What Hidden Growth Layers Exist in the U.S. Insurtech Market Value Chain?

Insurance in the U.S. has moved well past being a single, linear business built around agents, paperwork, and annual renewals. It has become a layered technology stack, where every stage of the policy lifecycle, from product design to claims settlement, now runs through software, data pipelines, and increasingly, artificial intelligence. As per Kings Research, the U.S. insurtech market size was valued at USD 17.70 billion in 2025 and is projected to reach USD 54.84 billion by 2033, representing a CAGR of 15.43% over the forecast period. This growth has come from places most industry commentary overlooks, especially the infrastructure, platform, and monetization layers sitting underneath the digital-first insurers that tend to get all the attention.

Understanding where value is actually created across this stack, rather than simply citing top-line growth figures, is what separates a surface-level view of the U.S. insurtech sector from a working knowledge of it. This piece breaks the value chain into its component layers, examines where automation is delivering measurable returns, and flags the structural friction still holding parts of the ecosystem back.

What Defines the U.S. Insurtech Market Value Chain?

At its core, insurtech refers to the ecosystem of technology-driven solutions that improve how insurance products are designed, sold, underwritten, and serviced. The value chain runs through five connected stages: product design, underwriting, distribution, claims, and retention. Each stage used to be handled in isolation by separate teams and separate systems; today, data flows continuously between them, which is precisely what unlocks new growth layers instead of just faster versions of old processes.

This end-to-end model spans every major insurance line, including property, casualty, life, and health, and involves a wide range of distribution formats: direct-to-consumer digital platforms, comparison aggregators, digital brokers, managing general agents (MGAs), embedded insurance channels, and traditional agent-assisted networks that continue to coexist alongside digital-first models.

Where Is Value Created Across the Insurtech Stack?

Rather than treating insurtech as one undifferentiated category, it helps to separate it into three functional layers, each with a distinct growth profile.

Infrastructure layer: This is the foundation, made up of cloud hosting, APIs, and core data systems that let insurers and insurtech firms exchange information in real time. Carriers migrating from on-premises environments to cloud-native cores are doing so specifically to ease the maintenance burden that legacy technology places on IT budgets, freeing resources for genuine innovation rather than upkeep.

Platform layer: Sitting above infrastructure are policy administration systems and digital marketplaces that connect carriers, brokers, and MGAs. These platforms increasingly function as neutral distribution rails, letting multiple insurance products reach customers through a single technical integration rather than dozens of bespoke connections.

Application layer: This is the customer-facing surface, made up of mobile apps, chatbots, self-service portals, and claims automation tools. It is the most visible layer of the stack, but its growth increasingly depends on the strength of the infrastructure and platform layers underneath it, a point often missed by analyses that focus only on consumer apps.

How Is Underwriting Automation Unlocking New Growth?

Underwriting has moved furthest from its manual origins. AI and machine learning models now process large volumes of structured and unstructured data, from telematics feeds to property imagery, to score risk with far greater granularity than static actuarial tables allowed. Generative and agentic AI tools are being layered on top of these models to extract and summarize claims-relevant information, supporting faster, more consistent underwriting decisions.

This shift shows up directly in federal labor data. The U.S. Bureau of Labor Statistics projects employment of insurance underwriters to decline by 3% between 2024 and 2034, even as the occupation is still expected to generate roughly 8,200 average annual openings from replacement needs. The agency attributes the decline specifically to automated underwriting software, which lets fewer underwriters process a larger volume of applications, a clear labor-market signal of how deeply automation has moved into core underwriting work.

Regulators are treating this shift as an oversight priority rather than a side issue. According to the NAIC's own Journal of Insurance Regulation, 24 states had adopted the Model Bulletin on the Use of Artificial Intelligence Systems by Insurers as of August 2025, establishing governance, documentation, and audit requirements for AI-driven underwriting decisions across a majority of the country's insurance activity.

What Role Does Distribution Innovation Play in Market Expansion?

Distribution has traditionally been the most agent-dependent stage of insurance, and it remains where the highest volume of insurtech investment is concentrated. Embedded insurance, where coverage is offered directly at the point of sale inside e-commerce, travel, fintech, or mobility platforms, is changing how policies are discovered and purchased, moving the transaction away from a dedicated shopping trip to an insurer's website.

