Analyzing Insurance Churn with SageMaker Data Agent, Amazon S3 Tables and Faker
- David McAmis
- Mar 22
- 1 min read
Updated: Apr 28
Learn how to use Amazon SageMaker Data Agent, Amazon S3 Tables, and the Python Faker library to generate realistic synthetic insurance data for churn analysis, without exposing live policyholder records. This post walks through generating synthetic tables from scratch and creating synthetic twins from existing tables, then shows how to use plain-language prompts to analyze churn patterns across products, demographics, payment behavior, and customer interactions.
From the AWS Builder Center blog:
