Guide · Claims

How to review a claims laundry list

A claims laundry list is the bulk claims listing a cedent or broker sends: hundreds of pages, thousands of rows, every counterparty’s own layout. Reviewing one means finding the handful of claims that genuinely threaten your position, without reading every row.

Evaluating software rather than learning the process? Start at claims laundry lists.

The document

What is a claims laundry list?

Laundry list is the market’s own name for the bulk claims register that arrives with a renewal, a treaty discussion or periodic reporting. It summarises a book’s claims experience in one document: claim numbers, dates of loss, claimants, narratives, venues and amounts, in whatever layout the sender’s systems produce.

The problem

Why is laundry-list review hard?

Because the signal hides in free text. The claims that matter announce themselves in narrative language, often misspelled, and in venue columns where one court name changes the complexion of a claim. Meanwhile totals rows masquerade as claims and the same insured is spread across files.

The checklist

What should a review look for?

Six things, in rough order of how expensive they are to miss:

Hazard language

Narrative terms from your own severity vocabulary, including the misspelled variants a keyword search misses.

Problem venues

Plaintiff-friendly jurisdictions and specific courts. A named court always matters; a bare state only matters in a venue column.

Attachment erosion

Insureds whose accumulated incurred is approaching a layer attachment point, summed across every file they appear in.

Large single losses

Individual claims carrying an outsized share of the book’s incurred.

Phantom rows

Totals, preambles and report artifacts counted as claims, inflating or hiding the real picture.

Duplicates

The same claim listed twice across sheets or files, double-counting incurred.

Screening

How do you screen narratives for hazard language?

Match at three tiers against your own vocabulary: exact, synonym and fuzzy. A misspelled “electrocuted” should still hit; “decrease” must not match “deceased.” The vocabulary itself is judgment: it belongs to your team, not to a vendor’s generic list.

Exposure

What is attachment-point monitoring?

Tracking each insured’s total incurred loss against the attachment point of your layer, banded below, approaching or reached. It is the difference between a list review and an exposure review: the claim that matters most may be unremarkable on its own row.

Automation

How does automated laundry-list review work?

The output is a banded, auditable review of the whole list, with every flag explained. Under the hood:

  • Deterministic engine. Classifies the document, maps the columns whatever they are called, suppresses report artifacts.
  • Your configuration. Every claim scored against your keywords and jurisdictions.
  • AI on the borderline. Adjudicates near-misses with written reasoning, and stops for a human when genuinely unsure.

That is Brisc’s Claims Analyst at work. Product detail: claims laundry lists, the Claims Analyst, and first notice of loss intake.

Common questions

Laundry list review, answered

Is a claims laundry list the same as a claims bordereau?

No. A claims bordereau is periodic movement reporting, reconciled version over version. A laundry list is a bulk listing, often attached to a renewal or a treaty discussion, reviewed once to find the claims that matter. The documents overlap; the jobs are different. See claims bordereaux ingestion and validation.

Can automated review use our own keyword list?

It should, because your hazard vocabulary and your problem jurisdictions are underwriting judgment accumulated over years. In Brisc’s Claims Analyst they are tuned per client with no deployment, and a re-run rescores an ingested list instantly after a change.

What happens when the AI is not sure about a match?

It stops and asks. Confident hits are accepted, weak ones ignored, and borderline cases are adjudicated with written reasoning. A case that stays genuinely ambiguous is tagged for human review rather than being forced into a yes or a no.

How is a risk score explained?

Every flag shows why it fired: the term that matched, where it matched, and the jurisdiction signal that weighted it, including the near-misses the engine decided against. A score you cannot interrogate is an opinion; a review has to survive a market dispute.

Who reviews claims laundry lists?

Reinsurance claims teams reviewing cedent listings, delegated-authority carriers reviewing TPA and coverholder claims, and portfolio underwriters assessing a book at renewal. Anywhere a counterparty summarises thousands of claims into one document, someone has to find the ones that matter.

Bring a 300-page laundry list. See what it finds.

30 minutes, no slide deck. We run a real listing through the Claims Analyst, flags, reasoning and near-misses included. Your book is specific; the walkthrough should be too.

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