See mortality risk more clearly by combining medical and non-medical data in life insurance underwriting
Risk factors interact, but most life insurance workflows assess them separately
Medical and non-medical data are considered with most life insurance applications, but often inside an underwriter’s head or through rule plans that can’t fully account for how those data points interact. That makes it difficult to make consistent decisions across applicants, workflows and underwriter experience levels. It has also contributed to a plateau in accelerated underwriting, when underwriters don’t have the ability to assess and place cases quickly.
Combined data can help life insurance carriers make faster, more targeted decisions, by evaluating these risk factors together
A combined data modeling approach normalizes medical and non-medical data, evaluates how they interact, and provides a risk score with supporting indicators and reason codes. That creates a more complete view of risk that enables more consistent, confident decisions across cases, workflows and underwriters.
Instead of fragmented, sequential assessment of medical and behavioral risk signals, carriers can systematically uncover interactions between the data—and then apply insights at scale to more accurately classify risk, identify higher- and lower-risk segments and compete more effectively for business.
Beyond Non-Medical Data for Life Insurance Underwriting
Assessing risk factors separately can miss important mortality differences
1.2x higher mortality risk
1.5x higher mortality risk
1.8x higher mortality risk
How combined data supports more consistent life insurance underwriting decisions
By providing earlier insight into how risk factors interact, the combined data approach helps carriers make more precise underwriting decisions.
- New business: Faster, more consistent underwriting decisions
- Evidence optimization. Identify where additional evidence may improve risk assessment and where it may add unnecessary friction or delay.
- Risk classification. Make more consistent, explainable and competitive underwriting decisions through more precise risk segmentation.
- Post-issue audit.
- Increase throughput of audits by automating the initial risk assessment by leveraging medical and non-medical data modeling.
- Pinpoint where underwriting decisions may have deviated from actual risk to strengthen the feedback loop between underwriting and audit.
The path forward and how we can help:
- Validate the combined data approach using retrospective analysis of your book of business
- Pilot the combined data modeling approach at post-issue audit as a practical starting point to test the concept against real cases
- Apply the combined view of risk into upstream new business underwriting workflows
Combining risk factors can reveal compounding mortality risk
3.7x higher mortality risk
2.3x higher mortality risk
5.5x higher mortality risk
Applying a combined view of risk across decisions can improve life insurance underwriting over time
Today, evidence decisions, new business risk classification and post-issue audit assessments are often handled separately, making it difficult to regularly apply learnings from one underwriting decision to the next at scale. By applying a view of risk across these two types of decisions that leverages combined data modeling, carriers can apply standardized risk scores and underwriting insights more consistently across underwriting workflows.
This can help carriers:
- Improve visibility into mortality risk to support more precise and competitive underwriting decisions
- Focus underwriting teams’ attention on complex or higher-risk cases
- Identify where underwriting decisions and actual risk outcomes may diverge over time
- Strengthen the connection between new business underwriting and post-issue audit
