Assessing Mortality Impact of Digital Underwriting Evidence

Guizhou Hu
Taylor Pickett
Jacqueline Waas

In Brief

An RGA study demonstrates how digital underwriting evidence significantly improves mortality outcomes in life insurance underwriting, offering powerful potential to enhance accelerated underwriting programs.

Key takeaways

RGA introduced a unique and creative methodology to estimate mortality slippage as a range.
Digital underwriting evidence (DUE) sources such as medical claims, LabPiQture, and electronic health records can reduce mortality slippage, with electronic health records demonstrating the highest individual impact and increasing decision rates.
Combining multiple DUE sources yields greater mortality improvements than individual sources alone, with the combination of all three sources showing the most substantial reduction in mortality slippage.

Executive Summary

Interest in adopting digital underwriting evidence (DUE), such as medical claims (MC), LabPiQture (LP), and electronic health records (EHR), continues to grow among life insurers. While DUE is expected to improve the mortality performance of accelerated underwriting (AUW) programs, studies assessing the impact of DUE on mortality remain limited. This paper reports on RGA studies designed to address that.

While different study methodologies were considered, the conclusions of this research are drawn from a comparison of underwriting decisions – traditional full underwriting (FUW), and accelerated underwriting (AUW), with or without DUE. Additionally, the research presents a range of mortality impacts, both a larger impact that reflects the value of new declines discovered via DUE and a smaller impact that assumes no value from these new declines. The true impact is likely somewhere between these two reference points.

Results will vary from one carrier to the next, but in RGA’s analysis of two notably different blocks of business, all three DUE sources demonstrated significant value, both individually and in combination.

EHR exhibited the largest single evidence mortality impact, with an accompanying increase in decision rate – the portion of cases that need no further information to complete underwriting. LP and MC showed strong value as well, and they may offer greater potential to improve underwriting efficiency through automation. Notably, combining multiple DUE sources resulted in a greater mortality impact than any source individually.

The potential for DUE to improve both customer experience and mortality outcomes is clear. The question now is: How can it best help individual carriers achieve their unique goals? Analysis of their own business will provide the most insightful answers, and RGA’s experts stand ready to help clients take the next step in this journey.

Come to co-author Jacqueline Waas’ panel discussion on “Bringing Power to Protective Value” at the 2025 AHOU conference, 9:30 to 10:30 a.m. Tuesday, May 6.

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Study methodologies review

Method #1: True mortality (i.e., experience study)

Retrospective and prospective longitudinal study
Benefits – Assesses the true mortality across underwriting classes and compares DUE to traditional underwriting.
Challenges – Takes years of exposure across tens of thousands of lives, making it an expensive and time-consuming study.

Method #2: Underwriting decisions as a mortality surrogate

Mortality is assessed by applying relative risk (RR) assumptions to each underwriting class, including postpone and decline, either with or without DUE. For a group of cases, a summation of RR or ∑RR is regarded as the mortality of the group. The impact of DUE can then be assessed by comparing mortality between two groups, one with and the other without DUE, which can be expressed as (∑RR with DUE) vs (∑RR without DUE).
Benefits – Mortality can be assessed for any group of cases with an underwriting decision.
Challenges – It assumes the mortality by underwriting assessment either with or without DUE is close to true mortality, even though there is very little historical experience to prove it.

Method #3: Full underwriting decisions as a mortality surrogate

Performed using the underwriter’s decision with DUE and comparing it to traditional full underwriting decisions (FUW). This method is commonly used to assess mortality slippage of AUW programs. The mortality slippage is assessed as (∑RR FUW)/(∑RR AUW). DUE can be viewed as an enhancement to AUW.
Benefits – The use of FUW decisions as a mortality surrogate is generally acceptable and widely adopted by the industry to assess the mortality slippage among AUW programs. This view is also supported by several decades of mortality experience corroborating the mortality assumptions assigned to each FUW decision.
Challenges – It requires FUW decision being available.

The main difference between methods #2 and #3 is that method #3 requires a FUW decision and method #2 does not. In addition, method #3 excludes cases where the AUW (or DUE) decision is a decline. In other words, it calculates the mortality slippage only among AUW/DUE accepted cases. By not considering AUW/DUE declined cases, method #3 does not “credit” DUE in the situation where DUE renders a declined decision while the FUW decision is accepted. On the other hand, method #2 gives DUE full credit for a declined decision.

This consideration gains new importance as AUW programs incorporate DUE. When AUW programs relied on less medical information than a comparable FUW approach (as was the case for most AUW programs until the past few years), very few if any cases that would be declined by AUW would have been accepted by FUW. However, more robust DUE added to the process creates much greater potential for new declines to be identified, which would not have been found even with an exam and insurance lab panel.

