Life insurance underwriting considers an applicant’s medical risk factors to supplement mortality risk characteristics. These risk factors are usually provided inside free-text documents. New insurance-specific Natural Language Processing (NLP) models can automatically extract material medical history and risk factors from such documents. Innovative companies are shifting towards accelerated underwriting, which makes use of new data together with algorithmic tools and modeling techniques to risk-group applicants quickly without the necessity of bodily fluids, physician’s’ notes, etc.This joint Solution Accelerator from Databricks and John Snow Labs makes it easy to implement this in practice – enabling a faster, more consistent, and more scalable underwriting experience.

This session will cover:

• The end-to-end solution architecture on Databricks, from data ingestion to dashboarding
• Easily analyze free-text documents to extract medical history & risk factors using NLP
• Executable Python notebooks implementing the solution that you can start from today
* The insurance trends and efficiency value from accelerated underwriting