Chase AI Market Strategy Report - Best Banks

Answer Capsule

Chase appears in 45% of all AI observations in the Best Banks category but converts only 8.7% of those appearances into valid recommendations, exposing a severe gap between brand awareness and AI shortlist eligibility. The bank holds a monthly AI Authority Value of $473,730, ranking fifth among ten measured competitors, while Capital One and Ally Bank together capture a disproportionate share of the total $29.1 million monthly opportunity. Chase's net sentiment score of 0.2721 is the weakest in the category, and its 4% negative visibility rate signals framing problems that reduce recommendation power. The clearest opportunity lies in converting Chase's high mention presence into recommendation-stage visibility by strengthening the citation architecture and improving the quality of public evidence that AI systems use to generate shortlists.

Who This Report Is For

This report is for Chase marketing, brand strategy, digital experience, and executive teams responsible for AI discovery positioning, competitive shortlist eligibility, and consumer banking acquisition strategy.

Report Card

• Report type: AI Company Market Strategy Report
• Target company: Chase
• Category / market studied: Best Banks
• Reporting month: June 2026
• AI platforms tracked: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity
• Public high-intent clusters: 3 (Best Bank & Account Discovery, Bank Comparison & Alternatives, Bank Pricing, Fees & Rates Research)
• AI observations analyzed: 1,536
• Competitors tracked: Ally Bank, Bank of America, Capital One, Citibank, Discover Bank, Marcus by Goldman Sachs, PNC Bank, U.S. Bank, Wells Fargo

Executive Summary

The June 2026 LLM Authority Index benchmark for Best Banks reveals a market where recommendation power is concentrating around Capital One and Ally Bank, while traditional branch-heavy banks like Chase struggle to convert massive brand presence into shortlist eligibility. Chase appears in 691 of 1,536 total observations, a 45% raw mention presence rate that places it among the most visible brands in the category. Yet only 133 of those appearances result in valid recommendations, yielding an 8.7% valid recommendation coverage rate that is the lowest among the top five banks by presence.

Chase's monthly AI Authority Value of $473,730 ranks fifth overall, behind Capital One ($1.45M), Ally Bank ($1.37M), Discover Bank ($774,298), and Bank of America ($656,958). The bank's net sentiment score of 0.2721 is the weakest in the category, reflecting a combination of 249 positive mentions, 381 neutral mentions, and 61 negative mentions across all observations. Chase's negative visibility rate of 3.97% is the third highest in the category, trailing only Wells Fargo (5.21%) and Bank of America (5.47%).

The strongest cluster for Chase is Best Bank & Account Discovery, where it captures $218,087 in monthly AI Authority Value, though this represents only 2.2% of the cluster's total $9.95 million opportunity. The weakest cluster is Bank Pricing, Fees & Rates Research, where Chase captures $148,369 against a $10.41 million total opportunity, achieving just a 4.1% top-three recommendation rate. Chase's strongest platform is Perplexity, where it achieves a 10.6% recommendation coverage rate and a 7.9% rank-one rate. Its weakest platform is Gemini, where recommendation coverage drops to 7.9% and the rank-one rate falls to 1.1%.

The gap between mention presence and recommendation coverage is the defining metric for Chase. The bank is visible but not recommended. In an AI-driven discovery environment, that is not a neutral position. It is a commercially dangerous one.

What Chase Is Winning

Chase has the strongest raw mention presence among the top five banks by AI Authority Value, appearing in 45% of all observations. This is not a small advantage. It means Chase's brand is broadly retrievable across AI platforms and prompt types. The bank is not invisible. It is present.

Chase performs best on Perplexity, where it achieves a 10.6% recommendation coverage rate and a 7.9% rank-one rate. On this platform, Chase's net sentiment score of 0.3567 is higher than its category average, and its negative visibility rate drops to zero. Perplexity appears to surface Chase in more favorable contexts than other platforms, suggesting that the source footprint visible to Perplexity's retrieval layer includes more positively framed material.

In the Best Bank & Account Discovery cluster, Chase achieves its highest monthly AI Authority Value at $218,087. This consideration-stage cluster represents the earliest buying moment, and Chase's presence here indicates the bank is entering the conversation for consumers beginning their research, even if recommendation conversion in that cluster remains limited.

Chase's average recommended rank of 2.40 when it does receive recommendation credit is competitive. When Chase is recommended, it tends to appear in the top two or three positions. The problem is not rank position once recommended. The problem is recommendation frequency.

Where Chase Has the Clearest AI Visibility Gaps

The most significant gap is the conversion rate from mention presence to valid recommendation. Chase appears in 45% of observations but is recommended in only 8.7% of them. More than 90% of the time Chase appears in an AI response, it is not being actively recommended. It is being mentioned in neutral contexts, listed without endorsement, or surfaced in negative framing.

