This presentation is designed to inspire actuaries to broaden their perspectives on data usage and reimagine the datasets they rely upon. We challenge the conventional notion that data volume is solely about row counts, and demonstrate the profound impact of overlooked variables outside the rating plan, quote data, competitive intelligence, and more. Our exploration ventures into powerful tools and techniques, including unsupervised learning, synthetic datasets, and text mining, offering a fresh lens to view and leverage data. Balancing innovation with integrity, we also tackle data privacy and ethical considerations. This talk is a call to action for insurance professionals to prepare for the future of data-driven insurance, fully capitalizing on their data resources.
Learning Objectives:
Discuss the trade off in row count and the machine learning considerations for small data sets
Evaluate the various datasets at the actuary's disposal in insurance outside of traditional pricing data sets
Evaluate the benefits of machine learning methods on various data sets alongside the benefits of domain knowledge
