Improving and Scaling Decisions -- Data Science and Automation in Insurance with Luca Baldassarre of Swiss Re

[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?