices or sources, who can access it, and how long it is kept.
- Model oversight: how the models are monitored for accuracy and bias, and whether their decisions can be explained to a regulator or policyholder.
- Human review: where a person can review, override, or escalate an automated decision.
- Geography and line of business: where the tool is licensed to operate and which insurance products it supports.
- Operational evidence: documented results from live deployments, requested in writing, rather than pilot-stage claims.
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