Underwriting is entering a new phase of maturity. The industry is moving beyond experimentation with accelerated underwriting and digital health data and toward a more integrated model built on disciplined risk assessment, scalable underwriting workflows, and robust monitoring. Artificial intelligence remains much earlier in its evolution, but will likely become a key differentiator for carriers that can identify practical use cases, govern them effectively, and translate them into measurable underwriting value.

Munich Re Life US’s 2026 Underwriting Survey reflects underwriting practices reported by 30 participating U.S. individual life insurers as of January 2026. This survey represents an evolution of Munich Re’s long-running Accelerated Underwriting Survey, which has been conducted biennially since 2018. While accelerated underwriting remained a central focus, this year’s survey broadened the scope to capture a more comprehensive view of underwriting modernization. This paper highlights selected high-level themes, while the detailed survey results and benchmarking opportunities are reserved exclusively for survey participants. 

The research provides a broad view of how underwriting practices are evolving as carriers seek to improve speed and customer experience while maintaining mortality protection and operational control.  Across the findings, one message is clear: the next generation of underwriting will not be defined by a single technology. Competitive advantage will come from how effectively insurers connect data, decisioning, workflow, and artificial intelligence with human expertise.

Four key trends shaping the life insurance industry

• Accelerated underwriting has become a core operating model, with attention shifting from adoption to optimization.

• Digital health data is changing how evidence is selected, combined, and interpreted. Electronic health records are moving from emerging input to essential infrastructure, but usability remains a constraint.

• Traditional underwriting requirements are becoming more targeted and data-informed, not obsolete.

• Artificial intelligence (AI) adoption is advancing, but inconsistently across underwriting programs.  AI is creating value first as a productivity and decision-support tool for underwriters.

For the purposes of the survey, "accelerated underwriting" – or AUW – is defined as the waiving of traditional underwriting requirements (e.g., fluids, medical exams) for a subset of applicants that meet favorable risk requirements in an otherwise "fully underwritten" – or FUW – life insurance process.  AUW can also apply to processes that don’t have a FUW alternative path, thereby waiving traditional requirements on all applicants and automatically declining those that don’t meet the AUW risk requirements. Simplified issue and guaranteed issue programs were not in scope. 

One of the clearest signals from the survey is this: AUW is no longer “new,” as all but one of the survey participants reported having an AUW program in production as of January 2026. Nearly all carriers have implemented AUW pathways, and the conversation has shifted. With accelerated programs now widely established, carriers are focusing on the next challenge: expanding acceleration while preserving mortality performance.

Accelerated underwriting has moved from a differentiated innovation to a foundational capability in the U.S. individual life market. As adoption has broadened, carrier priorities have evolved. Program leaders are increasingly focused on expanding access thoughtfully, improving underwriting evidence strategies, strengthening monitoring, and directing underwriter attention to the cases where judgment creates the most value.

AUW eligibility by face amount has continued to broaden since the original AUW Survey in 2018, but widening the front door is only one measure of progress. Sustainable performance depends on what happens after an applicant enters the accelerated pathway: how evidence is ordered, how exceptions are managed, and how many applicants ultimately receive an accelerated offer. Acceleration rates (as a percentage of eligible applicants) have plateaued in recent years and continue to be a challenge for the industry. See Figure 1 for details on eligibility and acceleration trends.

Another survey finding is that accelerated underwriting does not necessarily mean fully automated underwriting. Human underwriter review remains prevalent in the majority of accelerated cases, particularly when applicants approach program eligibility boundaries or digital evidence introduces complexity that signals a need for additional underwriter or clinical interpretation. The strongest operating models use technology to streamline routine applications while preserving human judgment for nuanced cases. The next phase of AUW progress may therefore depend less on simply expanding eligibility and more on improving evidence usability and data optimization in underwriting workflows.

Underwriting tools and data source usage remain a key focus of the 2026 Underwriting Survey. The results highlight how AUW has shifted from simply waiving fluids to orchestrating multiple evidence sources to determine which applicants can proceed without traditional requirements. Mature AUW programs often rely on a layered data strategy: some tools are used broadly and routinely to screen applicants, while others are deployed selectively when age, amount, risk indicators, or prior findings suggest that additional evidence is needed.

