What problem does it solve?
For fully underwritten life and health cover, much of the work is reading medical evidence. An attending physician statement or a set of health records can be long, mixing handwritten notes, scans and lab printouts in no particular order. Underwriters or nurse reviewers read it to find the handful of facts that matter (diagnoses, dates, medications, test values, smoking status) and then look them up in the insurer's underwriting manual.
That reading is slow, expensive and can differ between reviewers, and applicants wait while it happens. Electronic health records can make evidence available sooner, but someone still has to read it.
How does it work?
- Collect and digitize. Medical records arrive from providers, labs and record retrieval vendors; the system converts scans and handwriting to text and splits the file into encounters.
- Extract clinical facts. A model extracts diagnoses, procedures, medications, vitals and lab values with dates, and codes them to a standard vocabulary, each linked to its source page.
- Build the timeline. Facts are ordered into a timeline and checked against the applicant's own disclosures to highlight differences.
- Map to the manual. Retrieval over the underwriting manual suggests the relevant impairment guidance and any further evidence needed, without setting the final rating.
- Underwriter decides. The underwriter reviews the summary, opens the source pages where it matters and makes the decision; simple, clean cases can go to straight through rules that the insurer already governs.
- Audience
- Employee facing
- Autonomy
- Copilot
- Adoption
- Early adopters
- Channels
- Internal tools
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Cycle time reduction | Too few to pool | about 50% | 1 | 1 organization |
Value drivers: Speed and cycle time, Employee productivity, Customer experience, Compliance quality.
Indicative value
A life insurer that fully underwrites 20,000 applications a year with medical records
USD 300,000 to USD 1.6 million
Underwriting review time released per year
How this is calculated
Formula: applications * reviewHours * timeSavedShare * costPerHour. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Applications with medical records reviewed per year applications, applications per year | 20,000 | 20,000 | The reference insurer. |
| Underwriter or nurse reading time per file reviewHours, hours per file | 1 | 2 | Editorial assumption. Replace with a time study of your own medical evidence review. |
| Share of reading time the summary removes timeSavedShare, fraction of reading time | 0.3 | 0.5 | Editorial assumption. Aviva says its tool reduced the time underwriters spend reviewing each case by around half, the top of this range; replace it with the result of your own parallel run. |
| Fully loaded cost of an underwriting hour costPerHour, USD per hour | 50 | 80 | Editorial assumption. Replace with your own fully loaded cost. |
What it leaves out: Time released only. It leaves out the usually larger effect of faster decisions on placement rates (fewer applicants dropping out), record retrieval costs, platform costs and the effort of validating the model for a high risk use.
Who already uses it?
4 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Aviva
United Kingdom · Insurance · 2026
Aviva launched a generative AI underwriting summarisation tool for individual life insurance applications in November 2025, which it calls the first of its kind in the protection market, and extended it to individual critical illness applications in March 2026. The tool analyses medical reports and turns them into short summaries for underwriters. Aviva says this has reduced the time underwriters spend reviewing each case by around half, and that the critical illness extension showed the same high levels of accuracy in extensive testing. Aviva's chief underwriting officer, Robert Morrison, says the next focus is summarisation for income protection.
- Cycle time reduction: about 50%
"This has reduced the time underwriters spend reviewing each case by around half."
Claimed by: organization
Prudential plc
Hong Kong SAR China · Insurance · 2025
In its 2025 full year results, Prudential said it continues to enhance its health underwriting with AI powered solutions designed to increase underwriting automation and efficiency, and that it launched MedScreen+ in Hong Kong, an AI underwriting tool intended to provide underwriters with a faster, simpler and more transparent process and to support its financial consultants with instant, indicative underwriting results for customers. In its 2024 results it reported that around 74 per cent of new business policies were processed through auto underwriting capabilities. No outcome figures are given for MedScreen+ itself.
No outcome disclosed.
