AI in Critical Illness Underwriting

Artificial Intelligence is starting to shape parts of the life insurance and critical illness insurance application process. In the protection market, one area receiving closer attention is AI underwriting: the use of technology to organise, analyse or summarise information during the underwriting journey. 

This follows wider discussion around how AI is changing life insurance, particularly where technology is used to support applications, underwriting and customer journeys. 

For applicants, this should not be read as meaning that a computer is making the final decision. In critical illness underwriting, AI is more likely to work in the background, helping underwriters deal with a medical report, GP report, or detailed medical records before terms are offered.

Critical illness underwriting is often more involved than people expect. An insurer may need to review medical history, pre-existing medical conditions, family medical history, blood pressure, lifestyle, occupation, previous tests and ongoing symptoms. The outcome might be standard terms, a higher premium, an exclusion, a postponed application or, in some situations, a decline.

Recent UK market announcements show that AI underwriting tools are already being used to summarise medical reports in individual protection applications. The relevant point for applicants is not which provider is using the technology, but how these tools are being used: to support underwriters reviewing medical evidence, not to remove human judgement from the process.

Different insurers may use AI in different ways, so the process will not be identical across every provider.

What Is Critical Illness Underwriting?

Critical illness underwriting is the assessment an insurer carries out when someone applies for critical illness cover. It reviews the applicant’s disclosures, health background and any supporting evidence before deciding whether cover can be offered, what terms should apply and how much the policy will cost.

Critical illness insurance is designed to pay out if the policyholder is diagnosed with one of the serious illnesses or medical conditions covered by their policy.  As the insurer is accepting a defined medical risk, it needs a clear view of the applicant’s health before agreeing terms.

An application will usually ask about:

• Current health

• Previous medical conditions

• Family medical history

• Height and weight

• Smoking status

• Alcohol use

• Occupation

• Hobbies or higher-risk activities

• Previous medical tests, investigations or treatment

A clean application can sometimes move through underwriting with little extra evidence. The process becomes slower when the insurer needs more detail: a recent symptom, a previous investigation, a long-term condition, a family history disclosure, or something in the answers that needs context.

GP Reports are a common source of delay. They can contain years of notes, prescriptions, consultations and referrals. Much of that record may be irrelevant to the application, but the underwriter still has to identify what matters, understand the medical context and apply the insurer’s underwriting rules.

How AI Is Being Used in Underwriting

AI underwriting is not one fixed process. In the protection market, it can cover tools that help insurers summarise documents, identify relevant medical details, check information more consistently, support risk assessment and improve underwriting efficiency.

For critical illness insurance, medical report summarisation is one of the clearest uses. A medical report or GP Report may include years of consultations, test results, referral letters, medication history and follow-up notes. An AI underwriting summarisation tool can help turn that material into a shorter, more usable summary for the underwriter to review. 

That summary might highlight:

• Dates of diagnosis

• Previous investigations

• Treatment history

• Medication

• Test results

• Whether a condition is ongoing or resolved

• Related risk factors

• Follow-up appointments or referrals

That is not the same as AI making the final underwriting decision. A summarisation tool helps process and organise medical records. It does not automatically mean an applicant is accepted, declined or charged more by a machine.

The practical benefit is time. Underwriters can spend less time searching through lengthy records and more time assessing the evidence. Where a medical history is complex or open to interpretation, judgement still needs to sit with a trained underwriter.

Could AI Make the Underwriting Journey Faster?

AI can make parts of the underwriting journey faster, especially where the main delay is the review of detailed medical evidence.

A long GP Report can take time to assess manually. An underwriter may have to work through historic notes, repeat prescriptions, referral letters and test results before deciding which details affect the application. A summarisation tool can reduce the time spent locating key information and allow the underwriter to concentrate on the decision itself.

Recent provider announcements have described AI underwriting summarisation tools as a way to reduce the time underwriters spend reviewing medical reports while keeping underwriters involved in the process.

