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Historically, much of the automation/augmentation in insurance has occured in consistent, structured types of risk (e.g. auto). At a reinsurer like Swiss Re, risk is complex, inconsistent, and global in scale; LLMs are a major unlock. And this is just one of many learnings from Luca.
Key Takeaways
- [00:12] Luca's background PhD and two post-docs, with a focus on the core math of machine learning
- [01:43] Background on Swiss Re's business
- [05:14] What are the primary functions in a given business line?
- [07:51] Where's the bulk of the headcount in the business?
- [09:45] What does a claim between an insurer and reinsurer look like?
- [13:32] When did we start transitioning from paper pushing into the first wave of digitization?
- [14:54] Fair to say that Swiss Re is involved primary in less standard types of risk given focus on reinsurance and complex primary insurance? And therefore less automated today?
- [16:25] What's the role of your team?
- [19:15] How do you address this problem of underwriting/pricing tail risk?
- [20:22] Breaking down data extraction into 1) extracting known data fields into structured systems 2) extracting information on a bespoke/one-off basis
- [25:13] Have you observed post ChatGPT that your colleagues were starting to use these tools organically? Or have you tried to disallow this behavior?
- [26:23] How exactly have you seen this tech changing workflows?
- [27:27] How have you thought about implementing these models? What challenges have you run into?
- [29:21] Are these tools something you'd like to buy externally or build in-house?
- [33:49] Do you think that the document-based Q&A format will hold as the long-term form factor?
- [35:53] Zooming out from doc data extraction in underwriting/claims, are there insights you can add in other areas e.g. new product development?
- [40:38] Are there other major categories or excitement or time investment for your team that we should be talking about?
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