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3 Questions Life Insurance Carriers Should Answer

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3 Questions Life Insurance Carriers Should Answer

Author

Felicia McElhaney
Senior Director, Life Underwriting Innovation, LexisNexis Risk Solutions

September 2026

Every life insurance application requires carriers to answer three key questions:

  • Do I need more evidence?
  • In which risk class does this applicant belong?
  • Was this the right underwriting decision?

Increasingly, life insurance carriers are exploring how a combined data approach can reveal patterns of risk that may not be evident from reviewing medical or nonmedical data alone.

A combined data approach in life insurance underwriting means simultaneously modeling medical and nonmedical data against observed mortality outcomes to assess risk. That assessment may be expressed through scores, reason codes and other risk indicators that support underwriting decisions.

In a previous article, we explored how a combined data approach can help differentiate life insurance applicants with similar medical profiles by examining the interaction between medical and nonmedical risk factors.

In this article, we look at how to apply the combined data approach to life insurance underwriting workflows. Specifically, how can it help life underwriters answer those three key questions more consistently and confidently?

Choosing the Evidence

Determining what evidence is needed often means balancing confidence in the risk assessment against the time, cost and complexity of ordering additional requirements. The challenge is identifying which life insurance applicants need more evidence and which don’t.

Consider a life applicant who discloses depression.

Without a combined data assessment, the disclosure triggers a standard set of additional requirements. The case remains open while records are collected and reviewed, increasing evidence costs, extending underwriting cycle times and requiring the underwriter to review and reconcile information from multiple sources.

With a combined data assessment available early in the process, delivered as a score to an underwriting system or to the underwriter with reason codes, the carrier sees indicators that overall risk is lower than expected. After a targeted review of the supporting score, indicators and available medical information, which can enable an automated decision if ingested, the life underwriter confirms the condition appears well managed and determines no additional evidence is required.

Classifying the Risk

Life applicants with similar medical profiles don’t always represent the same level of mortality risk. Accurate risk classification and pricing depend on being able to differentiate risk more precisely.

Consider an applicant with Type 2 diabetes. Based on the available medical evidence, the condition appears stable.

Without a combined data assessment, the applicant is assigned to a standard risk classification based on the available medical evidence.

With a combined data assessment, the underwriter sees additional risk indicators that aren’t apparent from the medical profile alone. After a targeted review of the available medical and nonmedical evidence, the underwriter determines the applicant’s overall risk is higher than standard risk. The applicant is assigned to a different classification that more accurately reflects the overall risk profile, helping align pricing with risk.

Making the Right Decision

Post-issue audit can help life insurance carriers evaluate underwriting outcomes, but traditional audit processes are often limited to relatively small samples of business due to manual review processes and tight underwriting resources. That can make it difficult to identify broader patterns and apply those insights consistently.

A life carrier reviews a block of issued business as part of its post-issue audit process.

Without a combined data assessment, which can automate the initial risk assessment at scale, the carrier selects a sample of cases for manual review. One file, involving an applicant with hypertension and a lien, raises questions about whether the original classification accurately reflected the applicant’s risk profile. However, determining whether similar cases exist elsewhere in the portfolio would be time-consuming and labor-intensive.

With a combined data assessment, the carrier can evaluate a much larger population of issued policies using a consistent view of risk. The accompanying data provided as part of the combined data process enables the carrier to more easily identify a pattern among applicants with hypertension who were issued liens and whose mortality outcomes are less favorable than originally expected. With these insights, the carrier refines its underwriting guidelines and introduces clearer rules for how similar cases should be classified in the future.

The Combined Data Approach

These examples show how a combined data approach can help answer key questions throughout the life insurance underwriting process: what evidence is needed, how to classify the risk and whether the right outcome was achieved. By simultaneously modeling medical and nonmedical data, carriers can apply a consistent view of risk across different stages of the underwriting process.

For many carriers, post-issue audit is the most practical place to begin. Because underwriting decisions have already been made, carriers have more time to evaluate results, test assumptions and understand how a combined data approach performs in practice. Those insights can help refine underwriting guidelines, build confidence in the approach and inform how combined data is applied elsewhere in the underwriting process.

Combined Data Website

Go deeper with the new white paper, webinars and related resources on how combined medical and nonmedical data can help answer some of life insurance underwriting's most important questions. Explore practical applications, workflow considerations and why many life carriers are starting with post-issue audit.

Ready to Get Started?

Gain a more complete view of risk with the LexisNexis Risk Solutions combined data model approach, which combines medical and nonmedical data into a unified risk assessment expressed through scores, reason codes and other risk indicators to support life insurance underwriting decisions.

 

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