Insurance AI Engineering for Claims, Underwriting & Fraud

We treat underwriting models as high-risk under the EU AI Act and align them with fair-lending and NAIC expectations. That means documented features, bias testing across protected classes, human review of adverse decisions, and full audit trails. A model that cannot explain why it declined an applicant is a regulatory liability, so explainability is built in, not added later.

For clear cases, yes, with a confidence gate. AI extracts and validates claim documents, and straight-through processing handles the routine ones while low-confidence claims route to an adjuster with the context attached. High-value or ambiguous claims always keep a human decision, because the cost of a wrong automated payout is far higher than the time saved.

Yes. We build against policy administration and claims platforms like Guidewire and Duck Creek through their APIs and data exports, rather than replacing them. Most carriers run core systems that work but are hard to reach, so the pragmatic path is to wrap them cleanly and let AI read from and write to them safely.

We tune the model to your tolerance for false positives, because an over-eager fraud model annoys honest customers and buries investigators in noise. We measure precision and recall together on your historical claims, and route flagged cases to a human with the reasons, so investigators act on signal rather than a black-box score.

You do. Your policy data, claims history, trained models, and pipeline code remain your property, deployable in your own environment with zero-data-retention options so customer data is not exposed to external model vendors. You retain full ownership of the actuarial and fraud models we build with you.

There is no single price, because cost tracks scope. A single claims-automation workflow is a modest, weeks-long build, while a governed underwriting model with bias testing and audit trails is larger. We scope from one use case, give a fixed range up front, and sequence so early value funds the next step, rather than quoting one large number for everything at once.

The highest-value ones are claims document processing, fraud detection, underwriting and pricing support, and policyholder self-service. Each carries a different risk profile: claims and fraud are largely operational, while underwriting is regulated as high-risk. We usually start with claims or fraud, where value is fast and the compliance burden is lighter, then move to underwriting with the right controls.

A focused claims or fraud pilot typically reaches a working version in a few weeks, then tuning on real cases. Underwriting models take longer because of bias testing, documentation, and governance. We start with one process, prove its numbers, and extend, so you see value early rather than waiting many months for a single large launch.

Yes, when it is designed to. Straight-through processing handles routine, low-risk claims automatically, which frees adjusters to spend real time on the complex, emotional, or high-value cases where a person matters most. The goal is not to remove people from claims but to stop them spending the day on data entry that software handles better.

Often yes, if you start narrow. A mid-size carrier rarely needs a sweeping transformation; it needs one high-volume process, such as claims intake, automated well. The return comes from the labor and cycle time saved on that volume. We assess feasibility first, and will tell you honestly if your data or volume does not yet justify the build.