- H2O Driverless AI can be used to build claim triage models from your own claim, policy and payment history, with reason codes on every score.
- Low-risk claims can move to fast-track payment, while complex or suspect claims route to an adjuster or investigator with the reasons attached.
- Reason codes on a denial tell the adjuster what to check and the policyholder what is missing, so a claim can be corrected and resubmitted.
- An agent can assemble a claim file, read the report, photos and policy coverage, and draft a recommendation for the adjuster to approve.
Give every policyholder a fast, explainable answer
A policyholder who has just had an accident or a burst pipe learns what your promise means when they file. They want a fast and fair answer, and every extra day or unexplained request wears down the relationship.
Most claims queues still run on rules and manual review. Your adjusters open the same systems for a routine claim as for a complex one, and the rules cannot tell which claim needs a closer look. Denials add their own work: when a claim is denied or returned, you need a documented reason a policyholder, a complaint reviewer or an examiner can follow.
How claims management works
H2O can be used to add explainable claim scoring above the claims system you run today, plus agents that assemble the claim file for an adjuster. Your claims system stays in place; scores and agent summaries feed into it.
Step 1: score each claim at first notice of loss
Start with claim, policy, claimant and payment history, joined to how past claims were settled. H2O Driverless AI trains classification models on that history, and automatic feature engineering builds signals such as time since the policy started, claim amount against similar losses, and documents still missing.
Step 2: route by risk and complexity
Your team sets the routing policy: fast track, adjuster, specialist or investigator. Anomaly detection flags claims that look unlike anything in the history, and suspect claims route to your fraud workflow.
Step 3: explain every score and every denial
Each claim receives a score and reason codes that show which factors drove it. The adjuster sees what to check first, and a denial letter can state the reason in plain terms.
Step 4: assemble the claim file with an agent
Agents built in H2O AI Super Agent read the claim report, photos and policy coverage, then draft a recommendation: approve, request more information or decline. The adjuster reviews the draft and makes the decision.
Step 5: deploy and keep the model current
Each model ships as a scoring pipeline that runs inside your claims flow, with model documentation written as it trains. Claim patterns shift with weather, repair costs and new products, so watch for model drift and retrain on schedule.
Claim denials, from the other side of the decision
The same models can rank denied or returned claims by how likely they are to be paid once corrected and by their value, so your team works the recoverable ones first. The reason code tells your reviewer what to fix and tells the policyholder what to send, so fewer claims go around the loop twice.