Ertas for Insurance Claims Processing

The Challenge

Insurance claims processing is a document-intensive workflow where adjusters must review claim submissions, verify coverage, assess damages, and determine payout amounts — all while adhering to policy terms, regulatory requirements, and internal guidelines. A single auto insurance claim can involve police reports, medical records, repair estimates, photos, and policyholder statements, each requiring careful review and cross-referencing. The volume is staggering: large insurers process millions of claims annually, and any delay in processing directly impacts customer satisfaction and retention.

Generic AI tools cannot handle the complexity of claims adjudication because they lack understanding of insurance-specific concepts: coverage endorsements, deductible structures, subrogation rights, coordination of benefits, and state-specific regulatory requirements. A model that does not understand the difference between a comprehensive and collision claim, or between actual cash value and replacement cost, will produce useless output regardless of how fluent its language generation appears. Claims processing requires both domain knowledge and consistency — the same claim should receive the same assessment regardless of which adjuster or AI system processes it.

The Solution

Ertas enables insurance organizations to fine-tune claims processing models on their own adjudication history, policy terms, and internal guidelines. The resulting model understands the organization's specific products, coverage structures, and claims handling procedures — not generic insurance concepts. With Ertas Studio, claims operations teams train models on historical claims data: claim submissions paired with adjuster assessments, coverage determinations, and payout calculations.

The fine-tuned model can automate multiple stages of the claims pipeline. At intake, it triages claims by complexity and routes them to the appropriate queue. During assessment, it extracts key information from claim documents and cross-references it against policy terms. For straightforward claims, it can generate recommended adjudication decisions with supporting rationale. Deployed through Ertas Cloud or on-premise, the model processes claims in real time while maintaining a complete audit trail through Ertas Vault. Human adjusters review AI-recommended decisions, focusing their expertise on complex cases while the AI handles routine adjudication at scale.

Key Features

Example Workflow

A regional property and casualty insurer processes 50,000 claims annually with an average handling time of 14 days. The claims operations team exports 200,000 historical claims — including submission documents, adjuster notes, coverage determinations, and payout records — and uploads them to Ertas Vault after PII tokenization. Using Ertas Studio, they fine-tune separate models for auto, homeowners, and commercial lines. At intake, the triage model classifies claims by complexity and routes them appropriately — simple auto glass claims go to the fast-track queue, while complex liability claims go to senior adjusters. For fast-track claims, the extraction model pulls key data points (date of loss, damage description, coverage applicable, deductible) and the adjudication model recommends a payout with supporting rationale. Adjusters review and approve these recommendations with a single click for straightforward cases. Average handling time for fast-track claims drops from 7 days to 2 days, and the insurer processes 30% more claims without adding headcount.