Automate Claims Intake. Every Channel. Every Format.

First Notice of Loss is the single most important document in the claims lifecycle. It sets the tone for the claimant experience, establishes the factual record, and determines how quickly the claim enters the adjudication pipeline. Yet at most carriers, FNOL processing is a manual, error-prone bottleneck staffed by the least experienced members of the claims team. The fundamental challenge is format diversity. A claimant reports a loss by calling an 800 number, generating a phone transcript. An agent files an FNOL by email with a PDF attachment. A broker submits via a web portal that produces a structured form. A walk-in claimant fills out a paper form that gets scanned. Each of these channels delivers the same underlying information — who, what, when, where, how much — in a completely different format. EezyAutomation normalizes this chaos. The parsing engine accepts input from any channel and applies OCR (for scans and images), natural language processing (for free-text emails and transcripts), and field mapping (for structured forms) to extract a consistent set of claim fields: claimant name and contact information, policy number, date of loss, location, loss description, estimated amount, and any third-party involvement. The output is a structured claim record ready for import into your claims management system — Guidewire, Duck Creek, Majesco, or any platform that accepts CSV, JSON, or API input. Extracted fields include confidence scores so your intake team can review low-confidence extractions before the claim enters the workflow. High-confidence claims from structured sources can flow straight through to assignment without human intervention. At $3 per document, EezyAutomation costs less than two minutes of a claims intake clerk's time. For carriers processing 1,000+ FNOL submissions monthly, the cost savings are immediate and the cycle time improvements are measurable within the first month of deployment.

Modern insurers receive claims through five or more channels: phone, email, web portal, mobile app, agent submission, and postal mail. Each channel has different data quality characteristics. Phone transcripts contain rich narrative detail but poor structure. Web forms are structured but often incomplete. Email submissions vary wildly based on the sender. Scanned mail is low-resolution and may include handwriting. EezyAutomation handles each channel type with specialized processing pipelines that feed into a unified extraction model. For phone transcripts and email bodies, the engine uses natural language extraction to identify claim-relevant entities: dates, locations, dollar amounts, policy numbers, and descriptions of loss events. For structured web form submissions, the engine maps form fields directly to claim fields. For scanned documents, OCR combined with fuzzy-logic field detection identifies data in any layout. The unified output means your claims management system receives identically structured data regardless of the source channel. This consistency enables accurate reporting on intake volume by channel, processing time by source type, and extraction accuracy by format — metrics that are impossible to generate when each channel is processed differently by different staff members. Channel-aware processing also enables intelligent routing. Claims arriving through the mobile app with geotagged photos might route directly to a field adjuster. Claims arriving by email with a police report attachment might route to a complex claims unit. Claims arriving by web form with complete policy information might route to straight-through processing. The channel metadata becomes part of the structured claim record, enriching your triage logic. For carriers consolidating claims intake operations across multiple legacy systems, EezyAutomation provides a single parsing layer that normalizes input from all sources. Instead of maintaining separate intake workflows for each channel, you maintain one set of extraction rules and one set of routing logic that applies uniformly.

Not every parsed field deserves the same level of trust. A policy number extracted from a structured web form has near-perfect confidence. A loss amount mentioned casually in an email narrative has much lower confidence. Treating both extractions identically — either trusting everything or reviewing everything — wastes resources in both directions. EezyAutomation assigns a confidence score to every extracted field on a 0-100 scale. The score reflects multiple factors: the clarity of the source text, the strength of the contextual signals around the field, the consistency of the extracted value with expected patterns (e.g., does the policy number match the carrier's numbering format), and the historical accuracy of similar extractions from the same source type. You configure threshold rules that determine what happens at each confidence level. Fields above 90 might flow through automatically. Fields between 70 and 90 might be highlighted for quick confirmation. Fields below 70 might route to a specialized review queue. The thresholds are yours to set based on your risk tolerance and the downstream consequences of an incorrect extraction. Exception routing goes beyond simple threshold checking. EezyAutomation can flag claims that require special handling based on extracted content: claims involving injuries route to bodily injury adjusters, claims exceeding a dollar threshold route to senior adjusters, claims mentioning legal representation route to litigation management. These routing rules operate on the structured data the parser produces, not on keyword searches through raw text. The feedback loop is critical. When a reviewer corrects a low-confidence extraction, the correction improves the engine's performance on similar documents going forward. Over time, your specific claim types, policy formats, and agent submission patterns are learned by the system, and the proportion of exceptions decreases. Carriers typically see exception rates drop from 25-30% in the first month to under 10% within six months as the engine learns their document landscape.