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Insurance technology companies are changing insurance across the whole customer and operating lifecycle: how policies are sold and serviced, how risk is measured and priced, how losses are prevented, and how claims are handled. The drivers are big data, connected devices, mobile tools, AI, and automation. What is changing is a set of capabilities and business relationships. That does not mean every insurer has gone digital, and it does not mean technology automatically improves outcomes for customers.
Insurtech is more than an app or an online quote
The most visible part of insurtech is a smartphone app or a quote form, but that is only the front edge. Distribution and policy service are affected, and so are underwriting, pricing, loss prevention, and claims. The table below maps each stage of the lifecycle to the example applications described by the U.S. National Association of Insurance Commissioners (NAIC).
| Lifecycle stage | What changes | Example applications described by the NAIC |
|---|---|---|
| Distribution and policy service | Routine questions, document submission, and claim tracking move to digital channels | Chatbots that answer routine billing, policy, or claim questions; mobile apps and photo-based tools for submitting documents and tracking claims |
| Underwriting and pricing | Risk is scored using more data, including data from connected devices | Machine learning for pricing risk scores and rate-factor relativities; usage-based auto pricing using telematics |
| Loss prevention | Sensors give earlier warning that something has gone wrong | Smart-home devices that detect leaks, smoke, or unusual activity |
| Claims and fraud | Images and models assist assessment and detection | Accident-image analysis; estimating claim settlement values; fraud detection |
Connected devices bring new data into insurance
Connected devices are the element that makes insurance look more like an ongoing service rather than a yearly document. The NAIC describes three consumer-facing patterns. Each is a use case. None guarantees that a given insurer offers it, or that a customer will receive a discount.
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Usage-based auto insurance
Telematics tracks driving habits so an insurer can tailor auto pricing to how a vehicle is actually driven. Enrollment, the data collected, and how driving behavior changes a premium are set by each program’s terms.
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Smart-home sensors
Smart-home devices can detect leaks, smoke, or unusual activity and support loss prevention. The value is early warning: a problem flagged on the day it starts is usually easier to contain than one found days later. A smart water leak detector is a typical example of this sensor type. Buying one does not change insurance eligibility, coverage, or premiums, and this article does not verify which models any insurer recognizes.
Wearables in wellness and insurance offerings
Some life or health offerings use wearables in wellness programs. What a wearable is used for, and whether it can affect a policy at all, is defined by the individual program.
AI is being applied inside insurers
Beyond customer-facing tools, the NAIC describes AI in insurer operations across pricing, underwriting, claims, and fraud. These are reported applications. They show where insurers are directing AI, not how widely each one has reached full production, and the adoption figures later in this article show that deployment is uneven.
Pricing and underwriting
Machine learning is used to build pricing risk scores and rate-factor relativities. In life insurance, the NAIC describes AI in marketing, policy issuance, and underwriting.
Claims and fraud
In claims, AI can analyze accident images, support estimates of claim settlement value, and detect possible fraud.
Health insurance
The NAIC’s health examples include prior authorization, claims adjudication, fraud detection, and risk adjustment. These decisions determine coverage and payment, which makes the governance questions in the sections below most pressing in this area.
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How far adoption has actually reached
Several recent reports measure different parts of this shift. Each is useful on its own, but they use different populations, definitions, geographies, and methods, so each figure should be read with its publisher and year.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →| Publisher and year | What was measured | Figure | Sample or scope |
|---|---|---|---|
| Gallagher Re, 2026 report summary | Global InsurTech investment in 2025 | USD 5.08 billion, up 19.5% year over year; described as the first annual increase since 2021 | Not stated in the summary |
| Gallagher Re, 2026 report summary | Share of Q4 2025 InsurTech funding going to AI-centered companies | 77.9% | Q4 2025 funding; sample not stated in the summary |
| Capgemini Research Institute, 2026 World Property & Casualty Insurance Report | P&C insurers that had successfully scaled AI | 10% had 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 | Insurers that had scaled AI to production | 22% | NTT DATA report findings; sample not stated in the announcement |
| NTT DATA, 2026 report | Insurance workforce that had adopted AI tools | 66% | Same NTT DATA report; a measure of tool use by employees, not of insurers’ production deployments |
| NAIC, March 2026 | States piloting the AI Systems Evaluation Tool | 12 participating states | U.S. state insurance regulators |
These numbers do not add up to a single adoption rate, and they should not be averaged. Taken together, the funding figures show capital moving quickly toward AI, while the operating figures show that most insurers are still short of production use.
NTT DATA’s Bruno Abril, Global Head of Insurance, said in the report announcement: “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.” This is a company’s launch statement, not an independent finding.
What consumers gain and what they give up
The NAIC lists convenience, faster service, tailored pricing, and loss prevention as possible benefits. The same body identifies material risks that customers should weigh alongside them.
- Possible gains: more convenient service, faster handling of routine requests, pricing that reflects individual use, and earlier warnings that can prevent losses.
- Material risks: collection of sensitive personal data, cybersecurity exposure, potential bias in AI-supported decisions, and limited transparency about how data is used.
The available evidence does not establish that every insurer delivers these benefits, that pilots produce savings, or that premiums will fall.
What insurers remain accountable for
The opportunity comes with compliance responsibility. The NAIC states that insurers remain responsible for 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.
The NAIC Model Bulletin on AI
The NAIC’s Model Bulletin on the Use of Artificial Intelligence by Insurance Companies was adopted in December 2023. It sets expectations for insurer AI governance and describes the information a department may request during an investigation or examination.
The AI Systems Evaluation Tool
The NAIC said the AI Systems Evaluation Tool was being piloted by 12 participating states as of March 2026, with adoption anticipated at the 2026 Fall National Meeting when that page was written. That meeting may already have taken place, so check the NAIC website for a current update before treating adoption as settled.
Where this guidance applies
The NAIC material is U.S.-focused. Insurers and consumers elsewhere should consult their own regulator’s rules; this article does not survey insurance law in other countries.
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How to compare real insurtech providers or programs
The sources do not establish a verified vendor shortlist or comparable performance claims for individual products. When you assess a specific tool or program, use the following questions, which follow from the applications and governance issues described above.
- Job performed: Is it handling distribution, policy administration, underwriting, claims, fraud, or loss prevention?
- Integration: How does it connect to the insurer’s existing systems and to its partners?
- Data: What data does it collect, and who can access it?
- Model oversight: How are its models monitored, and can their outputs be explained?
- Human review: Where can a person review or override an automated decision, and how does escalation work?
- Scope: Which geography and line of business does it cover?
- Evidence: What operational results are documented, who measured them, and when?
The Bottom Line
Insurance technology is changing how insurance is sold, priced, serviced, and claimed, and capital is flowing into it quickly. The operating evidence is still early for most insurers, so treat any vendor’s promise of savings or speed as a claim to verify against the questions above and the governance rules that apply where you live.
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