Kuroma
AI Readiness benchmark for Banking / Neo-banking
Across 364 audited domains we have classified as Banking / Neo-banking, the median AI Readiness score is 39 out of 100. That is 2 points below the median for all 30,309 audited domains in the corpus (41).
How the scores are distributed
Half of the 364 audited Banking / Neo-banking domains score between 34 and 43. The corpus-wide interquartile range, across all 30,309 audited domains, is 36 to 49.
Which factors this industry fails most often
The rubric scores 19 factors. This table shows 15 of them: the rest were counted on fewer than 30 audits in this industry, which is too few to publish a rate from. A factor counts as failed here when a site scores below 50 out of 100 on it. The last column is how many audits each rate was calculated from. Media Accessibility is not scored on every kind of page: a rankings table or a paginated index has no question to answer and no image to caption. That row therefore leaves out every audit of a kind of page the factor does not apply to, and every audit that did not record a kind of page we could read. Every other row counts every audit in this industry that scored the factor.
| Factor | Fails in Banking / Neo-banking | Fails corpus-wide | Difference | Audits counted |
|---|---|---|---|---|
| Answer Box Potential AI Readiness · 7.2% of the total score |
98% | 97% | +1 | 364 |
| Trust & Brand Signals Authority Signals · 9% of the total score |
91% | 83% | +8 | 364 |
| Media Accessibility On-Page Content · 1.8% of the total score · counted only on pages this factor applies to |
89% | 86% | +3 | 248 |
| Schema Markup Completeness Technical Optimization · 1% of the total score |
85% | 63% | +22 | 364 |
| External Citations & References Authority Signals · 9% of the total score |
83% | 70% | +13 | 364 |
| Content Freshness AI Readiness · 5.7% of the total score |
62% | 52% | +10 | 364 |
| Structured Data Quality On-Page Content · 4.5% of the total score |
60% | 77% | -17 | 364 |
| AI Discoverability AI Readiness · 3% of the total score |
44% | 36% | +8 | 364 |
| Heading Hierarchy On-Page Content · 2.8% of the total score |
42% | 39% | +3 | 364 |
| AI Readability Score On-Page Content · 3% of the total score |
24% | 12% | +12 | 364 |
| Mobile Optimization Technical Optimization · 4% of the total score |
17% | 14% | +3 | 364 |
| Raw HTML Availability AI Readiness · 1.5% of the total score |
17% | 16% | +1 | 364 |
| Content Depth & Comprehensiveness On-Page Content · 6.5% of the total score |
12% | 16% | -4 | 364 |
| Page Performance Indicators Technical Optimization · 2% of the total score |
7% | 18% | -11 | 364 |
| Crawlability & Accessibility Technical Optimization · 13% of the total score |
0% | 0% | 0 | 364 |
Where Banking / Neo-banking diverges from everyone else
Relative to the whole corpus, this industry is distinctively weak on Schema Markup Completeness (+22 points), External Citations & References (+13 points), AI Readability Score (+12 points).
It is distinctively strong on Structured Data Quality (-17 points), Page Performance Indicators (-11 points), Content Depth & Comprehensiveness (-4 points).
Schema Markup Completeness: fails on 85% of the 364 Banking / Neo-banking audits it was measured on
Controlled tests show no AI-citation lift from schema.org markup, and Google states no special structured data is needed for AI features. Near-zero weight is kept only for classic rich results and Bing grounding.
External Citations & References: fails on 83% of the 364 Banking / Neo-banking audits it was measured on
Citing sources helps in controlled sandboxes but the effect shrinks toward zero in competitive replications, so claims are capped and the weight stays modest within the category.
AI Readability Score: fails on 24% of the 364 Banking / Neo-banking audits it was measured on
Engines preferentially select easier, clearer text, and fluency rewrites improve citation rates in controlled sandboxes. A moderate, causal signal.
How to read this
- What the sample is. The 364 domains we have audited and classified as Banking / Neo-banking. That is a sample of the industry, not a census: it is the set of sites our crawler has reached and scored, and it carries whatever bias that selection has.
- Rubric version 2026-08-02. Every figure here comes from audits scored on this single rubric generation. Scores from different generations measure different things, so they are never mixed. Compatible generations: 2026-08-02.
- Why there are no company names. These are aggregates. We do not publish which domains sit at the bottom of a distribution, and the queries behind this page cannot return a domain name.
- Publishing floor. We publish an industry only once it reaches 100 audited domains, and a factor's failure rate only once 30 audits in the industry count toward that factor. Below either floor the number would move with a handful of sites, so we withhold it.
- What the audit can and cannot see. This audit measures citation readiness: how easily AI engines can crawl, extract, and quote these pages. It cannot see the off-site brand footprint (mentions, reviews, third-party coverage) that gates whether AI engines retrieve a brand at all; Kuroma's visibility scans and off-site signals track that side. Read the full methodology.