AI for Insurance: Automating Underwriting, Claims and Fraud Checks
Underwriting and claims run on documents and judgment — and both are slow by hand. Here's how AI reads the documents, scores the risk, and explains every decision for audit.
Insurance and non-bank finance run on two things that don’t scale by hand: documents and judgment. Underwriting waits on manual document review. Claims queue up while someone reads and re-keys. Fraud gets caught in next quarter’s audit instead of the moment it happens. AI doesn’t remove the judgment — it removes the manual reading and re-keying around it.
What AI changes
Document-heavy underwriting. LLMs extract structured data from applications, statements and policies, cutting manual processing by 90%+ — with a human reviewing anything the model is unsure about.
Claims triage and automation. Route, tag and pre-assess claims automatically, escalating the complex ones to a person with the context already gathered.
Real-time fraud and anomaly detection. Streaming ML scoring in milliseconds, catching anomalies as they occur.
Explainable, audit-ready decisions. SHAP values and reasoning traces plus decision logs designed for audit — every automated decision is justifiable to compliance and regulators.
The proof
We built a real-time scoring system that went from an 8-hour process to 47 milliseconds, with explainability built in from day one. (See the PropCFlow story.) And on the document side, we process complex records 90% faster with zero critical errors (Glacier Analytics).
Where to start
Everything runs inside your own security perimeter, within your compliance requirements. A two-week Sprint proves accuracy and explainability on your real documents and cases first. And it works with your existing policy-admin and claims systems — integration is standard scope, not a rip-and-replace.
See how we build AI for insurance & financial services, or get a free AI roadmap.