Life Insurers Optimize Underwriting Using AWS AI Services Including Amazon Textract and SageMaker
Life insurance underwriting is being transformed with AWS AI and generative AI services to optimize the underwriting process from data collection to policy generation. Multiple insurers including Canara HSBC Life Insurance, Elevance Health, Root Insurance Co., and Sumitomo Life have implemented AWS AI/ML services to automate document processing, risk assessment, pricing, and policy generation workflows. AWS services such as Amazon Textract, Amazon Rekognition, Amazon SageMaker, Amazon Comprehend, Amazon Bedrock, AWS Step Functions, AWS Data Exchange, and Amazon S3 enable document data extraction, machine learning risk models, dynamic policy generation, and multi-channel policy delivery.
- Organization
- HSBC
- Industry
- Insurance
- Location
- United States
- Published
- November 2024
Reported outcomes
90%
quantified impactAutomation & deflection
Strategic outcomes
Catalog median for automation & deflection deployments: +68% across 125 reported metrics. Compare benchmarks →
Primary read
Use case focus
Showing 3 of 4
- 1Document Processing
- 2Risk Assessment
- 3Pricing Optimization
- Life insurers face challenges modernizing manual underwriting workflows that involve extensive data entry, risk scoring, pricing, and policy document creation.
- Manual processing is time-consuming, error-prone, and limits the ability to price policies accurately and personalize customer experiences.
- Insurers use Amazon Textract and Amazon Rekognition to automate extraction of data from government IDs, medical records, and application forms reducing manual data entry.
- Machine learning models built with Amazon SageMaker leverage large and diverse data sets including wearable, genetic, and health data for advanced risk scoring and pricing.
- Amazon Bedrock's generative AI models create personalized policy documents automatically.
- AWS Step Functions orchestrate workflows integrating data extraction, ML scoring, policy generation, and communications delivery.
- Canara HSBC Life Insurance reduced manual data entry by 70%.
- Elevance Health automated 90% of medical record processing.
- Root Insurance Co. used ML for risk pricing, enabling customers to save up to 52% on premiums based on behavior.
- Sumitomo Life combined Amazon S3 and SageMaker to personalize health services using large health datasets.
- Overall improvements include faster underwriting decisions, reduced costs, increased productivity, personalized pricing, and improved customer experiences.
Architecture
The architecture combines Amazon Textract and Amazon Bedrock LLMs for document extraction and summarization, Amazon SageMaker for ML models in risk scoring and pricing, AWS Step Functions for workflow orchestration, AWS Data Exchange for external data integration, and Amazon S3 for a scalable insurance data lake.
Implementation partners1
Sources & evidence1
The same organization appears in newer AI deployment evidence.
- Same organization re-documented as recently as 2026.
Measures whether this deployment's public evidence persists — not whether the system is still in production.
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