The wrong build-or-buy call does not appear in any single quarter. It shows up as a hidden tax on speed, cost, and control that compounds for years. For insurers, this is no longer a tooling choice. It is an operating model decision that determines how quickly products ship, how reliably compliance is maintained, and how well the organization adapts when the market turns.
Most carriers are moving away from custom cores and toward cloud-native SaaS for predictable upgrades and faster delivery. Analysts report that a growing share of new core replacements in property and casualty (P&C) now select SaaS as the default path. That trend reflects a clearer view of tradeoffs that matter at scale, not a shift in fashion.
Why This Decision Is Now Board-Level
Three forces have raised the stakes. Product complexity is expanding as specialty lines and parametric triggers move mainstream. Distribution is diversifying through embedded channels, program administrators, and digital MGAs that expect fully open interfaces. Regulation is tightening around pricing transparency, privacy, and model governance. Together, these forces reward platforms that can change quickly without risking compliance or stability.
Speed alone will not cut it, but applying cost discipline might. This is the standard that should guide a decision to build something or buy it instead.
The Tradeoffs That Decide Outcomes
The debate rarely fails on features. It fails when leaders misjudge speed, cost, risk, and long-term evolution.
Speed to Market. Custom cores take years to mature because discovery, design, integration, and testing have long tails. Vendor platforms with prebuilt product models and filing support can cut initial launch timelines and shorten iteration cycles. A majority of recent core modernization projects cite time to value as the decisive factor.
True Total Cost of Ownership. Homegrown systems appear cheaper in early phases, then absorb growing budgets for upgrades, patching, and regulatory change. Specialized engineers become a single point of failure. SaaS providers spread R&D, upgrades, and security investment across many customers, shifting cost curves toward operating expense and away from unpredictable capital spikes. Independent benchmarks show that carriers consistently underestimate long-term maintenance when modeling build scenarios.
Where Differentiation Belongs. Competitive edge typically lives in product design, data, pricing strategy, experience, and partner reach. Those layers can sit on top of a reliable core. Differentiation buried inside bespoke plumbing is difficult to maintain and harder to scale.
Compliance Burden. Insurance-savvy platforms include versioning, audit trails, regulated rates and rules handling, and automated filing support. That reduces manual effort and lowers the risk of material control weaknesses.
Integration and Scale. Modern insurance is a connected business. Carriers need standard APIs and proven integrations with data providers, distribution partners, claims services, and payment rails. Off-the-shelf connectors and developer tooling compress the calendar from idea to rollout, and that compression has direct dollar value.
When Building Actually Makes Sense in P&C
Building is a strategic choice that fits a narrow set of conditions:
Building your system provides a competitive advantage. This benefit cannot be achieved by simply configuring or adding a sidecar service to an existing platform. The organization has a stable and experienced engineering team. They are ready to support ongoing work for product development and compliance for the entire lifecycle.
Governance can enforce strong software development lifecycle practices, documentation, and knowledge transfer so that institutional memory does not concentrate in a few individuals.
The operating model accepts slower early delivery in exchange for long-term control, with the patience to absorb multi-year transitions.
When Buying and Configuring Wins
For most carriers, buying is the pragmatic path when predictability and sustained execution matter more than code ownership.
Speed to value and predictable TCO are priorities, and multi-year build cycles would stall product and distribution roadmaps.
Out-of-the-box transactional processing, rating, and compliance workflows are preferable to designing foundational plumbing from scratch.
A shared roadmap is attractive because it spreads regulatory updates and platform innovation across a community of carriers, reducing upgrade risk and cost volatility.
The future-state architecture favors composability. A configurable core can expose stable APIs while low-code and pro-code extensions add differentiation without destabilizing the base.
The AI Question: Build Models or Buy Capabilities
AI now sits at the center of the modernization decision. Underwriting assistants, claims triage, document intake, fraud scoring, and agent productivity all depend on data quality, model performance, and governance. The decision splits along two lines.
Where to Build. Proprietary risk signals derived from unique data, such as telematics, IoT feeds, or specialized commercial exposures, justify building models and owning model operations. That is where a carrier can create defensible pricing or risk selection advantages that competitors cannot easily replicate. This approach requires mature model governance, repeatable validation, and explainability that satisfies regulators.
Where to Buy. Horizontal capabilities, including document ingestion, summarization, customer service chat, coding assistants, and general productivity, often favor proven vendors. Best-in-class tools now offer private deployments, enterprise-grade guardrails, and connectors to insurance cores that reduce integration friction. Carriers piloting generative AI reported measurable productivity gains in claims correspondence and policy drafting, but only when paired with strong human-in-the-loop controls.
Regulation is tightening on both sides of the Atlantic. The EU AI Act introduces obligations for high-risk systems affecting underwriting and pricing models, including stricter requirements on data governance, transparency, and monitoring.
In the United States, state regulators and the NAIC have issued guidance that raises expectations for bias testing, vendor oversight, and documentation across underwriting and claims. Any build-or-buy AI decision must therefore account for model risk management, auditable data lineage, and clear role definitions for human review. This mirrors a broader pattern visible across regulated industries navigating AI governance at the infrastructure level, where the governance architecture is proving as consequential as the model itself.
