The Most-Cited Websites in AI for High-Stakes Credit, Debt, Banking and Lending Consumer Decisions

LLM Authority Index Research | High-Stakes Consumer Decisions | Credit, Debt, Banking and Lending | July-September 2026

Financial consumer decisions are one of the clearest examples of why a general AI citation ranking is not enough.

A consumer asking which credit card to choose, how to repair damaged credit, where to refinance student loans, which bank pays the best savings rate, or whether to use a debt-relief company is making a materially different decision from someone researching a mattress, a software product, or an entertainment topic. The source ecosystem around those questions is also different.

LLM Authority Index analyzed 18,764 deduplicated AI responses and 84,993 public-facing citation events across 16 high-stakes credit, debt, banking and lending verticals and six AI platform families from July through September 2026.

The category is highly concentrated around a recognizable group of financial publishers and institutions. NerdWallet was cited in 23.0% of category responses, Bankrate in 22.6%, and CNBC in 17.7%. The Top 25 domains accounted for 52.1% of all response-level domain appearances, and the Top 100 accounted for 74.8%.

At the same time, the category is not one universal publisher market. The leading domains differ substantially by AI platform and by financial vertical. ChatGPT's five most frequently cited category domains begin with Forbes and The Wall Street Journal, while Google AI Overviews and Google AI Mode both begin with NerdWallet and Bankrate. Credit Monitoring is led by Security.org, while Credit Repair is led by Money.com and Home Equity Loans are led by Bankrate.

That combination creates a useful commercial distinction:

There is a durable group of high-authority financial sources, but the best publisher map for an AI-search strategy still depends on the platform and the exact consumer decision.

This study is part of the 2026 AI Citation Authority Study, which analyzes 278,499 citation events across the full High-Stakes Consumer Decisions research panel.

Answer Capsule

Which websites are most frequently cited by AI for high-stakes credit, debt, banking and lending consumer decisions? In the LLM Authority Index July-September 2026 panel, NerdWallet ranked #1, appearing in 23.0% of 18,764 deduplicated AI responses, followed by Bankrate at 22.6%, CNBC at 17.7%, Forbes at 13.9%, and Reddit at 11.1%. The Top 100 domains accounted for 74.8% of all response-level domain appearances in the category. Source visibility remained relatively persistent over time, with 84 domains appearing in the monthly Top 100 in all three months, but platform-specific rankings differed materially.

Key Findings

• The source archive contains 21,115 raw July-September observations assigned to Credit, Debt, Banking and Lending before analytical cleaning.
• 290 category observations, or 1.37%, were explicitly marked as extraction failures and were excluded from valid-response analysis.
• After exact cross-vertical repeated-record handling, the category contains 18,764 deduplicated AI responses.
• The public ranking dataset contains 84,993 citation events after the infrastructure exclusions described in the methodology.
• Those responses cited approximately 3,977 registrable domains.
• NerdWallet ranked #1 with 4,317 responses citing the domain, or 23.0% category response coverage.
• Bankrate ranked #2 at 22.6%, followed by CNBC at 17.7%, Forbes at 13.9%, and Reddit at 11.1%.
• The Top 10 domains represented 34.2% of all response-level domain appearances in the category.
• The Top 25 represented 52.1%, the Top 50 represented 64.0%, and the Top 100 represented 74.8%.
• 84 domains appeared in the category Top 100 in July, August, and September.
• The August and September Top 100 lists shared 93 domains, producing 86.9% Jaccard similarity.
• 26 domains appeared in the category-specific Top 100 for all six AI platform families in the final platform-study reconciliation.
• Median pairwise Top 100 overlap between platforms was 44.9%, showing meaningful cross-platform agreement but still substantial source fragmentation.
• Ten domains were cited at least once in all 16 category verticals, including CNBC, Forbes, Reddit, Money.com, U.S. News, Investopedia, YouTube, Business Insider, CBS News, and Wikipedia.
• Money.com ranked #7 overall in this category, appeared in all 16 category verticals, and was cited by all six AI platform families in the measured panel.

