Swiss Re

Reinsurance operates on the ability to assess and price risk accurately across vast, heterogeneous datasets — yet Swiss Re's Corporate Solutions business found itself constrained by legacy workflows that could not extract actionable insight from the structured and unstructured data it held. Claims handling required up to 20 sequential steps, creating bottlenecks that slowed payouts and limited the organisation's ability to detect fraud at scale. With more than 40,000 claims processed annually, the operational cost of inefficiency was significant. Beyond process friction, siloed AI experiments failed to move beyond pilot stage, leaving enterprise-wide capability fragmented and the full value of the data estate unrealised.

Swiss Re deployed an AI-powered platform within its Corporate Solutions division, applying Natural Language Processing (NLP) to automate triage, extract meaning from unstructured claims documents, and surface signals relevant to fraud detection and decision support. Rather than layering automation onto existing workflows, the programme prioritised process redesign — eliminating redundant steps before encoding any logic into the platform. A deliberate hybrid model was adopted: diversified external technology partnerships provided platform-agnostic capabilities, while an internal team of AI, data, and transformation specialists retained domain expertise and governance control. Human-in-the-loop checkpoints were embedded throughout, ensuring oversight on complex or high-value cases and supporting regulatory compliance across jurisdictions.

The platform now supports 40,000+ claims annually, accelerating end-to-end processing and enabling faster client payouts at what Swiss Re describes as the 'moment of truth' in the customer relationship. Key outcomes include:

• Faster claims resolution through automated triage and NLP-driven document analysis, reducing reliance on manual review for routine cases
• Earlier fraud detection enabled by improved data analysis across structured and unstructured sources
• Freed capacity for claims professionals to focus on complex, high-value cases requiring human judgment

The programme has been elevated to the group's number one strategic priority under its 'Built to Lead' strategy, with formal business cases and defined financial and operational metrics governing each initiative.

• Redesign before automating: AI should be the prompt to eliminate unnecessary workflow steps, not a tool for replicating legacy processes digitally — Swiss Re reduced 20-step claims workflows as a prerequisite to deployment.
• Avoid vendor lock-in by design: A platform-agnostic approach with diversified external partnerships protects long-term flexibility as the AI landscape evolves.
• Internal domain expertise is non-negotiable: External vendors cannot substitute for in-house AI and transformation capability when scaling beyond pilots.
• Governance must be structural, not procedural: Embedding human oversight and defined metrics into each initiative — rather than treating compliance as an afterthought — is what enables enterprise-wide adoption.