API-based distribution complements this by letting AI systems assess individual customer needs and surface relevant coverage options in real time. This transition is layering new, faster on-ramps for customers alongside traditional agent- and broker-assisted channels, reflecting generational shifts in how people research and purchase financial products.

How Are Claims Becoming a Strategic Differentiator?

Claims handling has quietly become one of the clearest proof points of insurtech's return on investment, in part because the cost of doing it manually is so visible on an insurer's books. Automated first notice of loss (FNOL) systems can now capture, structure, and route claims data in real time, flagging inconsistencies and directing files to the right adjuster without manual triage. Conversational AI tools deployed for claims intake have been documented handling a substantial share of FNOL submissions, cutting operational costs and reclaiming meaningful adjuster time each month.

Fraud detection has advanced in parallel. Machine learning models applied at the point of FNOL can flag anomalies before a claim is fully processed, predict claims complexity, and anticipate re-openings, functions that used to depend almost entirely on adjuster experience and after-the-fact audits.

Blockchain-based data exchange is an emerging layer within claims as well. Shared, tamper-resistant ledgers allow multiple insurers and reinsurers to validate claims data without duplicating manual verification work, shortening the time between loss notification and settlement.

Claims volume across the industry gives a sense of scale here. According to the OECD's Global Insurance Market Trends 2025 report, the non-life sector recorded an average growth rate of 8.2% in gross premiums written in nominal terms during 2024, while gross claims payments grew by 7.5% on average in nominal terms over the same period, underlining how much operational weight sits on the claims function as premium volume keeps expanding.

A Worked Example: What Automation Actually Saves

To make this concrete, consider a mid-sized carrier processing 100,000 claims a year at an average manual processing cost of $350 per claim, a baseline claims-handling spend of $35 million annually.

• If automation lifts straight-through processing to cover 40% of claims volume, and each automated claim costs roughly 55% less to process than a manually handled one, the carrier saves close to $7.7 million a year in direct processing cost alone.
• Layer in a fraud-detection improvement that catches even 2 additional percentage points of fraudulent claims and the incremental savings compound further, before any gains in customer retention from faster payouts are counted.

The exact figures will vary by carrier, book of business, and claims mix, but the direction is consistent: automation savings scale with claims volume, which is why claims has become a strategic differentiator rather than just a cost center to be minimized.

What New Revenue Streams Are Emerging in the U.S. Insurtech Market?

Beyond efficiency gains, a genuinely new monetization layer is forming, one that most coverage of this space skips over in favor of adoption statistics. Three revenue models stand out:

• Subscription and usage-based pricing, where premiums flex with real-time behavior (driving patterns, health metrics, business activity) rather than being fixed at renewal.
• B2B SaaS licensing, where technology built for one insurer's underwriting or claims stack is licensed to other carriers, turning what was once an internal cost center into a standalone product line.
• Data monetization, where anonymized and aggregated risk data generated through the underwriting and claims process is packaged for actuarial research, reinsurance pricing, or catastrophe modeling, a use case still early relative to its ceiling.

These streams matter because they change the economics of insurtech itself: platform and infrastructure providers can grow the technology layer directly, independent of building a large policyholder base of their own.

The premium base these revenue models are layered onto is sizable in its own right. According to data compiled from statutory filings by the National Association of Insurance Commissioners, U.S. property and casualty insurers generated approximately $1.06 trillion in direct premiums written in 2024, marking the first time the industry has crossed the trillion-dollar threshold. This massive premium pool represents the underlying revenue base that underwriting, distribution, and claims technologies are increasingly competing to optimize.

What Structural Challenges Limit Value Realization?

Even with this momentum, three barriers consistently show up wherever insurtech adoption stalls.

Regulatory fragmentation

Insurance regulation in the U.S. is set at the state level, a structure rooted in the McCarran-Ferguson Act of 1945, which keeps federal authorities from directly overseeing the insurance business. This means a technology rollout that is compliant in one state may need separate adjustments in another, a genuine drag on scaling standardized AI or data products nationally.

