Certificate of Deposits: 2026 AI Market Discovery Index

Answer Capsule

In the Certificate of Deposits category for May 2026, AI systems are concentrating buyer attention on a small set of recommended providers rather than distributing visibility evenly. Ally Bank leads with 23.1% valid recommendation coverage and a modeled monthly captured recommendation value of $266,487. Marcus by Goldman Sachs holds a strong second position. Barclays, despite being a recognized global institution, appears in only 3.1% of observations and earns virtually no recommendation credit, exposing a significant gap between brand awareness and AI shortlist eligibility.

Executive Summary

AI platforms are becoming the primary shortlist builders for CD shoppers, and the data shows a clear concentration of recommendation power. Ally Bank captures 43.9% of the total modeled monthly recommendation value across the category, more than the next two competitors combined. Marcus by Goldman Sachs follows at $166,866, and Capital One holds third at $95,332. Together, these three institutions account for 87% of all captured recommendation value.

The gap between visibility and recommendation is stark. Several banks appear in AI responses at moderate rates but fail to convert those appearances into ranked recommendations. Bread Savings appears in 21% of observations but earns only a 2.7% Top 3 recommendation rate. Synchrony Bank appears in 17.6% of observations but achieves a 3.5% Top 3 rate. These brands are being mentioned, not advanced.

Barclays represents the most extreme case. Despite being a global banking name, it appears in only 3.1% of observations and earns a single Top 3 recommendation across all 1,013 prompts analyzed. Its modeled monthly captured recommendation value is $11.45, compared to Ally Bank's $266,487. This is not a visibility problem. It is a recommendation architecture problem.

The commercial implication is direct. AI systems are not distributing opportunity evenly. They are concentrating buyer attention on a small number of providers that have the right combination of public source visibility, positive sentiment, and structured comparison content. Banks that are merely present in AI responses without being recommended are losing the shortlist battle where it counts most.

The AI Discovery Shift in Certificate of Deposits

CD shoppers historically relied on bank websites, rate comparison tables, and financial publications to narrow their options. AI platforms are changing this by acting as discovery engines that produce ranked shortlists before the buyer ever visits a bank's site.

When a user asks an AI system for the best CD rates or top CD providers, the response typically names three to five institutions with rate details and supporting reasoning. This response functions as a pre-qualified shortlist. Banks that appear in these shortlists receive meaningful buyer consideration. Banks that are merely mentioned in passing, or listed without endorsement, do not.

The critical distinction is between being mentioned and being recommended. A mention means the AI system retrieved the brand name. A valid recommendation means the AI system placed the brand in a positive, ranked position suitable for buyer consideration. The data shows that many CD providers achieve the first but fail at the second.

AI platforms draw from public sources including bank websites, rate aggregators, financial news, comparison articles, and community discussions. The quality, structure, and consistency of this public evidence determines whether a bank is simply retrieved or actively recommended. This is not about traditional search engine optimization. It is about whether the public evidence layer supports AI systems in trusting, comparing, and advancing a brand at the moment a shortlist is formed.

Directional Category Leaders

1. Ally Bank

Ally Bank leads the category with a 41.3% raw mention presence rate and 23.1% valid recommendation coverage. It earns 162 Top 3 recommendations out of 1,013 observations, a 16% Top 3 rate, and 92 Rank 1 placements. Its average recommended rank is 1.62, meaning when Ally is recommended, it typically appears first or second.

Ally's modeled monthly captured recommendation value of $266,487 is more than 1.5 times the next closest competitor. The bank performs strongly across all three public clusters, with particular dominance in the Discovery and Ranking cluster where it captures $248,671 in modeled value alone.

The public interpretation: Ally Bank has built the strongest AI recommendation architecture in the CD category, earning top placement across multiple platforms and buyer stages.

2. Marcus by Goldman Sachs

Marcus by Goldman Sachs holds a clear second position with a 37% raw mention presence rate and 17.5% valid recommendation coverage. It earns 81 Top 3 recommendations and 42 Rank 1 placements, with an average recommended rank of 1.73.

Marcus captures $166,866 in modeled monthly recommendation value. It performs particularly well on ChatGPT, where it achieves 33.3% valid recommendation coverage and a 9.9% Rank 1 rate. This platform concentration suggests Marcus has strong public evidence alignment with ChatGPT's retrieval and ranking systems, a meaningful advantage as ChatGPT remains one of the most widely used AI research tools among retail financial decision-makers.

