AI Subrogation: How AI Finds and Recovers Missed Claims

Subrogation is one of the few parts of claims that makes money instead of spending it, yet most recoverable dollars are never collected. Not because they're disputed, but because the files were never reviewed for third-party liability. AI subrogation exists to close that gap.

What is AI subrogation?

AI subrogation is the use of artificial intelligence to read claim files, identify recovery opportunities, weigh liability, and draft demand packages, so recovery no longer depends on an adjuster finding time to review each file by hand. Instead of sampling a fraction of claims, an insurer can put every paid and closed file in front of a model that reads the unstructured contents and flags what's worth pursuing.

The credible implementations share one principle: the AI drafts and proposes, but people approve and send. That keeps a licensed human accountable for every outbound action while removing the manual bottleneck that causes leakage.

Why recovery leakage is so expensive

Recovery leakage is invisible by definition: you never see the recoveries you didn't pursue. That makes it easy to under-count and hard to fix with headcount alone. The volume of files that could carry a subrogation opportunity almost always exceeds what a team can manually screen, so opportunities age out past their statute of limitations and quietly disappear.

How AI subrogation works

A capable system moves a claim through the same steps a skilled adjuster would, but on every file:

1. 01Read the file. Extract facts from unstructured sources, adjuster notes, first-party statements, estimates, correspondence, police reports.
2. 02Reconstruct the loss. Build a chronology of what happened, in order, from the evidence.
3. 03Weigh liability. Map the facts to the applicable rules of fault, including jurisdiction-specific comparative-negligence variants.
4. 04Score viability. Estimate whether a recovery is worth pursuing given liability, damages, and the limitation window.
5. 05Draft the demand. Assemble a cited demand package (recovery theory, chronology, evidence, itemized damages) ready for human review.

Where humans stay in control

AI on legal-adjacent work has one dangerous failure mode: confidently stating something the documents don't support. The safeguard is grounding. Every statement should cite the exact source span it came from and be verifiable against the document; anything that can't be verified is withheld and labeled, not presented as fact. And nothing leaves the building without a recorded human approval.

AI drafts, proposes, and prepares. People approve and send.

How to evaluate an AI subrogation platform

• Grounding: Does every AI claim cite a verifiable source, and does the system withhold what it can't verify?
• Human gating: Can anything go out without a recorded human approval? (The right answer is no.)
• Coverage: Can it read every file, or only structured data?
• Jurisdiction awareness: Does it apply state-specific liability rules and limitation windows?
• Time to value: Does it require a long integration project, or can your team start by forwarding claims?
• Auditability: Is there an append-only record of what the AI did and who approved it?

Frequently asked questions