Given the importance of understanding how and why evidence is ordered, the survey continued to capture how underwriting tools and information sources are being used to triage or classify risks in the accelerated pipeline (i.e., routine or selective usage, evaluating, or not using). In this year’s survey, “selective” usage was differentiated into specific reasons, including age and amount requirements (AAR), for-cause requests, and other reasons. While the staple AUW tools – Medical Information Bureau (MIB), prescription history (Rx), and motor vehicle records (MVR) – continue to be used routinely by the vast majority of carriers, newer digital health data – medical claims data (Dx), clinical labs, and electronic health records (EHRs) – is now also used by a large majority of carriers, primarily on a selective basis and for varying reasons. While lab data is most commonly used as an AAR, EHRs are more likely to be requested for-cause in AUW decisioning. When evaluating aggregate usage trends (routine and selective) over time, the steepest growth continues to be seen in the newer digital health data sources – medical claims, clinical labs, and EHRs – as detailed in Figure 2. For visual clarity, Figure 2 highlights three benchmark years - 2018, 2022, and 2026 - while the accompanying discussion draws on the full survey history.

Modern underwriting increasingly relies on a layered evidence ecosystem. Application data, MIB information, motor vehicle reports, prescription history, medical claims, laboratory data, EHRs, and financial data each contribute different underwriting evidence. The strategic challenge is to determine which sources to use, when to use them and how to translate them into consistent underwriting action.

Digital health data can strengthen risk assessment by revealing clinical history, treatment patterns, and potential nondisclosures that may not be evident from application data alone. At the same time, richer evidence can surface complexity that appropriately leads to additional review. The value of digital data should therefore be measured not only by speed or straight-through processing, but also by the quality and confidence of the resulting underwriting decision, which can be monitored through pre- and post-issue auditing practices.

Electronic health records are becoming more embedded in underwriting, including for-cause review, post-issue monitoring, and as a supplement or alternative to traditional medical records. Compared with the prior AUW Survey results, EHR usage showed the greatest increase across all data sources over the past two years, rising by +27% on a selective basis. 

The industry’s challenge is also shifting – for the first time since introducing this question in the 2022 survey, “hit rates” did not top the list of EHR challenges. Instead, participants ranked “completeness of records” as the top challenge when using EHRs in their underwriting programs. Basic access remains important, but the greater opportunity now lies in record completeness, relevance, usability, and integration with underwriting workflows and rules engines. 

Also, for the first time, EHRs surpassed attending physician statements (APSs) as the most commonly used post-issue auditing tool, now leveraged by 66% of carriers, compared with 55% of carriers using APSs in their post-issue monitoring processes. This suggests that digital evidence is becoming more deeply embedded across the underwriting lifecycle. These findings indicate that the industry's focus is increasingly shifting from expanding access to digital evidence toward maximizing the value and usability of the information it provides.

Digital evidence is enabling more selective requirements while reinforcing the need for consistent risk standards.

The modernization of underwriting is often viewed as a shift away from traditional requirements; however, the survey findings suggest a more nuanced evolution. Preferred risk segmentation remains broadly aligned across accelerated and fully underwritten pathways, indicating that carriers continue to value common risk classification standards even when the evidence used to reach an underwriting decision may differ.

Age and amount requirements (AAR) are likely to become more adaptive as digital data improves. Participants were asked how they expect AAR to evolve in the future within their underwriting programs. Carriers largely expect AAR to apply more selectively by segment as digital data sources increasingly determine when additional requirements are needed. Instead of applying the same evidence threshold to every applicant within a broad segment, carriers can consider applicant characteristics, product design, distribution, available evidence, and emerging risk signals. Traditional requirements remain an important control, but they may be applied with greater precision.

Special underwriting programs, such as preferred risk enhancement, table improvement, and lifestyle credits, are used by 70% of carriers as targeted competitive levers to enhance offers and support placement. Their role is generally focused and controlled, with limitations on the maximum improvement allowed typically ranging between 1-3 tables or risk classes. This reinforces the principle that underwriting innovation works best when supported by clear eligibility, pricing, and governance parameters.

Wellness programs offer a potential bridge between underwriting, customer engagement, and in-force risk management. However, the market remains exploratory with only a few carriers reporting an active wellness engagement program as of 2026. Insurers are using, evaluating, or have evaluated a variety of wellness engagement tools, including multi-cancer early detection tests, other screening tests, and wearable devices, as underwriting continues to broaden beyond point-in-time risk assessment.

The survey findings point to an underwriting future that is likely to be more flexible, not less disciplined, as the industry continues to see the blending of digital and traditional underwriting pathways. The strongest frameworks will use new data to determine when additional evidence or intervention is warranted while retaining clear guardrails for risk selection, pricing, and customer treatment. Underwriting requirements will become more dynamic, monitoring more continuous, and collaboration across life insurance disciplines – including underwriting, actuarial science, and data science – even more critical.

