Life Insurance Consensus Index studies how leading AI platforms recommend life insurance companies for defined consumer situations. We submit a consistent scenario, preserve the responses, structure the recommendations, and measure agreement. Material insurance facts and editorial context are then reviewed as separate layers of the published research.
Define a scenario that can change the answer
Life insurance recommendations depend on age, health, medical history, tobacco use, occupation, activities, location, coverage amount, policy type, and underwriting method. Our studies specify a consumer need rather than simply asking for the best life insurance company.
For example, a healthy nonsmoking 40-year-old seeking $1 million of 20-year term coverage without a traditional medical exam gives each platform the same decision to consider. Controlled comparisons can then vary term length, coverage amount, or underwriting method while keeping other characteristics substantially consistent.
Our research covers term life, whole life, universal and indexed universal life, final expense and burial insurance, and health conditions and high-risk applicants. A separate scenario should introduce a variable that could plausibly change carrier recommendations, rather than repeat an existing question with different keywords.
Use a consistent prompt and research environment
We create a specific, neutral, realistic prompt and submit the same core question independently across the platforms in the research cycle. Open-market studies generally avoid naming preferred carriers. A deliberately carrier-specific comparison should be identified as such.
The standard framework is designed around ChatGPT, Gemini, Claude, Perplexity, Grok, DeepSeek, and Kimi. Each included platform receives equal weight for basic recommendation coverage. Where practical, fresh or controlled sessions minimize unrelated conversation history and personalization, and metadata records the model, mode, interface, search access, and research date.
Preserve the response before interpreting it
The raw platform response is the research record. We aim to retain the prompt, platform, available environment details, timestamp, recommended carriers and products, recommendation order, supporting reasons, qualifications, and citations where available.
Recommendation extraction distinguishes meaningful positive or conditional recommendations from background mentions, comparisons, and negative references. Conditional language remains part of the finding. A recommendation for some applicants with well-controlled diabetes should not become a guarantee for everyone with diabetes.
Equivalent carrier identities are normalized to avoid duplicate counting, while relevant brand, parent-company, product, and issuing-insurer distinctions are preserved. A carrier's appearance for one type of coverage does not imply a recommendation for all its products.
Calculate coverage and recommendation position
Recommendation coverage equals the number of platforms recommending a carrier divided by the number of usable platform responses. Six recommendations from seven usable responses equals 85.7%. If one scheduled platform fails and five of the remaining six recommend the carrier, coverage is 83.3%.
Technical failures, timeouts, refusals, incomplete output, and responses that do not meaningfully answer the prompt may be excluded from the usable denominator. A valid response that omits a particular carrier remains a response without a recommendation for that company.
Coverage is the primary ranking signal. Average recommendation position is the main tie-breaker where ordered lists are available, with top-position frequency and scenario-specific support adding context. Genuine ties are allowed. A carrier generally needs at least two independent platform recommendations for full carrier-fit treatment, although single-platform recommendations may remain in tables and historical data.
Examine the reasons and verify material facts
We examine recurring explanations such as pricing, conversion options, accelerated underwriting, permanent-policy design, financial strength, or treatment of particular applicant profiles. The fact that an AI platform gave a reason does not establish that the reason is current or supportable.
Depending on the study, verification may cover carrier identity, product availability, term lengths, issue ages, coverage amounts, conversion options, underwriting programs, riders, state availability, and financial-strength information. Public underwriting information may be incomplete or outdated, so uncertain claims require qualification rather than invented precision.
Sample premiums are tied to an applicant profile and source. We do not use invented AI price estimates as reliable quotes. No-exam, accelerated underwriting, simplified issue, guaranteed issue, and instant-decision processes require distinct descriptions. For permanent policies, guarantees, current assumptions, non-guaranteed illustrations, policy charges, and loan provisions need appropriate context.
How We Verify Life Insurance Companies, Policies & Underwriting Information →
Add experienced editorial review
Applicable research is reviewed by Chris Huntley, a former licensed life insurance professional, agency owner, publisher, and consumer educator with nearly two decades of prior industry experience. His review may identify inaccurate product descriptions, missing applicant variables, overly broad underwriting claims, unclear terminology, or misleading pricing statements.
Chris is not currently licensed to sell insurance and does not sell policies or provide individualized insurance recommendations through the site. His review can change the explanation or challenge the interpretation of an AI result. It does not retroactively alter raw responses or create additional recommendation votes.
Build study, category, and site-wide views
Each study asks which carriers received the strongest recommendation consensus for its specific situation. Category rankings aggregate recurring performance across related scenarios, using metrics such as study breadth, recommendation rate, total recommendations, average position, and best position. Summary scores should retain the context provided by these underlying measures.
Site-wide analysis may reveal broad visibility and areas of specialization across categories. A carrier can perform strongly in healthy term-life scenarios and less strongly in impaired-risk research. A high overall rank therefore cannot establish suitability for every applicant or policy objective.
Keep dates and historical results meaningful
Research date records when AI responses were collected. Publication date, insurance-information verification date, substantive editorial review date, and last meaningful update describe different actions. Updating a product detail does not imply that the AI study was rerun.
A new research cycle can produce a different result without making the earlier one incorrect. Where practical, we preserve prior responses, counts, and rankings. Corrections address actual errors such as miscounting, duplicate entities, incorrect positions, calculations, or product descriptions. Material methodology changes should be documented and may be versioned when they affect comparability.
Automation, independence, and transparency
Technology may assist research execution, storage, extraction, calculations, article organization, and historical tracking. AI can also help draft or summarize explanations. The structured dataset determines counts, positions, rankings, and scores; generated prose must explain those values rather than invent them.
The publication may earn revenue through affiliates, referrals, advertising, sponsorships, research, and data licensing. Consumer demand and commercial value may influence topic priorities, but commercial relationships should not alter recommendations, qualification, rankings, or historical findings.
Readers should be able to understand the scenario, prompt, platforms, research date, calculations, material verification, review status, and limitations. Life Insurance Consensus Index is owned and operated by 3D Chess Media, LLC, a Nevada limited liability company.
Recognize the limits of the method
AI responses can vary between runs, and platforms may share sources or favor brands with greater online visibility. Absence from a study does not prove that a carrier was evaluated and rejected. Product availability and underwriting can also vary by state and change over time.
This research measures recommendation behavior. It cannot determine an individual's eligibility, underwriting class, premium, exam requirements, or suitability of a policy design. Readers should verify current carrier information and actual policy terms and seek appropriate professional guidance for individual decisions.