Financial Technology and Banking Software: AI Visibility Market Discovery Index

Benchmark Summary

Questions This Section Answers

• Which brand led the Financial Technology and Banking Software benchmark in October 2026, and by how much?
• Which brands recorded the largest recommendation coverage increases this month?
• Did any tracked brand decline significantly in October 2026?

Mambu led the Financial Technology and Banking Software benchmark in October 2026, with valid recommendation coverage of 62.2%, ahead of Thought Machine at 46.7% — a 15.5-point gap between first and second. Mambu recorded the largest increase, rising from 33.6% in September 2026 to 62.2% in October 2026, and Q2 recorded the second-largest increase, rising from 4.7% in September 2026 to 20.0% in October 2026. No tracked brand recorded a significant decline in valid recommendation coverage in October 2026.

October 2026 marked a resumption of movement after the quieter September 2026 period. Mambu rose from 0.0% in July 2026 to 62.2% in October 2026, Q2 from 0.0% to 20.0%, and Thought Machine from 0.0% to 46.7%, with each of these three brands improving in each of the three months since July 2026. Azentio Software rose from 1.4% in August 2026 to 2.2% in October 2026, SAP Fioneer rose from 0.8% in September 2026 to 1.1% in October 2026, and Avaloq eased from 3.9% in September 2026 to 3.3% in October 2026, all within normal month-to-month variation. The benchmark began with 800 prompt-surface observations in October 2026 and produced 90 qualified observations after qualification.

Current Benchmark at a Glance

Qualified surface breadth counts the canonical AI/search surface families with at least one qualified observation: ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode. The benchmark measured four surface families in July 2026 and all six from August onward.

For the strategic interpretation of this benchmark, read CiteWorks Studio's analysis of How AI Search Is Recommending Financial Technology and Banking Software

Research Scope and Qualification

Questions This Section Answers

• How did the 800 raw prompt-surface observations narrow to 90 qualified benchmark observations?
• Why are brand-level percentages based on qualified observations rather than the raw collection?

The public benchmark is narrower than the raw collection universe by design. The table below shows how the raw prompt collection narrowed to the qualified observations that form the public denominator.

Brand-level percentages use the qualified observations as the public denominator, not the raw collection. See the AI Visibility Market Discovery Methodology for full details.

Current Brand Standings

Questions This Section Answers

• How do the tracked brands rank by valid recommendation coverage in October 2026?
• Which brands appear most often and which remain at zero presence?

This table is the primary current-month benchmark view, sorted by valid recommendation coverage.

How to Read the Standings

• Presence rate: the share of qualified observations in which the brand is mentioned at all, whether or not it is recommended.
• Valid recommendation coverage: the share of qualified observations in which the brand appears in a valid recommendation shortlist.
• Top-three rate: the share of qualified observations in which the brand appears among the top three recommended options.
• Rank-one rate: the share of qualified observations in which the brand is recommended as the single best option.
• Net sentiment: the balance of positive over negative mentions, scaled from -1 to +1.

For formulas and denominator rules, see AI Visibility Market Discovery Metric Definitions.

Recommendation Coverage Movement

Questions This Section Answers

• Which brands moved beyond normal month-over-month variation between September and October 2026?
• How far has Mambu risen since the July 2026 baseline, and which brands stayed flat?

Between September 2026 and October 2026, Mambu rose 28.6 points from 33.6% to 62.2%, Q2 rose 15.3 points from 4.7% to 20.0%, and Thought Machine rose 15.5 points from 31.2% to 46.7%, each beyond normal month-over-month variation. Avaloq eased 0.6 points from 3.9% to 3.3%, Azentio Software rose 1.4 points from 0.8% to 2.2%, and SAP Fioneer rose 0.3 points from 0.8% to 1.1%, all within normal month-to-month variation.

Largest Increase: Mambu

Mambu recorded the largest valid recommendation coverage increase in the series, rising from 0.0% in July 2026 to 62.2% in October 2026 — a 62.2-point gain that is the largest in the series. Presence climbed from 0.0% in July 2026 to 96.7% in October 2026. Mambu improved in each of the three months since July 2026, and its top-three rate reached 32.2% and rank-one rate 24.4% in October 2026.

Largest Decline: None

No tracked brand recorded a decline in valid recommendation coverage that exceeded normal variation in October 2026.

Category Leader: Mambu

Mambu led the benchmark in October 2026 with 62.2% valid recommendation coverage, ahead of Thought Machine. Its presence rate of 96.7% and rank-one rate of 24.4% were the highest in the benchmark on both measures.

