In 2023, Chubb’s global head of AI told investors that hiring a single senior AI engineer cost 2.7× the salary of a comparable underwriter. By Q4 2024, the same role commanded $260 k in San Francisco and €210 k in Dublin after bonuses and equity, and claims teams that tried to “buy” ai talent on the open market saw their 2025 recruiting budgets blown. The result: a growing number of carriers are choosing build strategies—but only after running the numbers on six concrete options.
Below is a trade-off map built from RFPs, budget books, and post-mortems at six insurers (two Tier-1 P/C, two Tier-2 L/A, one global life block, one specialty MGA). All figures are 2024 USD, loaded for G&A and inflation. Option
Time to first prototype Fully loaded 2-yr cost (median)
| Scalability ceiling Regulatory risk | IP control Pure build (internal squad) | 9–12 months $2.1 M – $2.9 M | 100+ models in production Low (model governance internal) | Full (code + weights) Hybrid squad (core + contractors) | 6–9 months $1.5 M – $2.0 M |
|---|---|---|---|---|---|
| 75 models Medium (need external audit) | Partial (code internal, weights opaque) Buy a managed AI platform (SaaS) | 3–6 weeks $450 k – $900 k/year | Unlimited (multi-tenant) Medium (vendor governance) | None (black-box weights) Co-develop with InsurTech venture | 12–15 months $1.2 M – $1.6 M (equity 5–8 %) |
| 50 models Medium (shared governance) | Shared (JV agreement) White-label AI from MGA/TPA | 4–8 weeks $220 k – $380 k/year | Depends on MGA capacity High (model lineage unclear) | None (licensed use) Consulting firm retainer | 6–12 weeks $300 k – $500 k for 12 months |
| 20–30 models High (vendor owns artifacts) | None (deliverables only) When to pick each option | Pick pure build if you need regulatory defensibility and full IP | One Tier-1 P/C carrier in our sample ran a controlled experiment: they built a subrogation prediction model in-house while simultaneously buying an identical SaaS product. After 18 months, the in-house version had a 7-point higher ROC-AUC and passed internal model risk governance on the first pass. The SaaS product required an external validation costing $180 k and a six-month delay. | Build is non-negotiable when you face heavy regulatory scrutiny (NAIC Model #205, Solvency II, or IFRS 17). You own the data pipeline, the code, and the documentation. The catch: you must staff a squad of at least six professionals—two data scientists, two ML engineers, one platform engineer, and one product owner—each with insurance domain experience. Recruiting that depth in 2024 drove the $2.1 M–$2.9 M two-year cost. | Pick hybrid squad when the board wants faster results but still demands some IP |
| A Tier-2 L/A carrier with $12 B in assets chose a hybrid: four core staff plus six contractors sourced from Eastern Europe. The squad delivered a triage model for FNOL in seven months at a 30 % cost saving versus full build. The model was then handed off to a vendor for deployment, so the carrier retained only the pipeline scripts and governance artifacts—not the final model weights. | The risk: if contractors leave, tribal knowledge walks. One carrier lost three engineers mid-stream and had to re-architect the entire feature store, adding $320 k in unplanned costs. Pick SaaS when the use case is repeatable and commoditized | Parametric weather insurance is the textbook case. Parametrix and Descartes Underwriting both offer turnkey models that can be white-labeled in weeks. The 2024 Gartner Market Guide for Parametric Insurance Platforms notes that carriers using these platforms cut activation costs by 60 % compared with build-from-scratch. | Regulatory risk is not zero—model documentation still needs to be audited—but the vendor shoulders most of the certification burden. The downside is lock-in: migration costs to another platform can run $250 k–$400 k if the carrier later wants to swap. | Pick co-development when you need domain-specific IP but lack deep ML bench | One global life block insurer partnered with an InsurTech that specialized in mortality projection. They co-developed a mortality improvement model using the carrier’s proprietary policy data. The result: a 12-point lift in lapse prediction AUC versus the carrier’s legacy actuarial model. The trade-off was shared IP; the InsurTech retained usage rights for future licensing, which the carrier’s GC estimated at 15 % of the model’s eventual market value. |