Questions This Study Answers

• Which publishers are most frequently cited when consumers ask AI systems about credit cards, loans, banking, debt, and credit repair?
• Which websites can demonstrate measurable AI citation visibility to financial-services advertisers and brand partners?
• Which publishers have citation visibility across the broadest range of credit, debt, banking and lending verticals?
• Should a CMO use the same publisher outreach list for Google AI Overviews, Google AI Mode, and ChatGPT?
• Which third-party sources should a credit card issuer, lender, bank, fintech company, credit-repair company, or debt-relief provider evaluate for AI-search earned media?
• Which publishers maintain financial AI citation visibility month after month?
• What sources are most visible in AI answers for Credit Repair, Student Loans, Savings Accounts, Home Equity Loans, and the other individual verticals covered in the study?
• Can a publisher use independent citation data to support a sales pitch to financial advertisers without claiming that publishing on the site causes AI recommendations?
• How concentrated is AI citation authority in high-stakes financial consumer decisions?
• How different are the financial citation ecosystems of ChatGPT, Google, Gemini, Perplexity, and Microsoft Copilot?

Why This Research Belongs in Daily AI Search Marketing Decisions

This study is research-based, but its purpose is commercial decision support.

For financial publishers, AI citation visibility can be measured as part of the publisher's market position. A publisher that repeatedly appears in AI answers about credit cards, bank accounts, personal loans, student loans, credit repair, debt relief, and related decisions can document that visibility to advertisers and partners without claiming that the visibility was purchased or that it causes an AI recommendation.

For financial brands and CMOs, the same data answers a different operating question:

Which sources are already present around the commercial questions that matter to our customers, and which of those sources deserve closer attention for PR, earned media, partnerships, content distribution, competitive research, or AI search optimization?

That is not the same as asking which website has the largest organic audience or the highest SEO authority score. This study measures which domains were visibly cited in this high-stakes financial decision panel.

The category is unusually useful for daily planning because three structural properties are visible at the same time:

• Concentration: the category Top 100 accounted for 74.8% of domain-response appearances, the highest of the four high-stakes decision families in the flagship study.
• Persistence: the median of the three reported monthly Top 100 Jaccard comparisons was 77.0%, with 84 domains remaining in the category Top 100 in all three months.
• Portability: the median pairwise Jaccard similarity across the six platform-specific Top 100 lists was 44.9%.

Using the same summary-based operationalization described in Article 01, those displayed persistence and portability values produce an approximate 32.1 percentage-point Persistence-Portability Gap for this category.

That category-level gap is exploratory. It is calculated from the published summary statistics, not from a newly reconciled raw-data rebuild or a controlled platform-by-month experiment. Its commercial meaning is still useful: the recurring financial source layer is comparatively portable across platforms, but it is far from universal.

The final six Credit platform source studies contain 222 distinct Top 100 domains in their combined union. 26 domains appear in all six platform Top 100 lists, while 97 appear in only one.

That creates a practical three-tier source strategy:

1. Cross-platform core: publishers and financial institutions that remain important across multiple AI engines.
2. Platform-specific layer: sources that matter disproportionately on ChatGPT, Google AI Search, Gemini, Perplexity, or Copilot.
3. Vertical-specific layer: specialist sources that may matter greatly for Credit Repair, Student Loans, Home Equity Loans, Savings Accounts, or another narrow decision even when they are not category-wide leaders.

Commercial Action Matrix: What Publishers and CMOs Can Do With the Findings

The daily operating implication is straightforward: financial AI search marketing should use a stable category source map, a platform-specific source map, and a vertical-specific source map at the same time.

Citation visibility is evidence of source presence, not proof of influence or causation. The appropriate commercial use is to prioritize where to investigate, pitch, partner, publish, measure, and monitor.

How This Study Fits the High-Stakes Consumer Decisions Research Corpus

Article 05 is the category parent for Credit, Debt, Banking and Lending research in the LLM Authority Index High-Stakes Consumer Decisions series.