SBLI
United States · Insurance · 2025
SBLI, the Savings Bank Mutual Life Insurance Company of Massachusetts, partnered with Swiss Re in January 2025 to deploy Underwriting Ease, a platform designed to summarise risk data and key risk aspects in one view for life underwriters and accelerate decisions, integrated into SBLI's existing Life Guide workflow. SBLI's chief underwriter, Brian O'Connell, says the tool provides a data driven approach that streamlines risk assessment and speeds decisions. Swiss Re separately says Underwriting Ease can optimize manual underwriting effort by up to 50%, a general claim about the platform's capability rather than a figure reported for SBLI's own deployment.
No outcome disclosed.
Manulife
Canada · Insurance · 2024
Manulife's 2024 annual report says generative AI in Singapore automates document digitization and summarization in underwriting, improving the accuracy of underwriting decisions and reducing processing time for policy applications, and that in the US it expanded the use of electronic health records and used generative AI to automate preliminary underwriting assessments. In 2025 it partnered with Munich Re Life US on alitheia, an AI driven risk assessment platform, raising the instant underwriting decision eligibility limit from US$3 million to US$5 million. No time or accuracy figures are disclosed.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- Historical medical files with the underwriting decisions made on them, for testing
- The underwriting manual (the insurer's own or a reinsurer's) in a form the insurer may use for retrieval
- A clinical vocabulary and mapping for impairments used in the manual
- Consent and authorization records for every medical record used
Systems to integrate
- New business and underwriting workbench
- Medical record retrieval vendors and electronic health record sources
- Document management with page level retention
- Rules engine for straight through decisions
Complexity: High
Clinical extraction from poor scans is hard, errors carry real consequences for applicants, and in the EU the use is high risk under the AI Act. Special category health data raises the bar on data protection, residency and access control. Expect formal model validation and a long parallel run.
- 1
Start with summarization, not decisions
The first release produces a cited summary and timeline for the underwriter. Straight through decisions stay with existing, validated rules until the summary is proven.
- 2
Build a gold standard set
Have senior underwriters and medical officers annotate a few hundred real files so recall of critical facts (for example a cancer history or abnormal lab value) can be measured, not guessed.
- 3
Measure what is missed, not just what is right
A missed impairment is far worse than an extra one. Track recall on critical conditions separately and set release thresholds with the chief underwriter and medical officer.
- 4
Run in parallel
For a period, underwriters review files the usual way and compare with the summary. Only reduce reading once differences are understood and documented.
- 5
Govern it as a high risk system where applicable
In the EU, meet the AI Act requirements for high risk systems (risk management, data governance, logging and human oversight) and, as the deploying insurer, carry out the fundamental rights impact assessment that Article 27 requires. Elsewhere follow the insurer's AI governance framework and local rules, such as Colorado's Regulation 10-1-1 where external consumer data or predictive models are involved.
Guardrails
- Every extracted fact links to the page and passage it came from
- The summary never sets the rating or declines an applicant on its own
- Health data processed only in approved regions, with access limited to underwriting roles
- Explicit consent or legal basis recorded for every record processed
- No inference of protected characteristics or genetic information beyond what law permits
KPIs to instrument
- Median days from application to decision, before and after
- Reading time per file from workbench logs
- Recall of critical impairments on the audited sample
- Share of summaries corrected by underwriters, by error type
- Placement rate (offers accepted) for summarized versus non summarized cases
Human in the loop
Underwriters make every decision and review source pages for any material fact. Medical officers own the clinical vocabulary and the gold standard set, and the chief underwriter signs off release thresholds and reviews a monthly sample of summaries against full reads.
Common failure modes
- Missed conditions in poor scans
- Handwritten notes and faxed pages drop out of extraction and the summary looks complete. Flag low quality pages and require a human to read them.
- Automation bias in the underwriter
- A clean summary is trusted and the source is never opened. Sample decisions and show the pages behind each material fact.
- Unfair outcomes through proxies
- Summaries emphasise facts that correlate with protected characteristics. Test outcomes by group and keep the underwriting manual, not the model, in charge of rating.
