promota.
Insurance · AI Claims Quality Audit

Mistakes Are Hiding in Your Closed Claims.

Most QA teams audit a 1-3% sample, leaving the rest unchecked. Promota reads every closed claim and flags possible mistakes: missing evidence, inconsistent decisions, overpayments, underpayments, weak denials. Each flag cites the policy and the file, so your team can verify it in seconds.

Promota audits decisions.Your team makes them.
Book a Pilot Scoping CallSee how the pilot works
// Pilot Targets

What a successful pilot looks like.

Targets we calibrate against on a Claims Quality Audit Pilot. Every flag is reviewed by your QA team before any operational action.

5-10%
Target flag rate
files with QA-validated issues, calibrated during pilot
30%+
Target QA time reduction
vs. current file-by-file manual review
100%
Coverage of closed claims in scope
vs. low-single-digit QA sampling typical at mid-market carriers
45-70d
To first audit packets
from kickoff through CISO approval, data extraction, and first cited, file-level findings
// What We Flag

Ten categories of claim exceptions. All cited. All reproducible.

Promota reviews every closed claim in scope against your policy language, claim documents, adjuster notes, and decision rationale, and ranks the files that justify QA attention.

Policy & document support gaps: Decisions made without cited support in the policy language or claim file
Inconsistent decisions across adjusters: Similar fact patterns resolved differently, surfaced and clustered
Missing evidence: Photos, estimates, statements, or inspection reports absent from the file
Likely overpayment: Payments above policy limits, duplicate line items, or unsupported supplements
Likely underpayment: Covered damage missed, depreciation errors, or undervalued estimates
Denial language risk: Wording patterns that historically correlate with complaints, appeals, or litigation
Weak decision rationale: Approvals or denials without a clear, documented basis tied to policy terms
Supplement-risk documentation gaps: Closed files with documentation patterns historically correlated with later supplements or rework
Coverage interpretation drift: Endorsement and exclusion application that varies from your own playbook
Regulatory & complaint exposure: Decision patterns aligned with DOI complaint and market-conduct triggers
// What Claims Quality Teams Get

A complete claims QA audit layer. Reviewable. Defensible.

Per-Claim Audit Packets
Each flagged claim gets a self-contained packet with the policy language, claim notes, photos, estimates, communications, and decision rationale, all cited inline. Hand it to a QA reviewer and they work the file, not a spreadsheet.
Decision-Risk Scoring
Each closed claim gets a decision-risk score so QA can triage 100% of the book to the files that actually need a second look.
Adjuster Consistency Analytics
Quantify variance across adjusters, offices, and lines of business, with file-level evidence, not anecdote.
Leakage Detection
Surface overpayments, missed depreciation, duplicate line items, and unsupported supplements tied to the source documents.
Litigation & Complaint Risk Flagging
Identify denial language and decision patterns that historically generate DOI complaints, bad-faith exposure, or rework.
Human-in-the-Loop by Design
Promota reviews past decisions and produces cited exceptions. Your QA team retains full authority. No automated approvals or denials. Ever.
// Built for Regulated Workflows

What Promota does not do.

Claims are regulated. The pilot does not let AI make claim decisions. It reviews past decisions and gives the QA team cited exceptions to inspect. The carrier keeps full human authority.

Promota does NOT approve claims.
All claim decisions remain with your licensed adjusters and authority structure.
Promota does NOT deny claims.
Denials are not automated. We review already-decided files and flag exceptions for QA.
Promota does NOT bypass human review.
Every flagged claim is reviewed by your QA team before any operational action is taken.
Promota does NOT replace your QA team.
We extend coverage from a small sample to the full book and route the right files to the right reviewers.
// Why Claims Leaders Partner With Promota
Audits decisions. Doesn't make them.
Promota reviews already-decided claims and surfaces cited exceptions for your QA team. No claim action is taken without a human. Full regulatory authority stays with the carrier.
Every flag has a citation.
Policy section, claim note, photo, estimate line, communication thread. Every exception is anchored to the source document so QA can validate in seconds.
100% coverage, not a sample.
Manual QA reviews 1-3% of files. Promota reviews 100% of closed claims in scope and ranks the ones that justify human attention.
No new systems, no IT project.
Read-only access or exported claim packets. Pilot runs alongside your existing claims platform, with no integration required to prove value.
Calibrated to your playbook.
Exceptions are tuned against your QA team's validation feedback. False positives drop. Reviewer trust compounds. Reproducibility is the metric.
// Claims Quality Audit Pilot

Five phases. One line of business. First audit packets in 45-70 days.

01
Scoping

One line of business to start: auto physical damage or property. We align on scope, time window, and QA validation cadence.

02
Data Intake

Read-only access or exported claim packets for 500-1,000 recently closed claims. No production system changes.

03
Exception Detection

Each claim is reviewed against policy terms, claim documents, adjuster notes, communications, and the closed decision outcome.

04
Audit Packets + Summary

Per-claim audit packets for every flagged file (policy, evidence, decision rationale, all cited inline), plus a ranked exception summary and adjuster-consistency view for your QA lead.

05
QA Validation

Your team validates a sample of flagged claims. We calibrate thresholds together. False positives down, reviewer trust up.

// Pilot Success Criteria

What we measure. Before kickoff and at pilot close.

Agreed in the scoping call, measured against the same files, and reviewed with your QA team at pilot close. The pilot is judged on whether these numbers move, not on whether the AI sounds smart.

Before
QA reviews 1-5% of closed files (sample)
After
100% of in-scope closed files reviewed and ranked for QA attention
Before
Manual file-by-file QA review time
After
30%+ reduction via pre-cited exceptions (target, calibrated to your workflow)
Before
Valid-issue rate found by current sampling
After
5-10% of files flagged with QA-validated issues (target)
Before
False-positive rate: not measured
After
Below an agreed threshold set in scoping; tuned each cycle against QA feedback
Before
Adjuster decision variance: anecdotal
After
Quantified with file-level evidence and clustered fact patterns
Before
Audit findings vary by reviewer
After
Reproducible. Same inputs, same exceptions, same citations
// Book a Pilot Scoping Call

See what your closed claims
are telling you.

A 30-minute scoping call: we walk through the exception categories most relevant to your line of business, the structure of an audit packet, and the false-positive threshold and success criteria we'd agree on for the pilot.