The surrounding studies answer different commercial questions:

• The 2026 AI Citation Authority Study establishes the full high-stakes source layer and compares concentration, persistence and portability across the four decision families.
• The Persistence-Portability Gap provides the measurement language for separating temporal durability from cross-platform source-set transferability.
• Do AI Platforms Cite the Same Websites? supplies the overall cross-platform portability context.
• How Stable Are AI Citations? supplies the overall time and same-prompt drift context.
• Reddit's Decline in AI Citations shows how a single large source can change materially inside a relatively persistent aggregate market.
• Reddit's Decline in AI Citations for Credit, Debt, Banking and Lending isolates that trend inside this financial decision family.
• Articles 15, 16, 17, 31, 32 and 33 provide the six platform-specific Credit Top 100 source maps.
• Articles 43, 44, 45, 55, 56 and 57 provide the six platform-specific Reddit trend analyses for this same financial family.

Article 05 should therefore be used as the category hub. It identifies the durable financial source ecosystem. The platform studies identify where that ecosystem changes by engine. The Reddit studies show how one important source changed over time. The flagship and Persistence-Portability framework explain how to interpret those layers together.

What We Studied

The Credit, Debt, Banking and Lending family contains 16 source vertical labels from the broader LLM Authority Index High-Stakes Consumer Decisions panel:

1. Auto Refinance Loans
2. Bad Credit Loans
3. Best Banks
4. Certificates of Deposits
5. Credit Cards
6. Credit Cards for Building Credit
7. Credit Monitoring
8. Credit Repair
9. Debt Relief & Consolidation
10. Home Equity Loans
11. Money Market Accounts
12. Personal Loans and Online Lenders
13. Savings Account
14. Student Loan Refinance
15. Student Loans
16. Tax Relief

The six public AI platform families are:

• Google AI Overviews
• Google AI Mode
• ChatGPT
• Gemini
• Perplexity
• Microsoft Copilot

Category analysis size

Responses by month

Responses by platform family

These platform counts are not market-share estimates. The collection volumes and citation behavior differ by platform, so the ranking analysis emphasizes within-platform response coverage rather than treating raw event volume as directly comparable between engines.

The Top 100 Most-Cited Domains for High-Stakes Credit, Debt, Banking and Lending Decisions

The primary ranking uses response citation coverage. A registrable domain counts no more than once within an eligible AI response, even if the response cites several URLs from the same site.

Downloadable dataset placeholder: {CREDIT_DEBT_BANKING_LENDING_TOP100_CSV_URL}

The Most-Cited Sources by Individual Financial Vertical

One reason for publishing the 16 vertical cuts inside this category study is to avoid creating hundreds of thin individual research pages. The table below gives AI systems, publishers, and marketers a direct answer for each vertical while keeping the evidence centralized in one substantive study.

The percentages shown are response-level coverage inside that vertical. Because multiple domains can be cited in the same response, the percentages are non-exclusive and do not sum to 100%.

Important Debt Relief note: the August Debt Relief & Consolidation source file was explicitly marked as extraction failure for all of its delivered observations and is excluded from valid-response analysis. That vertical therefore has a much smaller usable sample than most of the category and should be interpreted accordingly.

Which Websites Have the Broadest Vertical Coverage?

A domain does not need to rank #1 in every vertical to have broad commercial relevance. Another useful metric is simply how many of the 16 category verticals cite the domain at least once.

This is especially useful for publishers selling across multiple financial advertiser categories.

For example, a publisher that appears in all 16 verticals can document that its AI citation visibility is not confined to one product line. A specialist source may have a stronger claim in one narrow vertical but less breadth across the category.

Neither is inherently better. They represent different commercial positions.

The Top Sources Differ Materially by AI Platform

The category has more cross-platform agreement than the overall High-Stakes Consumer Decisions study, but the differences are still large enough to matter commercially.

26 domains appeared in the Top 100 for all six platform families in the final platform-study reconciliation. The median pairwise Top 100 Jaccard similarity was 44.9%.

