AI for Claims Triage and Severity Analysis

Claims triage is a cross-referencing problem. A single large-loss claim can span dozens of documents: medical reports, legal correspondence, adjuster notes, TPA bordereaux, and email chains. The data that determines severity is scattered across these sources, often in conflicting formats and languages. Getting an accurate read on exposure requires synthesizing all of it, not sampling from it.

The Problem

Severity drivers surface too late. In most claims operations, the signals that distinguish a routine claim from a large loss are buried across disconnected systems. Reserve increases, litigation escalation, and adverse medical developments appear in different documents at different times. By the time an analyst pieces together the full picture, reserves have already moved.

Manual review does not scale. A claims team handling thousands of open files cannot read every medical report, every legal filing, and every TPA attachment in full. The typical workaround is sampling or relying on external legal summaries. Both approaches introduce blind spots: sampling misses outliers, and legal summaries add cost without improving upstream visibility into the underlying documents.

Formats are inconsistent. Claim files arrive as PDFs, scanned images, Word documents, spreadsheets, and emails. TPA bordereaux follow different schemas depending on the provider. Medical records use varying terminology across jurisdictions. No two claim packages look the same, yet the triage decision requires comparing them on a common basis.

How Parsewise Addresses It

Parsewise operates at the document-package level, not the individual-document level. Rather than extracting data from one file at a time, the platform ingests the full claim file and reasons across all sources simultaneously using the Parsewise Data Engine (PDE).

Structured Claim Summaries

The platform consolidates emails, PDFs, free text, and reports into standardized, decision-ready summaries. Each summary links every extracted fact to its source document, page, and paragraph. Analysts can verify any data point with a click rather than re-reading the original files.

Automated Event Timelines

Parsewise automatically builds event timelines that sequence treatment milestones, litigation developments, and reserve movements across the entire claim file. These timelines surface temporal patterns (for example, a gap between injury date and first legal filing, or an acceleration in medical treatment costs) that indicate severity trajectory.

Early Severity Flags

The platform flags early severity indicators and adverse claim trends by detecting signals across documents that a single-document review would miss. Examples include:

  • Escalating medical treatment referenced in adjuster notes but not yet reflected in reserves
  • Legal counsel engagement appearing in email correspondence before formal litigation filings
  • Inconsistent injury descriptions across medical reports and first notice of loss (FNOL) documents

Portfolio-Level Risk Heatmaps

For claims managers overseeing large books, Parsewise standardizes risk signals across thousands of open claims and produces portfolio-level risk heatmaps. These heatmaps rank claims by severity indicators, enabling teams to prioritize review resources on the files most likely to drive reserve development.

Example Inputs and Outputs

Inputs

Source Type Examples
Medical documentation Treatment notes, surgical reports, rehabilitation records, independent medical examinations
Legal filings Complaints, motions, settlement demands, defense counsel status reports
TPA data Bordereaux, payment histories, reserve schedules, attachments
Correspondence Adjuster emails, claimant communications, broker notifications
Multi-language documents Cross-border claims with records in multiple languages (70+ languages supported)

Outputs

Output Description
Standardized claim summaries Structured profiles with key facts, exposure estimates, and source citations
Dynamic event timelines Chronological sequencing of treatment, litigation, and financial developments
Severity and litigation risk flags Early warning indicators with supporting evidence from across the claim file
Portfolio-level risk heatmaps Comparative severity rankings across open claims for resource prioritization

Customer Evidence

Compre Group, a specialist in legacy insurance and reinsurance portfolio acquisitions, uses Parsewise to perform diligence on acquired portfolios and reconcile claims data across multiple sources. Legacy portfolios arrive as large, fragmented document packages spanning loss runs, bordereaux, actuarial reports, and policy documents in varying formats. Parsewise ingests these heterogeneous document sets, standardizes loss runs and reserve triangles into consistent formats, and reconciles paid, incurred, and reserve movements across cedants and TPAs. The platform flags anomalies, reserve shifts, and data gaps, producing structured outputs for pricing decisions, reserve adequacy assessments, and regulatory reporting.

Technical Capabilities

Parsewise’s claims triage workflow is powered by the same infrastructure used across all verticals:

  • Cross-document reasoning: Links entities, detects contradictions, and reconciles data across the full claim file rather than processing documents individually. See Cross-Document Reasoning for a detailed explanation.
  • Extraction agents: Configurable with topics, dimensions, and natural-language instructions. Agents can be tuned for claims-specific fields (reserve amounts, litigation status, injury codes) and reused across claim types. See How Extraction Agents Work.
  • Scale: PDE processes more than 25,000 pages per run with autonomous runs exceeding 5 hours, handling spiky claims volumes without manual intervention.
  • Traceability: Every extracted value links to its source document, page, and bounding box, producing audit-ready outputs for regulators and internal review.
  • Security: SOC 2 Type II and GDPR compliant. No training on customer data. VPC and on-premises deployment options for carriers with strict data residency requirements. Details at trust.parsewise.ai.

Ready to see Parsewise in action? Request a demo or contact sales to discuss your use case.

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