AI Workflow Automation for Insurance

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.