AI Agents

Context

Built for insurance within the Fraud detection & alerting stack, this agent is used during claim assessment and pre-settlement to check whether submitted photos, scans, and PDFs are genuine and untampered. Typical scenarios include damage photos, repair invoices, medical notes, police reports, and IDs—especially when versions, timestamps, or embedded data don’t line up.

What it does

The agent inspects images and documents for manipulation, reuse, or fabrication. It analyzes camera and file metadata, detects splicing or copy-move edits, identifies AI-generated/overly compressed regions, and compares submissions against prior claims to spot near-duplicates. For PDFs and scans, it checks font/format anomalies, layered edits, and content mismatches between visible text and embedded objects. Findings are returned as a structured pass/flag outcome with a concise rationale and links to the exact regions or pages in question so reviewers can verify quickly.

Core AI functions

Image forensics (noise pattern and lighting inconsistencies, copy-move/clone detection, resampling artifacts, ELA-style cues); metadata extraction and consistency checks (EXIF, device, geotags, timestamps); near-duplicate and template matching across your archive; document structure analysis (layers, embedded objects, fonts); OCR-to-source comparison to catch altered totals or item lines; and reason-code generation that explains each flag in plain language with region or page-level lineage. Confidence scoring prioritizes material anomalies and routes edge cases to human review.

Problem solved

Manual checks miss subtle edits, recycled photos, or altered invoices—creating leakage and inconsistent outcomes. Reconstructing authenticity across multiple files is slow and error-prone without a clear, evidence-linked method.

Business impact

Assessments become faster and more defensible: manipulated evidence is filtered early, payouts on altered documents are avoided, SIU hit-rates improve, and disputes decline because decisions cite specific regions, pages, and metadata behind the flag.

Integration and adjacent use cases

Integration is light–moderate: ingest images and PDFs from your claims intake/DMS; write pass/flag results, annotated regions, and reason codes into the claims workflow or case management—no core changes required.

Common combinations in this stack include:

• Claim vs policy consistency checker (to reconcile coverage after authenticity is verified),

• Behavioral pattern analyzer (claims SIU) (to surface suspicious timelines or repetitions),

• Network association risk detector (to reveal risky claimant–provider links), and

• Suspicious claim escalation agent (to route high-risk cases with a complete evidence bundle).