The public interpretation: Marcus by Goldman Sachs is the strongest challenger to Ally, with particular platform depth on ChatGPT that positions it to narrow the gap.

3. Capital One

Capital One holds third position with a 19.4% raw mention presence rate and 8.7% valid recommendation coverage. It earns 60 Top 3 recommendations and 30 Rank 1 placements, with an average recommended rank of 1.82.

Capital One's modeled monthly captured recommendation value is $95,332. The bank shows balanced performance across platforms but does not dominate any single platform the way Ally or Marcus do. Its net sentiment score of 0.57 is solid but below the category leaders.

The public interpretation: Capital One is a consistent third-place performer but lacks the recommendation density to challenge the top two without a meaningful shift in its public evidence architecture.

4. Synchrony Bank

Synchrony Bank achieves a 17.6% raw mention presence rate but only 6% valid recommendation coverage. It earns 35 Top 3 recommendations and 17 Rank 1 placements, with an average recommended rank of 1.69.

Synchrony's modeled monthly captured recommendation value is $30,230. Its strongest platform is ChatGPT, where it achieves 13.5% valid recommendation coverage. However, its net sentiment score of 0.48 is the lowest among the top five competitors, suggesting inconsistent or mixed framing in AI responses.

The public interpretation: Synchrony Bank has reasonable visibility but low recommendation conversion, pointing to a gap between being retrieved and being endorsed.

5. Bread Savings

Bread Savings appears in 21% of observations but achieves only 7.8% valid recommendation coverage. It earns 27 Top 3 recommendations and 14 Rank 1 placements, with an average recommended rank of 1.70.

Bread Savings captures $21,810 in modeled monthly recommendation value. The gap between its 21% presence rate and 7.8% recommendation rate is one of the more pronounced mismatches in the dataset, suggesting that AI systems frequently retrieve Bread Savings content but do not consistently treat it as a shortlist-quality recommendation.

The public interpretation: Bread Savings has above-average visibility but weak recommendation conversion, leaving it exposed to competitors that turn presence into shortlist placement.

The Buying Moments That Now Decide the Category

Discovery and Ranking

This cluster captures buyers searching for the best CD providers or top CD rates. At 494 observations, it is the largest public cluster and carries a modeled opportunity value of $497,190, making it the dominant commercial moment in the category.

Ally Bank leads with a 22.1% Top 3 recommendation rate and a 14.2% Rank 1 rate. Marcus by Goldman Sachs follows at a 13% Top 3 rate, and Capital One holds third at 8.1%. Brands that do not earn consistent placement here are effectively absent from the buyer's earliest and most consequential consideration stage.

Head-to-Head Evaluation

This cluster captures buyers directly comparing specific CD providers against each other. It accounts for 136 observations with a modeled opportunity value of $960.

Overall recommendation density in this cluster is low. Most competitors earn zero Top 3 recommendations. Both Marcus and Capital One achieve 2.2% Top 3 rates. The low density likely reflects weaker public comparison content specifically structured for CD provider head-to-head analysis, creating an addressable gap for any bank willing to build that content layer.

Pricing and Plan Evaluation

This cluster captures buyers evaluating specific CD rates, term lengths, and product details. It accounts for 383 observations and carries a modeled opportunity value of $109,447.

Marcus by Goldman Sachs leads this cluster with a modeled captured value of $37,977, followed by Quontic Bank at $20,593 and Bread Savings at $20,575. Ally Bank captures $17,297. This cluster shows more competitive distribution than the Discovery cluster, giving mid-tier banks a meaningful opportunity to capture value when buyers are evaluating specific product terms rather than choosing a shortlist from scratch.

Why Recommendation Power Is Concentrating

Recommendation power in the CD category is not random. It is driven by the public evidence layer that AI systems use to retrieve, compare, and rank providers. Ally Bank and Marcus by Goldman Sachs benefit from dense citation architecture, appearing consistently across rate comparison sites, financial publications, bank review platforms, and consumer finance community discussions.

The citation sources that carry the most weight in this category include official bank rate pages with structured product information, financial news articles that directly compare CD rates, bank review aggregators with scoring frameworks, and community discussions where savers share rate experiences. Banks with consistent, positive, comparison-ready coverage across these source types are more likely to be retrieved and advanced as recommendations.

Banks with thinner public evidence layers face a structural disadvantage that compounds over time. Barclays, for example, has limited CD-specific public coverage. When it does appear in AI responses, the sentiment is not negative, but the volume is too low for AI systems to treat it as a reliable, comparison-ready recommendation candidate.

