Every Quality Audit manager knows the problem. An auditor pulls a claim file, any other relevant data, and spends the next few hours reading through adjuster notes, reserve changes, and correspondence - all while trying to remember what the Expectations Document / Guidelines/ Regulations require for actions like initial contact timing, time to respond, diary frequency, coverage investigation, and a dozen of other good faith claim handling best practices. Then they score it. By the time a pattern of non-compliance surfaces, the leakage has already happened.

This is not a technology problem, exactly. It is a scale problem. Quality Audit teams are good at their jobs. Manual review is resource intensive limiting the number of files that can be reviewed. Technology solves the scale problem.

What We Built

The ClaimLens Quality Audit Engine automates the first-pass review. It reads everything already in the claim file - the full set of indexed documents and the adjuster's notes or activity log - and scores compliance against your organization's Expectations Document / Guidelines / Regulations : the same one your compliance officer already maintains.

The Expectations Document / Guidelines / Regulations drives everything: what counts as a pass, what the deadlines are, which categories carry the most weight, what triggers a flag, and so on. When your compliance standards change, the compliance officer uploads the new version directly. No engineering work. No reconfiguration. Every future run uses the updated document automatically. Every historical run stays pinned to the exact version in effect at the time.

How It Works

Before a single check is scored, the system identifies the claim type - first-party or third-party -  by searching the claim documents. Checks that do not apply to that claim type are automatically skipped rather than scored as failures. This matters because a skill set built for all claim types should not penalize a first-party file for missing documentation that is only relevant to liability claims.

Each compliance check is then evaluated against two sources: the adjuster's activity log and the actual documents in the file. Finding a diary entry that says contact was made is not enough - the system also confirms that the underlying document exists and is substantive. Deadline checks measure elapsed time against actual timestamps, deterministically. A deadline that was missed is a failure, regardless of what else is in the file; the AI cannot change that outcome. For judgment-based items - whether a plan of action reflects genuine strategy, whether reserve documentation tells a coherent story - the AI searches the full document corpus and returns a clear, standardized determination: Meets Expectations, Needs Review, or Does Not Meet Expectations. It does not invent scores or fabricate evidence.

Every finding cites the exact text that supports the determination. Auditors see the evidence trail behind every result before they decide whether to accept it.

Human Review Is the Point

The engine produces a draft, not a verdict. Auditors review every finding, override anything they disagree with - with a required written justification - and finalize the report when they are satisfied. Finalization locks the report permanently. A PDF is generated with the complete audit trail: every automated finding, every human override, every justification, attributed to the auditor who signed off. That document is suitable for regulatory submission or uploading back into the claims management system.

What Changes

A review that takes a skilled auditor two to three hours takes the system a few minutes, significantly expanding the number of files that can be reviewed. Scoring consistency stops being a function of who happened to pull the file. Every file runs against the same standard, applied the same way, every time.

Compliance officers no longer depend on engineering to keep standards current. Publish an updated Expectations Document and it takes effect immediately.

Request a Demo

If your Quality Audit team is spending meaningful time on manual file review, or if you are running into auditor-to-auditor scoring inconsistency, we would like to show you what this looks like on a real claim file. Book a demo with us at https://doclens.ai/contact-us.