For applicants, that can mean fewer delays where a case is held up by a lengthy medical report. It can also support straight-through processing where an application is simple enough to move through the system without unnecessary manual intervention.

There are clear limits. AI will not make every critical illness application instant. Cases involving multiple medical conditions, recent symptoms, unclear test results, incomplete information or higher-risk disclosures still need human review. The insurer may also ask for further evidence before reaching a decision.

Explainable AI and the Problem with “Black Box” Decisions

Explainable AI matters because insurers need to understand how an AI system has reached a particular output.

In underwriting, this is not just a technical issue. Applicants, advisers, insurers and regulators need confidence that decisions are fair, consistent and capable of being challenged where necessary. A “Black Box” process, where nobody can properly explain why a result was reached, is difficult to justify when sensitive health data and financial protection are involved.

Some AI systems include explainability tools designed to show which factors influenced a model’s output. For most applicants, the concern is simpler: if AI has influenced the underwriting journey, the insurer should still be able to explain the decision in plain terms. 

A faster decision is only useful if it is also understandable. Applicants need to know why terms have changed, why an exclusion has been applied, or why further medical evidence is needed. Advisers also need enough information to help clients understand the outcome and, where appropriate, challenge or clarify it.

What Are the Risks of AI in Critical Illness Underwriting?

AI brings useful possibilities, but it also creates risks that insurers need to manage carefully.

Bias is one of the main concerns. If an AI underwriting tool is trained on incomplete, poor-quality or unbalanced data, it could produce unfair outcomes for certain groups of applicants. This is often described as disparate impact, where a process appears neutral but affects different groups differently in practice.

Bias testing, model performance checks, monitoring and model retraining are therefore essential. Insurers need to know whether their AI tools are working as expected, whether the results remain fair and whether any issues are emerging across different applicant groups.

Data protection is another major consideration. Critical illness underwriting often involves sensitive medical information, including GP records, consultant reports, test results and details about pre-existing medical conditions. Under the GDPR framework, firms must handle personal and health data lawfully, securely and transparently.

The Association of British Insurers has published practical guidance on responsible AI use in insurance and long-term savings, including governance, risk management and good consumer outcomes.

The FCA has also set out its approach to AI in UK financial markets, with a focus on safe and responsible adoption under existing regulatory expectations.

What Applicants Should Know Before Applying for Critical Illness Cover

AI does not change the applicant’s responsibility to answer questions accurately. Before starting a life insurance application, it can help to understand what information insurers may ask for and how medical disclosures may be reviewed.

If you are applying for critical illness cover, life insurance, income protection, Relevant Life Cover or Key Person Insurance, the insurer may ask about your health, medical history, occupation, lifestyle and family medical history. Those answers need to be complete and accurate. 

For company directors, shareholders, or businesses arranging cover around a key individual, similar issues can also arise in AI in business protection risk assessment, where medical and financial underwriting often need to be considered together. 

A request for a GP Report or further medical information does not mean the application is heading for a decline. Often, the insurer simply needs more detail before deciding what terms, if any, can be offered.

This is where advice can be valuable. An adviser can explain what the insurer is asking for, help the applicant understand the underwriting journey and identify insurers that may be better suited to a particular medical history. That can make a real difference where there are pre-existing medical conditions, previous investigations or medical notes that need context.

AI may make the insurer’s review more efficient, but it does not reduce the importance of accurate disclosure. Leaving out relevant information can affect the validity of the policy and may cause problems if a claim is made later.

Could AI Affect Insurance Premiums?

AI could influence how insurers assess risk and price protection insurance, depending on how each insurer uses the technology.

Predictive analytics and risk scoring can give insurers a more detailed view of an application. Where medical evidence provides a clearer picture of someone’s health, underwriting decisions may become more precise.

That can work in different ways. Some applicants may be offered standard terms because the evidence shows a condition is well controlled or no longer relevant. Others may be offered cover with a rating, an exclusion or a postponement if the risk is higher or still uncertain.

