The Business Problem
In property and casualty insurance, field adjusters work quickly at inspection sites — capturing photos, writing estimates, and compiling notes under tight timelines.
However, small but common issues such as blurry photos, missing coverage angles, or mismatched narratives between written notes and photographic evidence often slip through.
These minor lapses result in costly reinspections, settlement delays, and increased claim leakage at scale.
What was needed was an intelligent QA mechanism that could automatically review submissions before they reached the carrier, catching human oversights early and standardizing quality control across all adjuster workflows.
Our Solution: AI-Powered Pre-Submission QA
PalTech developed an AI assisted pre-submission quality assurance system that automates end-to-end analysis of adjuster submissions — from image quality to damage classification and narrative alignment.
The system combines computer vision, multimodal large language models (MLLMs), and natural language processing to deliver an explainable, prioritized summary for human reviewers.
It seamlessly integrates into existing carrier or third-party claims platforms as a lightweight, API-first service with an intuitive reviewer interface.
How It Works
Step 1: Image Quality Analysis
Every uploaded image is analyzed for clarity, lighting, and contrast.
The system generates per-image quality scores and recommendations (e.g., re-capture, enhance, review) — serving as the first gating check before submission.
Step 2: Photo Categorization
By combining NER outputs from adjuster notes with image metadata, the AI automatically groups photos by location, object, and elevation (e.g., Roof → Front → Second Story).
This enables coverage scoring, flagging missing views (like absent wide shots) and offering specific recommendations for improved documentation.
Step 3: Damage Identification
Computer vision models identify visible damage types — such as Roof Damage or Gutter Damage — assigning confidence levels and severity tags (High / Medium / Low).
The system consolidates these into structured summaries that help reviewers prioritize high-risk findings.
Step 4: Narrative Alignment
Here, AI cross-verifies adjuster narratives with image analysis to compute an agreement score per comment group.
This step highlights matches, partial matches, and mismatches, revealing where written descriptions may not align with photographic evidence — enabling reviewers to catch potential discrepancies instantly.
Step 5: Consolidated QA Report
Finally, all findings are compiled into a human-readable, interactive report that summarizes:
- Image quality and coverage adequacy
- Damage mapping and severity levels
- Narrative alignment results
- Key action recommendations and a weighted overall QA score
This unified report helps reviewers decide whether a claim package is ready for submission or requires correction.
What Reviewers See
- Risk-Based Triage: Automatically prioritizes high-risk claims based on severity and confidence scores.
- Explainable Flags: Each flag includes linked evidence — photo thumbnails, comment excerpts, and AI confidence metrics.
- Interactive Dashboard: Reviewers move through clear panels — Quality → Coverage → Damage → Alignment → Final Summary.
- Reviewer Controls: Approve, annotate, or return claims directly within the interface for a streamlined QA experience.
Business Outcomes
This AI-driven QA system helped insurance carriers transform claim review from a manual, reactive process to a proactive, intelligence-led workflow:
- Fewer Reinspections & Reduced Leakage: Errors and coverage gaps are detected pre-submission, preventing costly downstream corrections.
- Faster QA Turnaround: Reviewers focus only on flagged items, improving overall throughput by up to 40%.
- Higher Accuracy & Consistency: Standardized review logic ensures fairness and reduces subjectivity in claim assessment.
- Full Audit Trail: Every flag is traceable with linked evidence, confidence levels, and reviewer notes.
- Accelerated Settlement Cycles: Cleaner submissions lead to faster carrier approvals and better customer satisfaction.