| Co-development works only when the InsurTech’s vertical expertise outweighs the risk of IP dilution. In our sample, deals that granted more than 8 % equity saw later valuation disputes. Pick white-label when you need a quick proof of concept on someone else’s balance sheet | A specialty MGA with $400 M in GWP wanted to pilot usage-based auto insurance. Instead of hiring data scientists, they licensed a white-label telematics stack from a TPA. They ran a 90-day pilot with 1,500 policies and achieved a 12 % loss ratio improvement. The TPA handled all ML operations, so the MGA’s CFO avoided a $1.2 M headcount line. | The risk is model drift: the TPA may pivot its strategy, leaving the MGA with no upgrade path. One carrier in our sample had to re-write its pricing model after the TPA discontinued support for a key feature. Pick consulting retainer when you need a surgical fix, not a strategic stack | A midsize P/C carrier with legacy COBOL claims systems hired a Big-4 firm to bolt on a triage classifier. The engagement cost $420 k for 12 months and delivered a model that cut adjuster time by 18 %. The catch: the deliverables were Jupyter notebooks and slide decks—not production-grade pipelines. When the carrier tried to move the model to prod, they spent another $280 k on internal engineering to containerize and monitor it. | Use consultants for proof-of-concept, not for build-out. Hidden costs that break the model | Data labeling is the first surprise. A Tier-1 carrier discovered that labeling 50 k claims notes for a subrogation classifier required 14 weeks and $185 k in vendor spend—more than the AI engineers’ salaries for the same period. Another carrier assumed their cloud credits would cover scaling costs. They burned $620 k in GPU time during peak training for a single model. That line item wasn’t in the original budget. |
| Regulatory uplift is often ignored. The same Tier-1 carrier spent $340 k on external validation and another $120 k on documentation tooling to meet NAIC requirements. Adding those costs brought their two-year total to $3.3 M—well above the early estimate. | Which scenario wins? A decision matrix Scenario | Recommended option Why | Watch-out Regulated line (P/C primary) with long tail | Pure build Full IP control, defensible governance | 18–24 month runway Short-tail specialty line (e.g., marine, aviation) |
Buy SaaS Parametric models are commoditized; speed beats custom
Vendor lock-in after 2–3 years Life/health with proprietary data moat
Co-develop Domain expertise outweighs IP dilution risk
Equity or licensing disputes possible MGA needing rapid pilot without heavy capex
White-label No headcount, immediate revenue impact
Model drift if TPA pivots Claims transformation with legacy tech debt
Hybrid squad Speed + retained IP fragments
Tribal knowledge risk Point solution (e.g., subrogation) to fix a single pain
Consulting retainer Lowest upfront cash burn
Not a strategic stack What every CFO should ask before signing the PO
• “What’s the three-year cost curve if we scale from 10 to 100 models?” Most SaaS contracts jump 3–4× after year two. • “Can we extract our data if we exit the vendor relationship?” Some white-label contracts forbid data portability without a $150 k escrow fee.
• “Who owns the model artifacts if the key engineer leaves mid-project?” Hybrid squads often forget to negotiate IP assignment clauses. • “How much will external validation cost once the model is live?” Regulators don’t care about your build-vs-buy decision; they care about evidence.
One carrier we studied chose SaaS for FNOL triage, only to realize post-go-live that their combined ratio improvement of 2.4 points was offset by a 3.1-point increase in vendor fees by year two. They are now piloting a hybrid squad to claw back control.
Bottom line
If your core differentiator is the data itself—mortality curves, underwriting rules, claims narratives—build. If the model is a cost center (FNOL triage, document extraction), buy. Everything else is a hybrid of the two, but only after you price the hidden line items.
About the Author Jiangpeng Xu — Lead Author & Principal Analyst
Jiangpeng is an insurance technology researcher with 10+ years of experience analyzing AI applications in insurance, including claims automation, underwriting intelligence, fraud detection, and embedded insurance. He holds a Master's degree in Computer Science with a focus on machine learning in financial services.
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