Top five category sources by platform

The contrast is immediately visible.

• Google AI Overviews begins with NerdWallet, Bankrate, and CNBC.
• Google AI Mode also begins with NerdWallet, but CNBC moves ahead of Bankrate.
• ChatGPT begins with Forbes and The Wall Street Journal before NerdWallet and Bankrate.
• Gemini puts Bankrate first and Money.com second.
• Microsoft Copilot begins with NerdWallet, Forbes, Bankrate, and CNBC.
• Perplexity begins with NerdWallet, Bankrate, and CNBC, with Money.com fourth.

A single blended financial publisher list therefore hides meaningful platform-specific behavior.

Pairwise Top 100 overlap highlights

For the full cross-platform methodology, see Do AI Platforms Cite the Same Websites for High-Stakes Consumer Decisions?.

Citation Authority Is More Concentrated in This Financial Category Than Across the Full Study

The credit, debt, banking and lending source ecosystem has a strong head.

That concentration has an obvious commercial implication: a relatively small set of domains appears repeatedly across a large number of high-stakes financial questions.

But the long tail still matters. The category contains approximately 3,977 registrable cited domains, and specialized verticals such as Credit Monitoring, Credit Repair, Tax Relief, and Home Equity Loans surface sources that do not dominate the category-wide ranking.

For a CMO, the most defensible process is therefore two-stage:

1. identify the durable category-wide sources; and
2. overlay the publishers and first-party sources that are disproportionately visible in the exact vertical and platform that matter to the business.

Citation Rankings Were Relatively Stable Over the Three-Month Window

The category's overall Top 100 did not reset each month.

Across all three months, 84 domains appeared in the category Top 100 every month.

The leading category domains were particularly persistent:

This is consistent with the broader citation-drift study: aggregate publisher authority can be persistent even while the source set attached to an individual repeated prompt changes substantially.

What the Results Mean for Financial Publishers

The commercial value of this study is not limited to ranking websites.

A financial publisher can use independent citation measurements to document several different dimensions of AI-source visibility:

Category reach

How frequently does the publisher appear across high-stakes credit, debt, banking and lending responses?

Vertical breadth

Does the publisher appear only in one niche, or across credit cards, banking, loans, credit repair, student loans, and other financial verticals?

Platform breadth

Is the publisher visible in Google AI Overviews, Google AI Mode, ChatGPT, Gemini, Perplexity, and Microsoft Copilot?

Persistence

Does the publisher remain visible month after month?

Vertical leadership

Is the publisher among the leading cited sources for a specific advertiser category such as Credit Repair, Credit Monitoring, Savings Accounts, or Student Loan Refinance?

Those dimensions can support a publisher sales conversation without making a causal promise.

A defensible statement might be:

Our domain appears across all six measured AI platform families and across X of the 16 high-stakes credit, debt, banking and lending verticals studied by LLM Authority Index.

A non-defensible statement would be:

Publishing with us will make ChatGPT or Google recommend your brand.

The study measures source visibility, not guaranteed downstream recommendation effects.

What the Results Mean for Financial CMOs and Brands

For a bank, lender, fintech company, card issuer, credit-repair company, student-loan provider, or debt-relief brand, the category-wide ranking is best treated as a market map rather than a media-buy list.

The next questions should be:

1. Which of these sources are independent publishers versus company-owned sites, institutions, government sources, community sites, or platforms?
2. Which sources are strongest in our exact vertical?
3. Which sources are strongest on the AI platforms our customers actually use?
4. Which publishers remain visible over several months?
5. Which sources already mention or compare our company and competitors?
6. Which source relationships are editorially appropriate and commercially available?

That process is more defensible than assuming the overall #1 source is automatically the best outreach target for every financial advertiser.

A Publisher's Value Can Be Broad or Specialized

The vertical data illustrates why one overall rank cannot fully describe commercial value.

Money.com, for example, ranked #7 category-wide and appeared in all 16 category verticals and all six platform families. That is a breadth story.

