How AI Search Is Recommending Financial Technology and Banking Software: Monthly Trends

Executive Summary

Mambu extended its lead in October 2026, reaching 62.2% valid recommendation coverage, up 28.6 points from September 2026 (33.6%). The gap to second-place Thought Machine widened to 15.5 points. This was the largest month-over-month move by any brand in the series. The category now separates into three tiers: two brands with meaningful coverage, one brand in the mid-range, and seven brands below 5%.

Thought Machine held second place at 46.7% coverage, up 15.5 points from September 2026 (31.2%), also significant. Q2 was the third significant riser, climbing to 20.0% from 4.7% in September, a 15.3-point gain. All three brands have now posted significant upward movement for three consecutive months. The remaining seven tracked brands showed no significant movement in October 2026.

Avaloq slipped 0.6 points from September 2026 (3.9% to 3.3%), the only brand to decline. The movement was not large enough to register as significant. Azentio Software rose 1.4 points from September 2026 (0.8% to 2.2%). SAP Fioneer edged up 0.3 points to 1.1%. Bantotal, Silverlake Axis, Technisys, and Tietoevry Banking remain at 0.0% coverage with no presence in the qualified set.

The October 2026 qualified set was 90 observations, down from 128 in September 2026 and 144 in August 2026. Qualified surface breadth expanded to six canonical AI/search families, up from four in July 2026. Recommendation-shaped answers rose sharply from 2.3% in September to 31.1% in October, and valid recommendation shortlist share reached 64.4%, up from 27.3%. The benchmark is capturing more recommendation-oriented output than at any prior point in the series.

Each monthly run begins with 800 prompt-surface observations (695 unique questions) across the benchmark's defined AI/search surface universe in October 2026, level with 800 prompt-surface observations (684 unique questions) in September 2026 and 800 prompt-surface observations (703 unique questions) in August 2026. Of those October prompts, 798 mentioned a tracked brand or competitor; 95 were relevant and 703 were irrelevant. The public metrics use the 90 observations that survive both qualification stages, versus 128 in September 2026 and 144 in August 2026.

Key Findings

Benchmark Context

Questions This Section Answers

• How does the raw collection universe differ from the qualified benchmark set?
• Why did qualified observations drop from 144 in August to 90 in October 2026?

The report separates the raw collection universe from the qualified analysis set. Brand-level recommendation percentages are calculated within the qualified benchmark set.

Qualified surface breadth grew from four canonical AI/search families in July 2026 to six in October 2026. The qualified observation base peaked at 144 in August 2026, moderated to 128 in September 2026, and contracted to 90 in October 2026.

Benchmark-Level Metrics

September 2026 held 128 qualified observations with a 2.3% recommendation-shaped answer share. October 2026 dropped to 90 observations but more than tripled the recommendation-shaped share to 31.1%, alongside a jump to 64.4% valid recommendation shortlist share from 27.3% in September. The response mix shifted substantially: September had 91 factual answers, 22 ranked lists, 12 comparison analyses, and 3 recommendation shortlists; October had 34 factual answers, 28 ranked lists, and 28 recommendation shortlists.

AI Recommendation Trend

Questions This Section Answers

• Which brands posted significant recommendation coverage gains in October 2026?
• How wide is the gap between Mambu and the rest of the field?

Mambu and Thought Machine Now Hold a Two-Brand Lead, with Mambu Widening the Gap to 15.5 Points

Three brands, Mambu, Q2, and Thought Machine, posted significant gains in the current month, each representing a meaningful single-month move. The category-level change was driven by a small number of concentrated, significant upward movements rather than an accumulation of smaller shifts.

What Changed This Month

Questions This Section Answers

• What drove Mambu's step change to 62.2% coverage and 22 rank-one recommendations?
• Why did Thought Machine's coverage rise while its gap to Mambu widened to 15.5 points?
• How did Q2 more than quadruple its recommendation coverage without a matching jump in presence?

Mambu: A Step Change in Coverage

Mambu's valid recommendation coverage reached 62.2% in October 2026, up from 33.6% in September 2026 and 0.0% in July 2026. The 28.6-point current-month gain is significant and is part of a three-month upward streak, a pattern it shares with Q2 and Thought Machine.

Mambu was present in 87 of 90 qualified observations, a 96.7% raw mention presence rate. It earned 56 valid recommendations in October, up from 43 in September, with 29 top-three placements (32.2% rate) and 22 rank-one recommendations (24.4% rate). Its average recommended rank was 3.40.

The distinction between presence and recommendation remains central: Mambu was mentioned in nearly every response and recommended in 62.2% of them. Mambu's rank-one count of 22 is far ahead of the next-highest brand, Thought Machine, at 3.

Highest-priority diagnostic: Which specific prompts and surfaces are driving the October 2026 step change in rank-one recommendations, and is that level sustainable?

