Sompo Japan Insurance trials LLM-powered inquiry response system for operational efficiency

Sompo Japan Insurance Inc. deployed NLP for Document Processing & OCR in Property & Casualty. As reported by en-54177.site-translation.com: ~1,000 employees trial participants.

Maintained by Peter Korpak, Lead EditorHow evidence is checked
~1,000 employeesTrial Participants

Source-reported figures — cited source: en-54177.site-translation.com

What Sompo Japan Insurance Inc. was trying to fix

Sompo Japan Insurance faced challenges in handling large volumes of internal inquiries efficiently. Staff needed to manually search through extensive manuals and Q&A documents to formulate responses, resulting in time-consuming inquiry handling processes.

What Sompo Japan Insurance Inc. deployed

In October 2024, Sompo Japan launched a trial of 'Oshison LLM' (provisional name), an AI system built on a large language model trained on the company's internal manuals and Q&A data. The system automatically generates optimal answer candidates with source references, allowing staff to efficiently craft responses. Proprietary text structuring techniques were used to improve AI accuracy.

Results

The trial was rolled out to approximately 1,000 employees nationwide. Initially limited to auto insurance and liability insurance inquiries, with plans to expand coverage. The system is expected to reduce time spent on inquiry responses and improve overall operational productivity.

Key Takeaways

  • Training LLMs on domain-specific internal documentation (manuals, Q&A) can meaningfully accelerate knowledge worker response workflows.
  • A human-in-the-loop design—where staff verify AI-generated answers and sources before sending—balances efficiency gains with quality control.
  • Phased rollout by product line (starting with auto and liability) allows controlled validation before broader deployment.

Evidence for Sompo Japan Insurance Inc.'s Document Processing & OCR deployment

Reported outcome metrics
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AI Technology
NLP
Company Size
Enterprise

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