Q2

Q2 recorded the second-largest valid recommendation coverage increase in October 2026, rising from 0.0% in July 2026 to 20.0% — a 20.0-point gain placing it third in the standings. Q2 improved in each of the three months since July 2026, with presence reaching 28.9% and a top-three rate of 11.1% in October 2026.

Thought Machine

Thought Machine rose from 0.0% in July 2026 to 46.7% in October 2026, an increase of 46.7 percentage points. Thought Machine improved in each of the three months since July 2026, with presence climbing to 75.6% and a top-three rate of 26.7% in October 2026. Its 3.3% rank-one rate trails its 26.7% top-three rate, indicating it is more often named as an option than as a first choice.

Stable Brands

Avaloq, Azentio Software, Bantotal, SAP Fioneer, Silverlake Axis, Technisys, and Tietoevry Banking each remained within normal month-to-month variation in October 2026. Avaloq's coverage of 3.3% was down 0.6 points from 3.9% in September 2026. Azentio Software's coverage of 2.2% was up 1.4 points from 0.8% in September 2026. SAP Fioneer's coverage of 1.1% was up 0.3 points from 0.8% in September 2026. Bantotal, Silverlake Axis, Technisys, and Tietoevry Banking recorded no presence and no recommendation coverage in October 2026, holding at their July 2026 levels.

Recommendation Placement Snapshot

Questions This Section Answers

• How do top-three and rank-one rates differ among Mambu, Thought Machine, and Q2?
• Which brand converts top-three placements into rank-one recommendations most effectively?

Coverage alone does not show how prominently a brand is recommended. The table below shows top-three and rank-one rates for the leading brands by valid recommendation coverage.

Similar coverage levels can still hide very different first-position rates. Mambu converted most of its top-three placements into rank-one recommendations (24.4% of 32.2%), whereas Thought Machine reached a top-three rate of 26.7% but a rank-one rate of just 3.3%.

Buyer-Intent Distribution

Questions This Section Answers

• Which buyer-intent class do all 90 qualified observations fall into?
• Does the current series yet contain qualified observations for Pricing & Value or Multi-Brand Comparison?

All 90 qualified observations in October 2026 fell into the Brand Recommendation class, where a buyer is seeking a brand to meet a stated need.

The current public series measures only the Brand Recommendation class of discovery and does not yet contain qualified observations in the Pricing & Value or Multi-Brand Comparison classes.

Historical Measurement Record

Questions This Section Answers

• How has the qualified observation count and coverage leader changed across the four 2026 measurements?
• Which brand recorded the largest coverage movement in each measurement period?

This is an evergreen benchmark URL. New measurements are added to the same report.

Evidence and Source Layer

Questions This Section Answers

• What does each prompt-level observation retain, and what does source presence actually prove?
• Why is source presence not automatically proof that a source caused a recommendation?

The benchmark is built from prompt-level observations that retain the query, AI/search surface, answer, brand outcome, recommendation placement, sentiment, and, where exposed, citations or attributable evidence sources. Source presence is evidence about the information environment — it is not automatically proof that the source caused the recommendation.

Scope Boundaries

This public benchmark does not measure the following:

• Market share or sales attribution
• Every possible AI response to a given question
• Organic-search ranking
• Social mention volume
• Private or sponsored channels
• Causality from a metric movement alone

No metric movement in this benchmark should be read as causal on its own.

Top 10 Cited Domains

Questions This Section Answers

• Which domains received the most citations across AI platform responses in October 2026?
• What types of sources dominate the top 10, and do any tracked brand domains appear?

Across all AI platform responses in October 2026, 6,099 citations were observed, spanning 1,356 unique domains.

Citations are broadly distributed: the top 10 domains account for roughly one third of all citations, while the remaining citations are spread across more than 1,300 other domains. No tracked brand's own domain appears in the top 10. Financial and investment platforms dominate the list, including finance.yahoo.com, marketbeat.com, investing.com, fool.com, seekingalpha.com, simplywall.st, and tradingview.com, alongside general search and user-generated sources such as google.com, youtube.com, and reddit.com.

About This Benchmark

The LLM Authority Index AI Visibility Market Discovery Index tracks how AI search and assistant surfaces present brands in response to natural-language discovery prompts. It measures presence, recommendation coverage, placement, and sentiment for each tracked brand across six canonical AI surface families.

• AI Visibility Market Discovery Methodology
• AI Visibility Market Discovery Metric Definitions
• AI Visibility Market Discovery Research Standards
• Modeled AI Visibility Authority Value

Get a Company-Level Authority Index

The public industry benchmark shows category-level standings. A company-level Authority Index can go deeper.

The public percentage cannot identify the prompts, competitors, or sources causing the result.

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