- Consent and residency gaps
- Records are sent to a model outside the approved region or without a valid basis. Route health data through controlled infrastructure only.
What are the risks and rules?
EU AI Act
High risk
Annex III point 5(c): AI intended for risk assessment and pricing in relation to natural persons in life and health insurance. Article 6(3) exempts some purely preparatory tasks, but never a system that profiles natural persons. Extracting an applicant's health conditions and mapping them to the underwriting manual evaluates their health, which is profiling, so treat the system as high risk. Under the timeline as amended, the obligations for Annex III high risk systems apply from 2 December 2027, and Article 27 requires deployers of point 5(c) systems to assess the impact on fundamental rights before first use.
Guidance
- Annex III, high risk AI systems referred to in Article 6(2) (European Union, Europe). Point 5(c) lists life and health insurance risk assessment and pricing of natural persons as high risk.
- Article 27, fundamental rights impact assessment for high risk AI systems (European Union, Europe). Deployers of high risk systems referred to in Annex III points 5(b) and (c), which includes life and health insurance risk assessment and pricing, must assess the impact on fundamental rights before deploying the system.
- Implementation timeline of the EU AI Act (Future of Life Institute (artificialintelligenceact.eu), Europe). Lists 2 December 2027 as the date from which Chapter III, Sections 1 to 3, apply to high risk systems classified under Article 6(2) and Annex III.
- Opinion on Artificial Intelligence governance and risk management (European Insurance and Occupational Pensions Authority, Europe). Supervisory expectations for AI in insurance, including data governance, fairness, record keeping and human oversight.
- SB21-169, Protecting Consumers from Unfair Discrimination in Insurance Practices (Colorado Division of Insurance, North America). Amended Regulation 10-1-1, effective 15 October 2025, sets governance and risk management requirements for life insurers' and health benefit plan insurers' use of external consumer data and information sources, algorithms and predictive models. The Division has also held stakeholder meetings on life insurance underwriting.
Controls to put in place
- Registration as a high risk system in the EU and a conformity assessment before use
- Fundamental rights impact assessment under Article 27 of the AI Act before the insurer first uses the system, including when it is bought from a vendor
- Documented data governance for training and test medical data, including consent
- Logging of every summary, its sources and the underwriter's decision
- Periodic fairness testing of decisions made with the summary
- Data protection impact assessment for special category health data
- In the US, a valid HIPAA authorization from the applicant for every record requested from a provider: life insurers are generally not HIPAA covered entities themselves, while health insurers acting as health plans are and must meet the Privacy and Security Rules directly
Frequently asked questions
- Can AI decide life insurance applications from medical records?
For some applications, AI does decide. Manulife says its partnership with Munich Re Life US on alitheia, an AI driven risk assessment platform, raised instant underwriting decision eligibility from US$3 million to US$5 million. Summarization tools, such as Manulife's generative AI in Singapore, support an underwriter instead, and Prudential launched MedScreen+ to provide a faster, simpler and more transparent process for its underwriters. In the EU, AI used for risk assessment and pricing of individual life or health cover is high risk under the AI Act, so keep an underwriter accountable for decisions the summary informs.
- How much faster does underwriting get?
Aviva is the only insurer on this page that publishes a figure: it says its generative AI summaries of medical reports reduced the time underwriters spend reviewing each case by around half. Manulife says its generative AI in Singapore reduces processing time for policy applications but gives no number, and Prudential discloses no outcome for MedScreen+. Measure it yourself in a parallel run, and separate reading time from time spent waiting for records.
- What is the biggest risk?
Missing a material condition. Measure recall on critical impairments against a gold standard, keep source pages one click away and sample decisions, because a fluent summary is easy to trust too much.
How to cite this page
Blits.ai AI Use Case Library, "AI summarization of medical evidence for life and health underwriting", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/life-underwriting-medical-record-summarization. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 29 September 2026: Added deployments: Aviva, SBLI.
- 27 September 2026: First published