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Explore how Ki, a digital-native insurance syndicate, transformed the centuries-old Lloyd's of London market by building a billion-dollar business in just three years through algorithmic underwriting. Learn about the unique data science challenges in specialty insurance, including working with microscopic datasets, rare claims events, and unstructured data from PDFs and handwritten notes. Discover how Ki developed algorithms capable of assessing complex risks and determining optimal line sizes in seconds rather than days, despite significant data constraints. Examine the technical evolution across risk assessment modeling and portfolio optimization, guided by a pragmatic "start simple and iterate" approach where traditional machine learning best practices often don't apply. Understand the technical and cultural challenges of introducing data science to specialty insurance industry veterans, and gain insights into applying ML with limited data, sequential decision-making under uncertainty, and implementing data science solutions in traditional industries.