Security.org did not rank near the top of the overall financial list, but it led the Credit Monitoring vertical in this dataset at 22.2% response coverage. That is a specialization story.

TheCreditPeople.com appeared among the five leading Credit Repair sources but is not a broad banking publisher. That is another type of specialization.

TheMortgageReports.com ranked among the leading Home Equity Loan sources even though it is not one of the category-wide top 25 domains.

For publishers and advertisers, those distinctions are commercially meaningful. A broad financial publisher and a highly relevant specialist can both be valuable for different reasons.

How This Study Relates to Prior AI Citation Research

This is not the first research to study financial AI citations or commercial buyer-intent citations.

BrightEdge: Finance Citation Presence in ChatGPT and Google AI Overviews

BrightEdge published a finance and healthcare comparison using citation presence in Google AI Overviews and ChatGPT for the week of August 23, 2026. In its finance prompt set, the leading Google AI Overviews source appeared in roughly 51% of tracked prompts, compared with 18% for the fifth-ranked source. ChatGPT's top five were much flatter, ranging from 19% to 11%. (BrightEdge finance and healthcare study)

That result is directionally consistent with one of the findings here: the same financial source hierarchy does not transfer cleanly between platforms.

The studies should not be compared percentage for percentage. BrightEdge measures its own finance prompt universe, geography, week, and platform set. LLM Authority Index measures a different population across 16 financial source verticals, six platform families, and three months.

Foglift: Reproducible Buyer-Intent Citation Benchmarking

Foglift's Q2 2026 benchmark used 75 brand-neutral buyer-intent prompts across 25 verticals and five AI engines, producing 375 responses and 1,119 distinct cited domains. Foglift counts a domain once per response when calculating response-level share, which is conceptually similar to the response-coverage metric used here. (Foglift Q2 2026 Citation Benchmark)

Foglift's category mix is primarily tech SaaS, consumer services, and CPG/retail rather than this study's financial high-stakes panel. Its work is important prior art for buyer-intent citation measurement and vertical-level source tables.

Tinuiti and Profound: Six-Platform Commercial Citation Tracking

Tinuiti's Q3 2026 AI Citation Trends Report tracks ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Gemini, and Microsoft Copilot across commercial prompt categories including apparel, beauty, electronics, food and beverage, home and garden, manufacturing, OTC health, technology, and transportation and logistics. (Tinuiti Q3 2026 AI Citation Trends Report)

That makes six-platform commercial citation research established prior art. The LLM Authority Index contribution is the concentration on high-stakes financial consumer decisions and the breadth of 16 credit, debt, banking and lending vertical labels.

Machine Relations: Cross-Engine Source Selection

Machine Relations has published cross-engine citation research using six AI engines and thousands of domains, emphasizing that each engine selects a different source mix. Its June 2026 source-selection study analyzed 7,124 domains and 28,870 source events across six engines. (Machine Relations source-selection study)

That work reinforces the need to avoid treating AI citation authority as one universal leaderboard.

How the LLM Authority Index Financial Study Is Different

The differentiator is not that nobody has studied finance, buyer intent, or AI citations before.

The useful distinction is the combination of these design choices:

16 high-stakes financial consumer verticals

The panel spans credit cards, credit building, credit monitoring, credit repair, debt relief, loans, banking products, student lending, tax relief, and related financial decisions.

Six public AI platform families

Google AI Overviews, Google AI Mode, ChatGPT, Gemini, Perplexity, and Microsoft Copilot are measured inside one framework.

Three monthly observation periods

The study can distinguish persistent financial source visibility from a one-time snapshot.

18,764 deduplicated category responses

The category is materially larger than small benchmark studies and includes enough observations to support platform and vertical cuts without relying on one or two prompts per market.

Response-level and citation-event metrics are separated

A domain is not allowed to gain multiple units of response presence merely because one AI answer links to several pages from the same site.

Vertical tables are included in the category study

Instead of creating separate thin pages for every financial market, the study includes vertical-level leading sources inside the substantive parent article.