Chase's net sentiment score of 0.2721 is the lowest in the category. For comparison, Ally Bank achieves 0.7675, Discover Bank achieves 0.7466, and Capital One achieves 0.6146. Chase's 61 negative mentions across 1,536 observations represent a 3.97% negative visibility rate that is the third highest in the category. On ChatGPT, where Chase appears in 54.9% of responses, its net sentiment score falls to 0.0685 and its negative visibility rate rises to approximately 12%. This platform-specific weakness suggests that ChatGPT is surfacing negative or cautionary content about Chase at a much higher rate than other platforms, and that specific sources within the public evidence layer are driving that framing.

Chase's recommendation coverage varies significantly by platform. On Gemini, recommendation coverage is 7.9% with a rank-one rate of just 1.1%. On Google AI Mode, recommendation coverage drops to 2.4% with a rank-one rate of 0.4%. These are near-zero recommendation signals on platforms that are increasingly important for consumer discovery at the decision stage.

In the Bank Comparison & Alternatives cluster, Chase captures only $107,274 against a total opportunity of $8.78 million, achieving a 5.5% top-three recommendation rate. This evaluation-stage cluster is where consumers actively compare options, and Chase is being displaced by Capital One and Ally Bank, which together capture nearly $1 million in this cluster alone.

In the Bank Pricing, Fees & Rates Research cluster, Chase's top-three recommendation rate falls to 4.1% and its average recommended rank drops to 2.97. This decision-stage cluster carries the highest per-observation commercial value in the category, and Chase is not winning it.

Biggest Opportunity

The clearest opportunity for Chase is converting its high mention presence into recommendation-stage visibility by improving the quality and framing of the public evidence that AI systems use to generate shortlists. Chase does not need more mentions. It needs better mentions. The bank's 45% raw mention presence rate is already among the highest in the category. The problem is that most of those mentions are neutral or negative, and very few result in recommendation credit.

The most actionable path is to strengthen Chase's citation architecture across the Bank Comparison & Alternatives and Bank Pricing, Fees & Rates Research clusters, the two stages where Chase is most visibly displaced by Capital One and Ally Bank. That means ensuring that comparison content, review material, rate information, and customer experience data are structured for AI retrievability and framed in positive, recommendation-eligible contexts. Chase's platform-specific weakness on ChatGPT, where its net sentiment score drops to 0.07, suggests that specific sources are driving negative framing on that platform. Identifying and correcting those sources is the fastest path to improving recommendation coverage where Chase's brand presence is already the highest in the category.

Prompt Evidence

Perplexity / Best Bank & Account Discovery Prompt: "What is the best bank for a checking account?" Result: Chase appeared in the response but was not advanced to a top-three recommendation position, reflecting the broader pattern of presence without recommendation conversion.

ChatGPT / Bank Comparison & Alternatives Prompt: "Compare Chase and Capital One for savings accounts." Result: Chase was surfaced in a neutral comparison context without receiving recommendation credit, while Capital One received positive shortlist framing.

Gemini / Bank Pricing, Fees & Rates Research Prompt: "Which bank has the lowest fees for personal banking?" Result: Chase was not recommended. Capital One and Ally Bank received the top recommendation positions, consistent with Gemini's category-level pattern of advancing digital-native banks at the decision stage.

Google AI Overviews / Best Bank & Account Discovery Prompt: "Best bank for customer service in 2026." Result: Chase appeared in the response with positive framing but was not ranked in the top three, suggesting that positive surface-level framing is not sufficient to earn recommendation credit without stronger underlying citation support.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map Chase's full recommendation footprint across all six platforms and three clusters to identify the specific prompts, sources, and competitor displacement patterns driving the visibility-to-recommendation gap, with particular focus on ChatGPT and Google AI Mode.

Phase 2: Recommendation Readiness Plan Identify the citation sources, content gaps, and framing issues preventing Chase from converting mention presence into recommendation credit, and prioritize platform-specific corrections for ChatGPT and Gemini where the negative visibility rate is highest.

Phase 3: Owned Answer Layer Buildout Structure Chase's official product pages, rate information, fee disclosures, and customer experience content for AI retrievability, ensuring that owned sources provide clean, positively framed, and citable material for AI systems to synthesize at the comparison and decision stages.

Phase 4: Citation / Authority Layer Development Strengthen third-party validation signals through comparison articles, review content, and community-sourced discussions that frame Chase in positive recommendation contexts, particularly for the evaluation and decision-stage clusters where competitor displacement is most acute.

Phase 5: Monthly AI Visibility and Recommendation Tracking Monitor Chase's recommendation coverage, sentiment score, rank position, and captured AI Authority Value across all platforms and clusters each month to measure directional progress and adjust the source and framing strategy.

Why This Matters

Chase is one of the most recognized banking brands in the United States. It appears in nearly half of all AI responses about best banks. Yet fewer than one in ten of those appearances result in a recommendation. In an AI-driven discovery environment, where consumers increasingly ask AI systems to build their shortlists before they ever visit a bank website or branch, being mentioned without being recommended is not a neutral outcome. It is a losing one.