Thought Machine: Coverage Grows but the Gap Widens

Thought Machine rose to 46.7% valid recommendation coverage in October 2026, up from 31.2% in September 2026 and 0.0% in July 2026. The 15.5-point current-month gain is significant and extends a three-month upward streak, but the gap to Mambu widened from 2.4 points in September to 15.5 points in October.

Thought Machine was present in 68 of 90 observations (75.6% raw presence) and earned 42 valid recommendations, up from 40 in September. Its top-three rate was 26.7% (24 observations), and it held 3 rank-one recommendations (3.3% rate), with an average recommended rank of 3.86.

Thought Machine added 2 valid recommendations while Mambu added 13, and its rank-one count rose from 1 to 3 while Mambu's rose from 14 to 22. Its presence declined from 81.2% in September to 75.6% in October even as its coverage improved, a pattern the current dataset cannot explain on its own.

Highest-priority diagnostic: Which competitor takes the rank-one position when Thought Machine is included in a recommendation shortlist?

Q2: Coverage More Than Quadruples

Q2 advanced to 20.0% valid recommendation coverage in October 2026, up from 4.7% in September and 0.0% in July. The 15.3-point current-month gain is significant and marks a third consecutive month of gains. Q2 is now the clear third-place brand, 26.7 points behind Thought Machine.

Q2 was present in 26 of 90 observations (28.9% raw presence) and earned 18 valid recommendations, up from 6 in September. It held 10 top-three placements (11.1% rate) and 1 rank-one recommendation (1.1% rate), with an average recommended rank of 2.92.

Q2's presence grew only modestly from 22.7% in September to 28.9% in October, but its recommendation count tripled from 6 to 18. Its conversion of presence into recommendation improved materially this month.

Highest-priority diagnostic: Which prompts moved Q2 from presence to recommendation between September and October 2026?

Mid-Tier and Zero-Coverage Brands

Avaloq reached 3.3% coverage in October 2026, down from 3.9% in September, with 3 valid recommendations (down from 5). Present in 8 observations (8.9% presence), Avaloq held 1 top-three and 1 rank-one placement, with an average recommended rank of 5.33. The 0.6-point decline was not large enough to register as significant.

Azentio Software rose to 2.2% coverage, up from 0.8% in September, with 2 valid recommendations and 1 top-three placement. SAP Fioneer edged to 1.1% coverage, with 1 valid recommendation placed at an average rank of 4.

Bantotal, Silverlake Axis, Technisys, and Tietoevry Banking remained at 0.0% coverage in October 2026, as they did in July, August, and September. None of the four appeared in any qualified observation in October.

Highest-priority diagnostic: For each brand, which specific prompts and surfaces have produced any positive signal in prior months, and what evidence sources underpin them?

Buyer-Intent Interpretation

Questions This Section Answers

• Which buyer-intent clusters were represented in the October 2026 qualified set?
• What commercial questions can the benchmark not yet answer about pricing or head-to-head comparisons?

All 90 qualified observations in October 2026 fell into the Brand Recommendation cluster. No qualified observations were recorded for the Pricing & Value or Multi-Brand Comparison clusters. The public benchmark therefore cannot yet answer which brands surface in cost discussions or how brands fare in direct head-to-head comparisons. Those commercial questions require cluster-specific coverage that has not yet appeared in the qualified set.

Brand Opportunity Summary

Questions This Section Answers

• Which brands show positive recommendation signals but limited presence?
• What is the highest-priority diagnostic for brands stuck at 0.0% coverage?

The benchmark identifies where attention is warranted; a company-level analysis is needed to explain why.

Evidence Behind the Benchmark

Questions This Section Answers

• What data makes up the aggregate metrics in this report?
• How is source presence treated in the analysis?

The aggregate metrics are built from prompt-level observations (query, surface, recommendation outcome, rank, sentiment, and citations where exposed). Company-level analysis can go deeper into prompt, competitor, surface, and evidence patterns. Source presence is not automatically treated as proof of causation.

About This Benchmark

This report is part of the LLM Authority Index AI Visibility Market Discovery research program.

• AI Visibility Industry Market Methodology
• AI Visibility Industry Market Metrics
• AI Visibility Industry Market Standards

Report-Specific Interpretation Notes

• Small-count movement: July 2026 had only 15 qualified observations; movement from a near-zero baseline is more sensitive to individual observations than movement measured on a larger base.
• Qualified denominator vs raw collection: all percentages are calculated within the qualified set, not the raw prompt count.
• Directional analysis: category-level movement identifies changes worth investigating; it does not by itself establish the cause of those changes.

Next Step

The Public Benchmark Shows Where a Brand Is Winning or Losing. A Company-Level Audit Shows Why.

The aggregate percentages in this report only begin the analysis. Beneath Mambu's 62.2% coverage and Thought Machine's 46.7% lie specific questions: which high-intent prompts each brand wins, which competitor takes the recommendation when a brand is mentioned but not recommended, what attributes AI associates with each option, and which external sources shape those answers. The same questions apply to Q2's rapid conversion improvement and to the brands with no recommendation presence at all.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy.

Request an AI visibility audit