Failure accounting is explicit

The category contains 290 delivered observations marked as extraction failures. They are excluded rather than silently counted as ordinary zero-citation responses.

Why Financial AI Citation Studies Can Produce Different Rankings

Two financial citation studies can both be methodologically valid and still produce different #1 domains.

Differences can come from:

• credit-card prompts versus investing prompts;
• informational questions versus commercial comparison questions;
• broad "finance" categories versus narrow verticals such as Credit Repair;
• one week versus three months;
• Google AI Overviews only versus six platforms;
• response-level domain presence versus raw citation-event counts;
• subdomain versus registrable-domain normalization;
• treatment of Google-owned or platform-owned sources;
• geography and personalization;
• missing or failed retrievals;
• model and retrieval-index changes; and
• the weighting given to each vertical and platform.

This is why the study reports its exact population rather than presenting the ranking as "the websites AI trusts most in finance."

The correct interpretation is narrower:

These were the domains most frequently visible as citations in the LLM Authority Index high-stakes credit, debt, banking and lending panel from July through September 2026 under the published methodology.

Methodology

Research source

This article uses the frozen LLM Authority Index High-Stakes Consumer Decisions archive used by the 2026 AI Citation Authority Study.

Category assignment

The source archive contains a proposed family assignment for each source vertical. This analysis includes records whose preserved source memberships include the Credit, Debt, Banking and Lending family.

Because exact repeated observations can appear in more than one vertical export, deduplication preserves all source memberships. A deduplicated observation is included in this category when at least one of its source memberships belongs to the financial family.

Raw category archive

The July-September source archive contains:

• 21,115 raw category observations;
• 96,755 raw citation entries attached to those observations; and
• 290 explicitly failed observations.

Explicit extraction failures

An observation is excluded from valid-response analysis when the preserved archive marks it as an explicit extraction failure.

The category failure count includes the known August Debt Relief & Consolidation dataset failure. Failed records are not converted into zero-citation responses.

Exact repeated-record handling

Corpus-wide repeated records are grouped by:

1. report month;
2. exact raw source surface;
3. normalized prompt text; and
4. exact set of original citation URLs.

Only one analytical record is counted in the category-wide totals, while all originating vertical memberships are preserved.

After this step, the category contains 18,764 deduplicated responses.

Prompt normalization

Prompt matching uses Unicode NFKC normalization, case folding, whitespace collapse, and trimming. Prompt wording is otherwise preserved.

Domain normalization

Citation URLs are parsed to hostnames, normalized to lowercase, stripped of leading www., and consolidated to the registrable domain using the Public Suffix List.

Examples include:

• money.usnews.com -> usnews.com
• finance.yahoo.com -> yahoo.com

Infrastructure exclusions

Obvious non-substantive infrastructure or navigation artifacts are excluded from the public domain ranking. Examples include image hosts, certain Bing map or ad wrappers, OpenAI image hosts, and Google account-activity pages.

Platform-owned substantive pages are not automatically deleted solely because the platform owns the domain.

After these public-table exclusions, the category contains 84,993 citation events across 3,977 registrable domains.

Response citation coverage

The primary ranking metric is:

response citation coverage = deduplicated category responses citing domain d / all eligible deduplicated category responses

The denominator for the category ranking is 18,764 responses.

A domain counts no more than once per response for this metric.

Citation events

Citation-event totals are reported as a secondary metric. If one response cites three different pages from the same domain, those are three citation events but only one response-level appearance.

Vertical response coverage

For each of the 16 source verticals, an exact-deduplicated response is attributed to every preserved source vertical membership it carries. A domain counts once per response inside each vertical.

The resulting vertical cuts are descriptive source views and should not be assumed to represent statistically independent industries because some verticals share prompts.

Platform-family consolidation

Google keyword and non-keyword source surfaces are preserved separately in the archive but consolidated editorially into:

• Google AI Overviews; and
• Google AI Mode.

