AI MVP Development for InsurTech Products

Most InsurTech products die in the plumbing, not the pitch. Before a single model runs, your MVP has to speak the carrier's language: ACORD forms and ACORD XML/JSON messaging for submissions, X12 837/835 EDI for health claims, and read/write access into a policy admin system like Guidewire (PolicyCenter, ClaimCenter, BillingCenter), Duck Creek, or Sapiens. We build the ingestion and integration layer first — intelligent document processing that pulls structured fields out of ACORD 125/140 loss runs, dec pages, and broker PDFs — so the AI has clean, mapped data to reason over instead of choking on a scanned fax.

On the underwriting side, the interesting work is accelerated (algorithmic) underwriting and submission triage for commercial lines. We wire risk-scoring models to the data sources carriers actually trust — MIB, LexisNexis Risk Solutions, ISO/Verisk exposure and property data, MVR pulls, and telematics — then route clean risks to straight-through processing and flag the rest for a human underwriter. Because this is regulated, we build governance in from day one: the NAIC Model Bulletin on the Use of AI Systems by Insurers, Colorado's SB21-169 testing regime for ECDIS and predictive models, and NY DFS Circular Letter No. 7 all require documented bias testing for unfairly discriminatory outcomes, so we ship model cards, feature-lineage logs, and proxy-discrimination test harnesses alongside the model — not as an afterthought.

Claims fraud is where AI pays for itself fastest, and it's a problem we've shipped for adjacent risk teams. Our fraud work mirrors the approach in the fintech-fraud-detection case study: anomaly detection on claim patterns, graph/entity-resolution to surface staged-accident and provider-collusion rings, and severity scoring that feeds the SIU (Special Investigations Unit) queue with explainable reason codes rather than an opaque score. We pair that with subrogation identification and duplicate-claim detection so the same pipeline recovers dollars on both the fraud and the leakage side.

For P&C lines we build the front door of the claim: FNOL (first notice of loss) intake that a policyholder completes from a phone, computer-vision damage assessment that estimates auto or property severity from uploaded photos, and triage that decides fast-track versus assign-to-adjuster in seconds. On the pricing and exposure side we integrate telematics and UBI (usage-based insurance) via OBD-II or a mobile SDK, and IoT signals like smart-home water sensors, to move from proxy rating toward behavior-based pricing — always with the actuarial and rate-filing team in the loop so the model stays inside what's been filed with the state DOI through SERFF.

Distribution and servicing is the other half. We build quote-and-bind flows and embedded-insurance APIs for partners who want to sell coverage at point-of-sale, plus RAG-powered agent and policyholder copilots that answer from your actual policy wordings, endorsements, and underwriting guidelines instead of hallucinating coverage. These copilots plug into agency management systems (AMS360, Applied Epic) and can draft ACORD certificates, explain exclusions, and surface the right endorsement — with citations back to the governing document so a licensed producer can trust the answer.

Health and life carry their own weight: PHI under HIPAA, medical necessity and bill review, and adverse-action obligations under the FCRA when a model contributes to a declination or higher rate. Our medical-claims and document-review experience — reflected in the medivance-radiology build — carries directly into automating medical bill review, prior-authorization triage, and adjudication support, with the audit trail and human sign-off regulators expect. Across life and health we keep GLBA privacy, SOC 2 controls, and consumer-consent handling wired into the data pipeline rather than bolted on at the end.

Our model is to pick the one workflow with real loss-cost or expense impact — claims triage, submission clearance, or fraud referral — and ship a production-grade slice of it in 2-3 weeks, integrated with your policy admin stack and instrumented so actuarial and compliance can see every decision. You get 100% code ownership: the models, the prompts, the integration code, and the governance artifacts are yours to file, audit, and extend. With 18+ AI MVPs shipped by a team of 15+ engineers, we build the version that survives contact with an underwriter, an adjuster, and a market-conduct exam — not a demo.