The banks winning in AI discovery share a common pattern: clean, positive, and citable public evidence that gives AI systems clear reasons to advance them. Chase has brand awareness without shortlist eligibility. The next move is not to generate more mentions. It is to improve recommendation-stage visibility by strengthening the evidence layer that AI systems use to decide which brands to advance at the moment a consumer's shortlist is being formed.

Core Metrics

• Mentions: 691
• Valid recommendations: 133
• Valid recommendation coverage: 8.7%
• Top 3 recommendation count: 92
• Top 3 recommendation rate: 6.0%
• Rank 1 recommendation count: 71
• Rank 1 recommendation rate: 4.6%
• Average recommended rank: 2.40
• Positive mentions: 249
• Neutral mentions: 381
• Negative mentions: 61
• Raw mention presence rate: 45.0%
• Monthly AI Authority Value: $473,730
• Monthly AI Recommendation Value: $179,824
• Monthly AI Visibility Assist Value: $293,907
• Captured share of AI opportunity: 1.6%
• Strongest cluster by recommendation behavior: Best Bank & Account Discovery
• Strongest platform by recommendation behavior: Perplexity

Note: Monthly AI Authority Value, AI Recommendation Value, and AI Visibility Assist Value are modeled benchmark estimates based on commercial intent modeling. They are not revenue, pipeline, or booked demand figures.

Sentiment Score

Sentiment Score = (positive mentions x 1 + neutral mentions x 0 + negative mentions x -1) / total mentions

Chase Sentiment Score = (249 x 1 + 381 x 0 + 61 x -1) / 691 = 188 / 691 = 0.2721

This score matters because unclassified mention counts are misleading in ways that have real commercial consequences. Chase appears in 691 observations, but that number includes 61 negative mentions and 381 neutral mentions that carry no recommendation value. Counting all 691 appearances as wins would overstate Chase's effective AI visibility by a factor of more than five. A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention are not equal outcomes. Treating them as equivalent is bad measurement. Classified sentiment is required before any meaningful interpretation of AI visibility can be made.

Sentiment by Platform

Methodology

1. Market studied: Best Banks, covering retail banking, online banking, savings accounts, checking accounts, and banking services in the United States consumer market.
2. Brands included: Ally Bank, Bank of America, Capital One, Chase, Citibank, Discover Bank, Marcus by Goldman Sachs, PNC Bank, U.S. Bank, and Wells Fargo. This is not a full market census and does not include all active competitors in the category.
3. Data collection window: June 2026. Data was generated on June 17, 2026. This is a point-in-time benchmark.
4. AI platforms tested: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity.
5. Observations analyzed: 1,536 total AI observations across all platforms and clusters. Unique prompt count was not available in the public version of this dataset.
6. Prompt clusters: Three public high-intent clusters were analyzed. Best Bank & Account Discovery covers the consideration stage. Bank Comparison & Alternatives covers the evaluation stage. Bank Pricing, Fees & Rates Research covers the decision stage.
7. Definition of a mention: A mention is recorded when a company name appears anywhere in an AI-generated response, regardless of sentiment, framing, or recommendation status.
8. Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality recommendation that earns recommendation credit. Neutral references, cautionary mentions, and competitor-displacement appearances are not counted as valid recommendations. This distinction is the foundation of the LLM Authority Index methodology.
9. Metrics used: Valid recommendation coverage, top-three recommendation rate, rank-one recommendation rate, average recommended rank, net sentiment score, monthly AI Authority Value, monthly AI Recommendation Value, monthly AI Visibility Assist Value, and captured share of AI opportunity.
10. Modeled value: Monthly AI Authority Value, Recommendation Value, and Visibility Assist Value are modeled benchmark estimates derived from commercial intent modeling applied to recommendation frequency and rank position. These figures are not revenue, pipeline, bookings, or return on investment.
11. Limitations: AI outputs change with model updates, retrieval layer shifts, and changes in the public source environment. This report reflects a single measurement window and should not be treated as a permanent competitive ranking. The competitor universe is fixed and does not represent all market participants. Platform-level sentiment figures are derived from the available observation counts and may reflect prompt distribution differences across platforms as well as source content differences.

Find Out Where You Stand in AI Recommendations

The LLM Authority Index benchmark shows where the category stands. It does not show which specific prompts Chase wins or loses, which sources are shaping its framing on ChatGPT, or which content and citation changes are most likely to improve shortlist eligibility. A company-specific AI visibility analysis would identify the exact prompts where Chase is being displaced, the platforms where the risk is highest, and the source and content changes most likely to move the needle. CiteWorks Studio works with banking brands to close the gap between brand presence and recommendation-stage visibility, using evidence from the public source layer to build a corrective strategy grounded in how AI systems actually generate answers.