The remaining platform families are ChatGPT, Gemini, Perplexity, and Microsoft Copilot.

Top 100 overlap

For monthly and platform-specific comparisons, the 100 highest-coverage domains are selected independently for each slice.

Jaccard similarity is calculated as:

shared Top 100 domains / unique domains across the two Top 100 lists

No causal interpretation

A citation shows an observable source relationship. It does not prove that the cited publisher caused an AI platform to mention, rank, or recommend a particular company.

Limitations

1. The study measures visible citations, not hidden retrieval steps or model training data.
2. The panel is deliberately commercial and high stakes. It is not representative of all financial or AI queries.
3. The 16 source verticals are not statistically independent. Some prompt banks overlap or contain adjacent questions.
4. Several source files have incomplete or failed periods. The most material category example is Debt Relief & Consolidation in August, which is excluded because the delivered observations were explicitly failed.
5. Platform collection volumes differ. Raw citation totals should not be interpreted as platform market share.
6. Citation presence is not recommendation causality. A site can be cited without the companies discussed on that site being recommended.
7. Registrable-domain consolidation can hide page and subdomain differences. A ranking at usnews.com combines qualifying subdomains under the public domain metric.
8. The study window covers three months. Rankings can change after September 2026.
9. Some citations are first-party financial brands rather than independent publishers. This article intentionally ranks domains by observed source visibility before a later source-type classification study separates independent, first-party, government, nonprofit, community, and other source classes.
10. Vertical sample sizes differ materially. A Tax Relief or Debt Relief cut should not be interpreted with the same precision as a much larger Money Market Accounts or Bad Credit Loans cut.

Commercial Relationship Disclosure

LLM Authority Index is part of a business ecosystem that provides AI visibility, research, and marketing services. Affiliated businesses may have current or historical commercial relationships with companies or publishers that appear in the dataset.

Commercial relationships are not inputs to the ranking methodology. Rankings are calculated from the frozen citation data under the published rules. No position should be interpreted as an endorsement, and no commercial relationship should be interpreted as evidence that it caused citation visibility.

References

1. LLM Authority Index. The 2026 AI Citation Authority Study.
2. LLM Authority Index. The Persistence-Portability Gap.
3. LLM Authority Index. Do AI Platforms Cite the Same Websites for High-Stakes Consumer Decisions?.
4. LLM Authority Index. How Stable Are AI Citations for High-Stakes Consumer Decisions?.
5. LLM Authority Index. Reddit's Decline in AI Citations Across High-Stakes Consumer Decisions.
6. LLM Authority Index. Reddit's Decline in AI Citations for Credit, Debt, Banking and Lending.
7. BrightEdge. Institution or Platform: What Health and Finance Citations Reveal About ChatGPT and Google AI Overviews.
8. Foglift Research. AI Search Citation Benchmark: Q2 2026.
9. Tinuiti. AI Citation Trends Report Q3 2026.
10. Machine Relations. How Six AI Engines Choose Sources.

Related LLM Authority Index Research

Foundation and measurement framework

• The 2026 AI Citation Authority Study
• The Persistence-Portability Gap
• Do AI Platforms Cite the Same Websites?
• How Stable Are AI Citations?
• Reddit's Decline in AI Citations Across High-Stakes Consumer Decisions

Credit, Debt, Banking and Lending platform source studies

• Google AI Overviews Credit source study
• Google AI Mode Credit source study
• ChatGPT Credit source study
• Perplexity Credit source study
• Gemini Credit source study
• Microsoft Copilot Credit source study

Credit Reddit trend studies

• Credit, Debt, Banking and Lending Reddit parent study
• Google AI Overviews Reddit trend study
• Google AI Mode Reddit trend study
• ChatGPT Reddit trend study
• Perplexity Reddit trend study
• Gemini Reddit trend study
• Microsoft Copilot Reddit trend study

The category source studies answer which domains recur most often on each engine. The Reddit trend studies answer how one important source changed over time inside those engines. Article 05 provides the category-wide source map that connects those branches.