The concentration effect is self-reinforcing. Banks that are recommended frequently accumulate more public references, which makes them more likely to be recommended again. Banks that are absent from shortlists remain structurally invisible to the AI discovery layer, regardless of their actual product quality or rate competitiveness.

The Category's Most Visible Warning Sign

Barclays is the category's most striking warning sign. A global financial institution with significant brand recognition, it appears in only 3.1% of all observations and earns exactly one Top 3 recommendation across 1,013 prompts analyzed. Its modeled monthly captured recommendation value is $11.45.

This is not a brand awareness problem. Barclays is a globally known name. It is a recommendation architecture problem. The public evidence layer for Barclays in the CD context is thin, fragmented, and not structured for AI comparison retrieval. AI systems do not have enough consistent, positive, comparison-ready content about Barclays CDs to justify recommending them.

The commercial cost is measurable. Barclays loses an estimated $607,596 in modeled monthly recommendation value to competitors. Every time an AI system produces a CD shortlist, Barclays is almost certainly absent from it. For a bank of its scale, this represents a significant and likely growing missed opportunity in a channel that is becoming a primary discovery mechanism for retail financial products.

What This Means for the Category

Shortlist compression is the dominant structural dynamic. Three banks control 87% of the modeled recommendation value. The remaining six compete for 13%. This concentration will intensify as AI systems continue to favor providers with the densest and most consistently positive public evidence layers.

Competitor displacement is already visible in the data. Banks that achieve moderate visibility but low recommendation conversion are being systematically passed over. Bread Savings, Synchrony Bank, and CIT Bank appear in AI responses but are not consistently advanced. Their presence creates the appearance of market participation without the commercial benefit of shortlist inclusion.

Trust-source dependency is becoming a structural barrier to entry. Banks that lack strong, structured coverage across rate comparison sites, financial publications, and review platforms will find it increasingly difficult to enter AI shortlists regardless of how competitive their actual rates are. The public evidence layer is not a marketing advantage. It is a prerequisite for recommendation eligibility.

AI discovery is now part of buyer choice in this category. CD shoppers who use AI platforms to research options receive pre-filtered shortlists before they visit a single bank's rate page. Banks that are not on those shortlists are excluded from consideration before the buyer journey formally begins. Closing that gap requires stronger entity, content, source, and citation architecture, not just better rates or broader advertising.

What This Public Benchmark Does Not Include

- Full cluster dataset covering all 10 buyer intent clusters

- Prompt-level response tables showing exact AI outputs per platform

- Citation-source failure maps identifying which sources are missing or underperforming

- Platform-by-platform recovery priorities for each brand

- Entity and schema diagnostics for structured data readiness

- Source-layer gap analysis for rate comparison and review coverage

- Company-specific content recommendations for improving AI shortlist eligibility

- Exact competitor threat profiles by platform and cluster

- Full paid opportunity model with investment scenarios

This page shows the market shape. The paid report shows the repair map.

Methodology and Disclaimers

1. Market studied: Certificate of Deposits (CDs), including online banks and traditional financial institutions offering CD products.

2. Brands and entities included: Ally Bank, Marcus by Goldman Sachs, Capital One, Synchrony Bank, Bread Savings, CIT Bank, Quontic Bank, Popular Direct, and Barclays. This is not a full market census.

3. Data collection window: May 2026.

4. AI platforms tested: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity.

5. Observations analyzed: A total of 1,013 observations were analyzed across all platforms and clusters. Prompt count was not separately disclosed in the public dataset.

6. Prompt categories: Discovery and Ranking (consideration stage), Head-to-Head Evaluation (comparison stage), and Pricing and Plan Evaluation (decision stage).

7. Definition of a mention: A mention means the company appeared in an AI-generated response, regardless of sentiment or position.

8. Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality placement that earns recommendation credit. Visibility is not the same as recommendation credit. This is the core CiteWorks distinction applied throughout the analysis.

9. Ranking and scoring metrics used: Valid recommendation coverage, Top 3 recommendation rate, Rank 1 rate, average recommended rank, net sentiment score, positive visibility rate, and modeled monthly captured recommendation value.

10. Limitations: This is a point-in-time benchmark. AI outputs can change with model updates, source changes, and query variations. Modeled values are estimates based on observed recommendation patterns and are not actual revenue figures. This report is not a full audit or a complete market census.

For a company-specific Authority Index report, the deeper analysis would show which prompts each company wins or loses, which AI platforms are under-recognizing the brand, which source layers are shaping recommendations, and what changes may improve AI shortlist eligibility.