A Clearer View of Risk Through Combined Data Models

Help strengthen underwriting decisions by modeling medical and non-medical data together

Life insurers have access to more underwriting data than ever before. The challenge today is to use that information to make faster, more consistent decisions that are better aligned to risk.

This white paper explores how modeling medical and non-medical data together can help carriers assess risk earlier, make more targeted evidence decisions, improve risk classification and better understand how underwriting decisions relate to mortality and portfolio outcomes.

What is this white paper about? This white paper shows how modeling medical and non-medical data together can give life insurance carriers a clearer view of applicant risk and support more consistent underwriting decisions.

What are combined data models in life insurance underwriting? In this context, combined data models refer to modeling medical and non-medical data together to better understand applicant risk.

What will life insurers learn from this white paper?

How combined data models can support better underwriting decisions

See how modeling medical and non-medical data together against mortality outcomes can create a more complete assessment of applicant risk and support more confident underwriting decisions across the policy lifecycle.
 
With combined data models, carriers can gain a more connected view of risk and apply insights more consistently to help:
  • Make more targeted evidence decisions.
  • Classify risk more consistently.
  • Evaluate underwriting decisions at scale.
  • Strengthen alignment between risk assessment and mortality outcomes.
  • Support accelerated underwriting initiatives.

Practical use cases for life insurers

Evidence Optimization

Use earlier risk insight to help make more targeted and evidence-based acquisition decisions while maintaining confidence in the underwriting process.

Risk Classification

Reveal lower- and higher-risk segments that may not be apparent through isolated data sources, enabling more precise and consistent risk classification. 

Post-Issue Audit

Make post-issue audit more scalable by helping life insurance carriers evaluate, refine and improve underwriting decisions over time.

Key outcomes for life insurers

Key takeaway: Modeling medical and non-medical data together can help carriers classify risk more consistently, make more informed evidence decisions and better connect underwriting activity to portfolio results.

The white paper outlines practical ways this approach can help life insurers:

  • Apply a consistent risk assessment methodology across underwriting workflows.
  • Identify opportunities to improve evidence strategies and risk segmentation.
  • Create a feedback loop between underwriting decisions and portfolio outcomes.
  • Evaluate and refine underwriting approaches using real-world performance insights.

Who should read this life insurance underwriting white paper?

This white paper is designed for life insurance leaders responsible for underwriting strategy, risk assessment, transformation, modernization, portfolio performance and accelerated underwriting programs, including:

  • Chief Underwriters
  • Underwriting Operations Leaders
  • Actuarial Leaders
  • Chief Risk Officers
  • Product and Pricing Leaders
  • Innovation and Transformation Teams
  • Accelerated Underwriting Program Leaders

Frequently Asked Questions

In this white paper, the term refers to modeling medical and non-medical, or behavioral, data together to create a more complete view of applicant risk.

Current mortality models for life insurance underwriters focus on non-medical sources while combined data models incorporate medical data and the interaction between medical and non-medical data. 

This white paper is intended for life insurance leaders focused on underwriting transformation, evidence optimization, risk classification, portfolio performance and accelerated underwriting initiatives.

Download A Clearer View of Risk Through Combined Data Models

Explore how modeling medical and non-medical data together can help strengthen underwriting consistency, improve risk segmentation and support more informed decisions across the underwriting lifecycle.

Download the underwriting insights white paper!