For applicants, the issue is not simply whether the premium changes. It is whether the reason for a higher premium, exclusion or postponed decision can be understood. More detailed risk assessment still needs to be fair, explainable and properly governed.

Critical illness insurance relies on pricing risk accurately. Applicants should also have confidence that decisions are not being made unfairly or without meaningful human oversight.

AI and Claims Processing Times

Although underwriting takes place before a policy starts, AI may also become more common in claims processing.

AI tools could be used to organise claims evidence, summarise medical documents, identify missing information or help claims teams review records more efficiently. This may help reduce delays where a claim involves a large amount of documentation, although the outcome still depends on the evidence and the policy wording. 

Critical illness claims need careful assessment. A claim usually depends on whether the diagnosis meets the definition set out in the policy. Medical evidence remains central, whether AI is used in the process or not.

Applicants should understand this before taking out cover. Critical illness insurance does not cover every illness. It pays out only when the claim meets the specific terms and definitions in the policy.

The Future of AI in the Protection Market

AI is likely to become more common across the protection market, including life insurance, critical illness insurance and income protection.

That does not mean human underwriters disappear from the process. AI is more likely to support underwriter training, medical report review, risk assessment, claims handling and customer service.

Some insurers may use AI tools in shadow mode before relying on them more heavily. In practice, the model runs in the background and its outputs are compared with human decisions before being used in live decision-making. This gives the insurer a way to test performance, consistency and fairness before changing the underwriting process.

Model lineage, monitoring and regulatory examination are also likely to attract more attention. Insurers will need to understand how their models were built, what data they use, how they perform and whether they continue to produce fair outcomes.

For customers, the aim should be a smoother underwriting journey without losing the human oversight that matters in complex or sensitive cases.

Final Thoughts

AI in critical illness underwriting is not about turning protection insurance into a fully automated process. Its most useful role is likely to be practical: helping insurers work through medical reports, GP records and other evidence more efficiently.

Used appropriately, AI underwriting tools can help underwriters focus on the information that matters, reduce avoidable delays and support more consistent decision-making. That has clear potential benefits, but critical illness insurance involves sensitive health data, significant financial decisions and applicants whose medical histories may need careful interpretation.

The test is not simply whether AI makes underwriting faster. It is whether the process remains fair, transparent, properly governed and subject to human judgement.

For anyone considering critical illness cover, the basics remain the same. Understand what the policy covers, answer the application questions accurately, and make sure the cover is suitable for your circumstances and budget. Where your medical history is more complex, getting advice before applying can make the underwriting journey easier to navigate.

FAQs

Is AI already being used in critical illness underwriting?

Yes. AI is beginning to be used in parts of critical illness underwriting, especially where insurers need to review and summarise medical evidence. Recent UK market announcements show AI underwriting summarisation tools being extended to individual critical illness applications.

Does AI decide whether I can get critical illness cover?

Usually, no. The current use described here is focused on helping underwriters review and summarise medical evidence. That is different from replacing human decision-making altogether.

Can AI make critical illness applications quicker?

It can speed up parts of the process, particularly where a long medical report or GP Report needs to be reviewed. Complex applications can still take longer if the insurer needs further information or detailed human assessment.

Do I still need to disclose medical conditions if AI is used?

Yes. You still need to answer all application questions accurately and fully. AI does not remove the need for proper medical disclosure when applying for critical illness insurance, life insurance or income protection.

Could AI affect the cost of critical illness insurance?

Potentially. AI and predictive analytics can help insurers assess risk in more detail, but pricing decisions still need to be fair, explainable and compliant with regulation.

Is AI in underwriting fair?

AI can support faster and more consistent underwriting, but fairness depends on how the tool is designed, tested, monitored and governed. Insurers need bias testing, data protection controls and human oversight to reduce the risk of unfair outcomes.