Five years ago, the defining infrastructure problem in fintech was building the plumbing beneath payments and banking — the abstraction layers that let a startup issue virtual cards, hold balances, or initiate ACH transfers without becoming a regulated bank. Stripe, Marqeta, and their European equivalents built those layers. The result was not that fintech startups all looked the same; it was that they could focus their differentiation on product rather than infrastructure. Insurance is now at the same inflection point, and the infrastructure layer is being built with similar patterns, slightly different constraints.
What API-First Insurance Infrastructure Actually Means
When we talk about insurance-as-a-service infrastructure, we mean the stack of components that sit between a carrier's balance sheet and the point of distribution — the APIs that handle quote generation, policy issuance, premium collection, endorsement processing, claims intake, and document management, exposed in a way that any distribution partner can integrate without needing to understand the carrier's core policy administration system.
In principle, every carrier should want this. Efficient distribution is in their interest. In practice, most carrier core systems were built for internal processing by trained underwriting staff, not for external API consumption at volume and speed. The policy administration systems that dominate the European market — Guidewire, Duck Creek, and various proprietary legacy systems — have APIs, but they are typically designed for batch processing and internal integrations, not for the sub-second response times that a digital distribution partner needs to embed insurance into a checkout flow.
Wakam's architecture, which we backed in Fund II, represents one approach: a carrier that is built API-first from inception, where the external API is not an afterthought on top of internal systems but is the primary interface through which all policy operations flow. That architectural choice makes a substantial difference to what distribution partners can actually build — and how quickly.
The Technical Challenges Are Not What Most People Assume
When I talk with engineers who are newly entering the insurance infrastructure space, the challenges they anticipate are usually around data security, regulatory compliance, and perhaps the complexity of insurance product logic. These are real, but they are not the hardest problems. The hardest problems are around state management and consistency guarantees in a domain where state changes have legal and financial consequences.
Consider a simple policy issuance flow. A distribution partner calls the API to issue a new motor policy. The API call succeeds — the policy is written to the carrier's system — but the confirmation response is lost in transit due to a network timeout. The distribution partner does not know whether the policy was issued. Do they retry? If they retry and the underlying system issued on the first attempt, they now have two policies for the same risk. If they do not retry and the policy was not issued, the customer believes they have cover that does not exist.
In payments, this is solved by idempotency keys — a unique token attached to each request that ensures at-most-once processing regardless of retries. Insurance infrastructure needs equivalent guarantees, but across a more complex state machine: a policy is not a single transaction but a long-lived contract with many potential state transitions (quote, bound, in-force, endorsed, cancelled, claimed against, lapsed). Building the consistency guarantees that make this state machine reliable at API scale is harder than it looks from the outside.
The related challenge is multi-party consistency. An embedded insurance product typically involves a distribution partner, an MGA (sometimes two), a carrier, and potentially a reinsurer. Each has its own system of record. A policy cancellation initiated by the policyholder through the distribution partner's app needs to propagate consistently through all four systems, with appropriate audit trail for regulatory purposes. Getting this right without introducing synchronous dependencies that kill latency is a genuinely hard distributed systems problem.
Regulatory Constraints as API Design Constraints
One aspect of insurance API infrastructure that is different from fintech API infrastructure is the regulatory constraint on what operations can be performed via API, and by whom. Insurance distribution regulation in Europe (IDD — the Insurance Distribution Directive) creates obligations that apply to the distribution chain, not just the carrier. When a digital platform distributes an insurance product, it may be acting as an insurance intermediary under IDD, with consequent registration, disclosure, and conduct requirements.
This creates an interesting API design problem: some operations that are trivially available in a fintech API context require consent flows, disclosure delivery, and record-keeping that are not simply additional fields in a request payload but are legally substantive events that need to be captured, timestamped, and retrievable for regulatory audit. The infrastructure teams who understand this from the start build it into their API contracts from the beginning. Teams who treat it as an add-on typically find that compliance retrofitting is expensive and often architecturally disruptive.
We are not saying that regulatory complexity is a reason not to build insurance infrastructure — precisely the opposite. Teams who navigate these constraints correctly build the kind of trust with carriers and regulators that creates genuine distribution network effects. A carrier will share more distribution capacity with an infrastructure partner whose regulatory handling they trust than with a faster-moving but less careful competitor.
The Qover Model and the MGA Layer
One architecture pattern worth examining is the MGA-as-infrastructure model that Qover has developed. Rather than building API infrastructure that exposes a single carrier's products, Qover built an MGA layer that can route distribution flows to multiple carriers, abstracting the distribution partner from carrier-specific policy administration complexity entirely. The distribution partner integrates once; Qover manages carrier relationships, product approvals, and claims handling on the back end.
This is architecturally similar to how multi-network payment processing works — the merchant integrates to the processor, the processor handles the network routing. The insurance analogy holds up to a point, then diverges: insurance products are not fungible the way payment rails are, and carrier selection has underwriting implications that payment network selection does not. The teams building multi-carrier insurance routing therefore need underwriting intelligence in their routing logic, not just connectivity intelligence. This is where the problem becomes technically interesting in a way that is specific to insurance rather than general fintech infrastructure.
Where the Infrastructure Investment Goes Next
The next layer of insurance API infrastructure that is not yet well-served is claims handling infrastructure — not just claims intake (FNOL APIs exist and are improving) but claims processing APIs that can exchange status, document requests, and payment instructions between the carrier's claims system and the distribution partner's customer-facing interface in real time. The gap between "claim submitted" and "policyholder has visibility into claim status" is currently measured in days at most carriers. The infrastructure to close that gap to minutes is being built, but the complexity of carrier system integration makes it harder than FNOL intake alone.
The teams building in this space in 2023 are dealing with the same greenfield infrastructure opportunity that payments infrastructure teams dealt with in 2015. The regulatory complexity is higher; the network effects are similar; the opportunity to become the layer that everyone builds on is real.