Study design

RGA’s study used a modified version of method #3. We first used method #3 to calculate mortality slippage, and we treated the results as the high end of an estimated range. We then used assumptions close to method #2 to give “credit” to DUE for newly identified decline decisions to calculate the low end of the mortality slippage estimate. Using a range allowed us to assess the impact of assumptions and methodologies. Allowing for methodology limitations and associated assumptions, we believe the true mortality slippage is somewhere within that range.

Study data

The study consisted of historical FUW data from two carriers, which included the following:

Foundational evidence (FD ) – Application disclosures, MIB check, motor vehicle reports (MVR) and prescription data (Rx)
Digital underwriting evidence (DUE ) – Medical claims (MC), LabPiQture (LP), electronic health records (EHR) – retrospectively ordered for the purpose of the study and not included in the historical FUW decision
FUW evidence – Attending physician statements (APS) and/or insurance lab testing, including paramedical exam

Underwriting review

RGA underwriters then assessed the risk using various combinations of FD + DUE. These were:

FD Decision
FD + LP
FD + EHR
FD + MC
FD + LP+MC
FD + EHR+ LP
FD + EHR + MC
FD + EHR + MC + LP

The results were then compared with the FUW decision for mortality slippage calculation as explained above.

Want to dive deeper? Stop by the RGA booth at the upcoming AHOU conference, May 4-7 in Anaheim, Calif.

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Study results

We performed two studies with data from two different carriers. The results are presented in Table 1 and Table 2:

Number of cases – Number of individuals with given DUE hit
Decision rate FD – Percentage of cases for which a final underwriting decision can be made with FD only
Change in decision rate FD+DUE – The change (increase or decrease from FD only) of the percentage of cases that a final underwriting decision can be made with FD+DUE
Mortality slippage of FD only – Derived from comparison of FD only vs. FUW decisions: Cases with a FD decision of “need more information” were excluded. Cases declined by FD only were also excluded because they would also be declined by all other combinations of evidence, which is why there are no ranges.
Mortality slippage of FD+DUE – Presented as a range, as explained in the methodology section

Results show the morality slippages of FD only were much higher in Carrier 1 than Carrier 2, which also explains the significantly higher FD+DUE mortality slippages for Carrier 1. Even with the very different background mortality slippages, several shared conclusions emerge between the two studies.

As expected, mortality slippage was favorably impacted in all cells. The greatest impact was seen while layering in multiple DUE sources, and EHR showed the greatest impact of any single piece of evidence.

In addition to the impact on mortality slippage, EHR also appears to facilitate more underwriting decisions. In other words, it decreases the number of cases that “need more information.” MC, on the other hand, tends to increase the chances of “need more information.” LP has little impact on this.

Further considerations

It is important to consider the specific block of business when determining the impact of DUE. As illustrated by this study, the impact can be significantly different between carriers and is dependent on variables such as target customer bases, distribution channels, and AUW program rules and guidelines.
Considering the variance in findings and limited sample size, insurers should view the mortality impacts reported in this study as directional trends and not precise estimates of what they may realize. In addition, mortality slippage comparisons among different rows in the tables may be influenced by not coming from the same cases. Some variation could be due to the different group of cases, not a difference in the DUE.
Directionally, the studies found that DUE can be beneficial in reducing mortality slippage. While EHR had the highest impact, the value increases when data from multiple sources is layered in. Additionally, we were able to demonstrate that it is possible to achieve similar morality results to traditional FUW through appropriate use of DUE.
Carriers should perform a comprehensive cost-benefit evaluation of DUE on their population so an optimized DUE decision can be made. This will enable more in-depth consideration of how the use of DUE would impact business priorities beyond mortality, such as cost of evidence and speed and ease of review. This may influence which DUE is prioritized. For example, EHR had the largest mortality impact, but it may also require more underwriter review and, thus, reduce underwriting efficiency relative to other DUE sources.
It is important to note the following:
- Results were contingent on evidence availability (hit rate). Hit rates can differ significantly based on the type of DUE and a company’s customer base.
- “Need more information” and extra decline cases caused by DUE would decrease the overall benefit of DUE because of the reduced offer rate.
  - “Need more information” cases would incur additional requirement costs.
  - Decline cases, while having a mortality benefit, would come at the cost of a lower offer rate.

As insurers navigate these new tools on their journey to a more optimized underwriting approach, RGA is eager to share our expertise and partner with them to achieve their goals.