Tokio Marine Insurance UAE

In Property & Casualty insurance, claims processing speed directly determines customer satisfaction and operational cost. Tokio Marine Insurance UAE faced a structurally manual workflow for car insurance claims: staff extracted dozens of data points by hand from police reports, Emirates IDs, driver's licenses, and vehicle registration documents arriving via email. At peak volumes of up to 600 claims per day, the process created compounding bottlenecks — delays, transcription errors, and mounting staff pressure. The 2024 UAE floods exposed the fragility of this model, as claim volumes surged to record levels with no scalable mechanism to absorb the spike. The status quo meant slower settlements, higher error rates, and a workforce stretched beyond sustainable capacity.

Tokio Marine Insurance UAE partnered with Kodak Alaris to deploy an AI-powered document automation platform designed to handle claims registration end-to-end. The system applies Natural Language Processing (NLP) to automatically ingest incoming claim emails, identify and classify attached documents, and extract the required data fields without manual intervention. Kodak Alaris's platform replaces the entirely manual intake workflow, routing structured data directly into downstream claims systems and flagging only genuine exceptions for human review. This integration preserved existing claims infrastructure while inserting automation at the highest-friction point — document ingestion and data extraction. The result is a straight-through processing model where staff focus on adjudication rather than data entry.

The automation platform delivered substantial, measurable improvements across the claims operation:

• 90% reduction in claims processing time — the headline outcome, reflecting faster straight-through processing from submission to registration
• 3x increase in daily claims volume handled, enabling the team to absorb surge events like the 2024 UAE floods without proportional headcount growth
• 60% reduction in exception handling, indicating the NLP extraction is accurate enough to minimize manual fallback cases

Beyond the metrics, the shift freed claims staff from repetitive data entry, redirecting capacity toward higher-judgment work. The drop in exception rates is a particularly strong signal of automation quality — processing speed and accuracy improved together.

• High email claim volumes (600+/day) with multi-document submissions represent the clearest ROI case for NLP-based document automation in P&C insurance.
• Reducing exception rates alongside cycle time is the right dual measure of automation success — speed gains that generate more exceptions simply shift the bottleneck.
• Natural disaster events expose capacity ceilings in manual workflows; insurers should design for surge, not average daily volume.
• Integrating automation at the document ingestion layer — rather than replacing core claims systems — reduces deployment risk and accelerates time-to-value.
• Staff adoption is smoother when automation handles data entry and humans retain decision authority over complex or flagged claims.