A pragmatic pattern is emerging. Carriers place AI close to the workflow but far from raw production systems of record. They secure data in virtual private clouds, use retrieval-augmented generation to limit hallucinations, and log prompts, responses, and outcomes for audit. They buy accelerators where the market is moving fast and build where proprietary data confers real advantage.
Digital Platform and Integration Strategy
Core replacement without a platform strategy trades one constraint for another. A modern target state shares several traits.
Open, Versioned APIs. Stable contracts allow frequent internal change without breaking partners. Versioning and deprecation policies should be explicit.
Event-Driven Design. Events for submission intake, quote bind, payment, claim FNOL, coverage changes, and reserve movements allow downstream systems to react in real time.
Extension Layer. Low-code tooling for product teams and pro-code SDKs for engineers enable fast changes to rates, rules, and workflows while containing risk.
Identity, Consent, and Privacy. Consent capture, data retention, and PII controls must be first-class citizens, with vendor support for regional data residency requirements.
Observability. End-to-end monitoring, traceability, and error budgets keep platforms reliable and auditable.
Quantifying TCO and Risk Realistically
A credible decision model forces hidden costs and risks into the open:
Internal Labor and Opportunity Cost. Include product owners, architects, developers, QA, security, and compliance staff. Account for turnover and recruitment risk.
Security and Compliance. Budget recurring penetration tests, vulnerability management, audit evidence, SOC or ISO programs, and regulatory change cycles. Vendor attestations do not remove the carrier’s obligations.
Upgrade and Release Overhead. Model the frequency and duration of maintenance windows, regression testing cost, and the ability to adopt new capabilities without extended freezes.
Integration and Data Quality. Include the cost of building and maintaining APIs, event streams, data mappings, and lineage documentation. Poor data contracts ripple through every downstream system.
Availability, Recovery, and Performance. Define service level agreements and attach explicit dollar values to downtime, RTO, and RPO targets.
Vendor Lock-In and Exit. For buy scenarios, require data export, schema documentation, and clear rights to replicate environments. For build scenarios, plan for code escrow, documentation, and modular design to avoid internal lock-in.
Change Governance. Include the cost of release management, segregation of duties, and AI model governance. Downtime and control failures carry direct financial and regulatory consequences.
Contracting for Outcomes, Not Promises
Whether building or buying, contracts must translate strategy into enforceable terms.
Data Ownership and Portability. The carrier owns its data and derived data. Exports must be complete and usable without vendor software.
Security and Compliance SLAs. Define breach notification timelines, patch windows, encryption standards, and audit support aligned to the carrier’s control framework.
Availability and Performance. Set meaningful uptime, latency, and throughput targets with financial credits that create real incentives to meet them.
Roadmap Governance. Establish a formal process to influence the vendor roadmap, with transparency on deprecations and transition timelines.
Extensibility and Custom Code. Clarify where customizations live, how they are supported, and how they survive platform upgrades.
Pricing Protections. Guard against unplanned cost spikes with clear policies on consumption metrics, overages, and renewal terms.
A Short Scenario from the Field
A regional commercial lines carrier faced stalled product launches and duplicate underwriting work across business units. Rather than rebuild everything, the program selected a configurable core for policy, billing, and claims, paired with an in-house pricing sidecar built to capture unique data signals in target niches. The core delivered filing support, audit trails, and stable integrations. The sidecar delivered differentiation. Release management shifted to a quarterly cadence while model releases followed a separate, governed cycle. Distribution partners integrated once and accessed multiple products through consistent APIs. The result was not a single breakthrough but a portfolio of reliable, compounding wins.
A Decision Framework That Survives Scrutiny
Executives can pressure-test a build-or-buy decision with six direct questions.
Which capabilities must be unique to win in target segments, and which can be standardized without losing competitive edge?
What calendar time is acceptable before the next three products launch, and what delays would break distribution commitments?
How much recurring spending can be committed to platform upgrades and security without starving product investment?
Which AI use cases warrant proprietary model ownership, and which are better served through vendor tools with strong governance?
What evidence confirms that integration, data quality, and filing support will not become recurring blockers?
How quickly can the organization demonstrate value in a pilot with real policies, real claims, and real partners, not only a demonstration environment?
Conclusion
The build-or-buy decision in insurance is a question of placement. Proprietary investment belongs where it cannot be commoditized. Everything else is a candidate for standardization.
Most carriers will buy a configurable core for policy, billing, and claims. They will build where unique data supports superior risk selection or measurable expense advantage. They will adopt AI through a combination of vendor accelerators and internal models, all governed by auditable controls.
The carriers that struggle are not the ones that chose the wrong path. They are the ones that misread where differentiation actually lives, and spend engineering capacity maintaining infrastructure that no customer or distribution partner can see or value.
This pattern extends well beyond insurance. Across regulated industries, the governance architecture surrounding AI systems is proving as consequential as the models themselves, and the organizations gaining ground are those treating infrastructure decisions as strategic from the start.
Precision in that placement is the decision. Everything else is implementation.