Lastly, the survey explored the current and anticipated role of artificial intelligence in the life insurance underwriting process. The following definitions were provided to participants:

Artificial intelligence (AI): Computer systems or algorithms that can perform tasks which typically require human intelligence. In underwriting, this includes technologies such as predictive modeling, machine learning, natural language processing, and even rules-based automation used to analyze data, detect patterns, assess risk, or support decision-making.

Generative artificial intelligence (GenAI): AI systems designed to create new content or insights—such as text, images, code, or summaries—based on patterns learned from data. In underwriting, this may include using large language models (LLMs) or similar tools to draft communications, summarize case files or medical records, assist in rule development, or generate synthetic data for testing.

Agentic artificial intelligence (agentic AI): AI systems that are goal-driven and can act autonomously, making decisions and taking actions on behalf of an organization, without direct human intervention. In underwriting, this may include AI systems that can initiate or complete tasks, such as automatically ordering medical exams, requesting additional information, or making binding underwriting decisions, based on their analysis of data and predefined rules or objectives.

Artificial intelligence is becoming part of the underwriting toolkit, but adoption remains pragmatic. Overall, the survey suggests that AI adoption is advancing, but not uniformly across underwriting programs. See figure 3 for the current state of AI adoption in underwriting. 

Traditional AI, GenAI, and agentic AI are all gaining attention, but their use today appears concentrated in narrower, well-defined applications rather than in fully autonomous, end-to-end underwriting. Carriers are prioritizing bounded use cases that can deliver near-term value. Summarizing medical records emerged by far as the leading GenAI use case in underwriting, followed distantly by drafting communications and improving workflows. These applications can reduce friction and cognitive load without immediately transferring accountability for risk decisions to autonomous systems.

To understand what is limiting carriers’ GenAI implementation journeys, participants were asked to rank barriers to GenAI and/or agentic AI adoption in underwriting. The themes were familiar: lack of regulatory guidance on AI-driven decisions and lack of confidence in Gen AI performance topped the list, while lack of internal expertise and concerns about data privacy and security were also identified as key considerations for GenAI adoption in underwriting.

The survey also explored which data sources carriers view as most critical to their AI initiatives in underwriting. The highest-priority sources were all digital health data sources: EHRs ranked first on average, followed closely by medical claims (Dx) data and prescription history, with APSs also scoring strongly. This ordering is directionally consistent with other survey findings: carriers see the greatest opportunity for AI to help interpret large volumes of clinically dense, often unstructured medical evidence.

Lastly, the survey results point to a positive evolution of the underwriter’s role, as emerging technologies create opportunities to allow underwriters to focus their expertise on more complex and higher-value work. Participants were asked how they expect GenAI and/or agentic AI to affect underwriter productivity over the next five years. The responses suggest that carriers are generally optimistic about GenAI and agentic AI increasing underwriter productivity and creating new roles or career paths for them, rather than simply displacing underwriting expertise.  As AI technology assumes more responsibility for summarization, evidence organization, and routine workflow tasks, underwriters can devote more attention to complex risk assessment, higher value cases, quality review, guideline refinement, model oversight, and cross-functional collaboration. This evolution will increase the importance of combining deep underwriting judgment with data fluency and technology governance.

Overall, the survey results suggest that the future of underwriting may not be defined by a single platform, data source, or operating model. It will likely be shaped by how effectively insurers bring together digital evidence, AI technology, and human expertise into a cohesive, well-governed underwriting strategy. 

As digital health data becomes more widely available and underwriting workflows become more connected, the distinction between accelerated and traditional underwriting may continue to narrow. The industry appears to be moving toward more flexible and integrated approaches that align evidence and decision-making with the unique characteristics of each applicant.

Achieving this vision will require strong collaboration between underwriting, medical, actuarial, data science, and technology professionals. The organizations that are likely to succeed will make faster decisions without sacrificing discipline, use richer data without creating unnecessary complexity, and pursue innovation while maintaining confidence in their underwriting risk assessments. The future may not be a choice between accelerated and traditional underwriting, but in integrating them more intelligently.

Munich Re has the expertise and experience to support your underwriting strategy, digital evidence optimization, monitoring, and governance – and to help you navigate responsible AI adoption. We remain committed to delivering exceptional value to our clients, partners, and the industry, and we welcome your continued engagement.