Legacy system integration

Many carriers still run core policy administration and claims systems built decades ago, systems that were designed long before modern APIs, cloud infrastructure, or AI tooling existed. A significant share of enterprise IT budgets at traditional insurers goes toward simply maintaining these systems, resources that could otherwise fund new capability.

Data privacy and security compliance 

Insurers handle some of the most sensitive personal data in financial services: health records, financial history, and property details, putting them under both federal and state-level obligations. The Gramm-Leach-Bliley Act requires state insurance regulators to set standards for the privacy and disclosure of nonpublic personal financial information, while the NAIC's Insurance Data Security Model Law adds a state-by-state cybersecurity layer, tracked by the NAIC as adopted across roughly 28 state jurisdictions as of August 2025. Firms operating across multiple states must reconcile these overlapping frameworks rather than complying with a single national standard.

Build, Partner, or Acquire: How Should Insurers Approach Insurtech Capability Gaps?

Carriers weighing how to close a capability gap generally face three paths, each with a different risk and speed profile.

• Build: in-house development gives full control over the technology and data, but takes the longest to deliver and carries the highest execution risk, particularly for AI-native capabilities that require specialized talent.
• Partner: integrating with an existing insurtech platform or MGA is faster to deploy and lower risk upfront, though it leaves the carrier dependent on a third party's roadmap and pricing.
• Acquire: buying an insurtech firm outright compresses the timeline further and brings proprietary technology in-house immediately, though it carries integration risk and a higher upfront capital commitment.

In practice, most carriers use a mix of all three depending on the stage of the value chain: partnering for distribution and customer engagement tools that need to move fast, while building or acquiring capability in underwriting and claims, where proprietary data and models create a more durable competitive edge.

How Will the U.S. Insurtech Market Evolve Over the Next Decade?

Three forward trends look durable rather than cyclical. First, ecosystem convergence: insurance increasingly sold inside non-insurance platforms (retail, travel, mobility, fintech) rather than through standalone insurance shopping experiences. Second, platform consolidation: a smaller number of infrastructure and platform providers powering a larger share of the industry's technology stack, similar to how cloud consolidation played out in other financial services segments. Third, the rise of AI-native insurers: carriers built from the ground up around AI-driven underwriting and claims, rather than digitizing legacy processes after the fact.

Regulatory frameworks will need to keep pace with this shift, particularly as AI governance becomes its own compliance category alongside data security, sitting beside existing insurance law rather than trailing behind it.

Frequently Asked Questions

What is insurtech and how does it work in the U.S.? 

Insurtech refers to technology-driven solutions, including AI, cloud infrastructure, data analytics, and automation, applied across insurance product design, underwriting, distribution, and claims. In the U.S., it operates within a state-regulated insurance system, meaning the underlying technology must adapt to varying compliance requirements across jurisdictions.

How do insurtech companies generate revenue? 

Primarily through insurance premiums (for carriers and MGAs), but increasingly through B2B software licensing, usage-based and subscription pricing models, and data-driven services sold to other insurers or reinsurers.

What technologies are most used in insurtech? 

Artificial intelligence and machine learning for underwriting and claims triage, cloud infrastructure for scalability, APIs for embedded distribution, and, in emerging use cases, blockchain for shared claims data verification.

Is insurtech replacing traditional insurers? 

Growth is occurring largely through partnership and platform integration rather than outright displacement, with traditional carriers adopting insurtech capabilities directly or through acquisition alongside continued reliance on agent- and broker-assisted channels.

What are examples of embedded insurance? 

Coverage offered at the point of sale within non-insurance platforms: travel insurance bundled with a flight booking, device protection offered during an electronics purchase, or business insurance surfaced inside a small business banking app.

Explore the Full Market Analysis 

The growth layers covered here, across underwriting, distribution, claims, and emerging revenue streams, are explored in far greater depth in Kings Research's dedicated report on the sector. It includes detailed segmentation by product line, distribution model, and end user, along with company profiles of the key players shaping the space.

Access the full U.S. Insurtech Market Report from Kings Research.