The Life Insurance Consensus Index Research Team designs, collects, structures, verifies, and publishes the research behind this site. Our goal is to measure how leading AI platforms recommend life insurance carriers for specific consumer situations and explain the findings with appropriate insurance context.
Who the Research Team represents
The Research Team byline reflects organizational authorship. Different parts of a study may involve different people, software systems, and automated tools. The attribution represents that combined research and editorial process; it does not imply that one unnamed expert personally sold, underwrote, or tested every product discussed.
Life Insurance Consensus Index is owned and operated by 3D Chess Media, LLC, a Nevada limited liability company. The publication provides research and education. The Research Team does not act as an individual reader's insurance producer, broker, financial adviser, legal adviser, or tax adviser.
Designing useful comparisons
Our work starts with a defined applicant and coverage need. A healthy nonsmoking 40-year-old seeking $1 million of 20-year term coverage without a traditional medical exam presents a more useful research question than a generic request for the best insurer.
We organize research across term life, whole life, universal and indexed universal life, final expense and burial insurance, and health conditions and high-risk applicants. Studies vary characteristics that could plausibly change recommendations, such as age, coverage amount, policy type, term length, health, tobacco use, occupation, or underwriting method.
Once the scenario is defined, we create a standardized prompt that preserves the same applicant profile, policy objective, and constraints across platforms. Open recommendation studies generally avoid supplying preferred carriers so each platform can generate its own recommendation set.
Collecting and interpreting recommendations
Our standard research framework is designed around ChatGPT, Gemini, Claude, Perplexity, Grok, DeepSeek, and Kimi. Each included platform contributes equally to basic recommendation coverage. We preserve the underlying record, including carrier and product names, order, reasons, qualifications, and citations where available.
Extraction requires care. A positive or conditional recommendation differs from an incidental mention, comparison reference, or criticism. We normalize equivalent carrier names to prevent duplicate counting while preserving meaningful distinctions among brands, issuing companies, and products. A recommendation for one policy type does not become an endorsement of every product the insurer sells.
Recommendation coverage measures how many usable platform responses recommended a carrier. Where ordered recommendations are available, position provides a secondary signal. Ties, fragmented results, and disagreement remain part of the findings rather than being forced into a stronger consensus than the data supports.
Verifying material insurance facts
AI systems can give outdated or oversimplified explanations. The Research Team may verify material details such as policy types, term lengths, issue ages, coverage limits, conversion provisions, riders, no-exam programs, state availability, and financial-strength information.
Underwriting requires particular care because public information cannot capture every internal rule or applicant detail. We avoid turning general observations into approval guarantees. Pricing examples should be tied to a defined hypothetical profile or quote source rather than presented as a rate available to everyone.
How We Verify Life Insurance Companies, Policies & Underwriting Information →
Working with our editorial reviewer
Chris Huntley provides experienced review of applicable content after the research and verification layers are prepared. His prior career included selling coverage, operating insurance businesses, handling underwriting challenges, and educating consumers. He may identify inaccurate product descriptions, missing applicant variables, unclear terminology, or statements about pricing and eligibility that need qualification.
Chris is a former licensed life insurance professional and is not currently licensed to sell insurance. He does not sell policies or provide individualized insurance recommendations through this site. His editorial judgment helps explain the findings without changing the underlying AI vote count or selecting a preferred consensus winner.
Automation and quality control
Technology may support prompt execution, response storage, extraction, normalization, calculations, table generation, article organization, and historical tracking. AI may also assist drafting and summarization. Generated prose is not evidence: structured research records determine recommendation counts, coverage, positions, rankings, and Consensus Scores.
Before publication, the workflow should check scenario consistency, platform inclusion, recommendation classification, carrier identity, calculations, material facts, underwriting qualifications, and any required editorial review. Category rankings should follow the underlying studies, with important limitations and commercial disclosures made clear.
Commercial independence and accountability
The site may earn revenue through affiliate or lead-generation relationships, advertising, sponsorships, research, and data licensing. Consumer demand and commercial value may influence which topics receive attention. They should not change the recommendation data, qualification criteria, ranking calculations, or historical results.
Carriers can request evidence-based corrections without a commercial relationship. Payment cannot create AI recommendations or guarantee favorable treatment, and accurate unfavorable research should not disappear because a carrier objects.
Corrections, updates, and research history
The team is responsible for correcting material errors in counts, normalization, ranking calculations, source interpretation, and insurance information. A later change to a product or a new research run is a different event from correcting an error in the original publication.
Where practical, we preserve earlier research cycles and distinguish research, publication, verification, review, and modification dates. This helps readers see both what was reported at the time and what has changed. Questions about methodology, carrier data, historical results, corrections, licensing, or research partnerships can be submitted through our contact page.