When someone asks AI which bank, lender, or platform to trust, the answer names two or three. Scanley finds the recommendations you’re missing, works out why, and drafts the fix for your sign-off.
AI visibility for financial services
FX-001, review draft
Status: Awaiting Copperline marketing and compliance approval. This is a proposed page only. It has not been published.
Suggested page title Checking Account Rates | Copperline Credit Union
Suggested page URL /checking-account-rates/
Rates effective [date, set on publish]
No dashboard to remember. Scanley reports where your team already works.
An answer engine does not hand back a list to browse. It picks a short answer, and everyone left out of it was never considered. There is no alert when it happens: your rankings look fine, your traffic looks normal, and the question gets decided without you.
51% of U.S. consumers use AI to get financial advice or information.
J.D. Power, survey of 4,000 consumers, 2026.
What happens next: a meeting about what a 62 means, who moved it, and whose job the fix is.
What happens next: you read it and approve it. The diagnosis, the draft, and the verification were already done.
The same questions, re-asked across every engine each month, so movement is real movement.
Why the engine skipped you, down to the page and the line that caused it.
The fix, written and delivered to your channel as a document to review.
Marketing reads it, compliance signs it. Nothing ships without a signature.
The next run re-asks the question. Until it moves, we do not claim it.
Answer engine optimization is a job. Someone has to re-ask the questions every month, notice the citation that quietly disappeared, work out which page caused it, draft the fix, and carry it through compliance. Scanley does that role’s work for a monthly engagement. Your team reviews, signs, and stays in control.
what one digital marketing manager costs in base pay, before benefits and ramp
PayScale, Indeed, ZipRecruiter, Salary.com and Glassdoor, 2026. The surveys disagree by fifty thousand dollars, so we quote the range, not an average.
Which engine, which day, how many runs, and whether you were recommended or just listed nearby. Every figure states its method, so the number that reaches your board is one your team can defend.
“Best auto loan rates in Tulsa.” The answers quoted five competing credit unions by name, with APRs. The institution we scanned was named by none of the four engines measured.
The cause was one line: its rates lived where no engine could read them, no figure in the text, no effective date. Every cross on your scan gets this treatment: the miss, the cause, and the fix it needs.
One finding from a live scan, 2026.
The auto loan question is still the costliest gap in the AI answers, and this week the review record explains part of why: members are loudest about the app, not the products.
Next stepApprove the auto loan page draft; it is a fifteen minute review and it targets the scan’s costliest gap.
Built by the team behind a financial data platform trusted by 10,000+ professionals. Methodology led by a search director from the largest health publisher on the internet.
Five real questions your customers ask, run across the latest AI answer engines, one scored report.
Nine-pillar diagnosis of exactly why AI skips you, with a prioritized fix list.
Monthly scans, drafted fixes with your approval, and weekly Scanley Insights that read your scans, review sentiment, Google Analytics and Search Console together, delivered as Monday Metrics where your team works.
Hands-on fix execution with your marketing and compliance teams.
The AI scan is free and needs no conversation. Figures for everything after it come with the report, once you have seen what your market actually looks like.
It is the next chapter of it. Traditional SEO earns rankings in a list of links; answer engine optimization earns citations inside the single answer AI assistants give your customers. The disciplines overlap, but the measurements, fixes, and competitors are different.
The work of making your company the one AI assistants name when someone asks which bank, lender, or platform to trust. It spans how your site is structured, how consistently the web's records describe you, and whether your rates and products exist as text an engine can quote.
ChatGPT, Gemini, Perplexity, Claude, and Google's AI Overviews: the assistants your customers actually use. As new engines earn real usage, they join the scan.
A scored report on five of the questions your customers actually ask: who each engine named, who it recommended, and where you were missing. Question by question, engine by engine, with the method printed on the report. The full 21-question set runs once you are in a program.
The free AI scan is delivered in two business days. Remediation typically starts showing citation movement within one to two monthly scan cycles, tracked against your locked baseline question set so the comparison is honest.
They do, and most tools hide it. Once you are a customer we ask every question twice and report how many runs named you, and every number on your report states the engine, the date, and the method, so your team can reproduce it.
Most hand you a single score with no way to check it. We hand you the questions, the engines, the dates, and the difference between being recommended and merely being mentioned, because your board will ask, and a number you cannot defend is worse than no number.
Not to measure you. The scan and the audit read what the public web and the engines already see. Fixes ship through your team and your existing publishing process, or through an implementation engagement we scope with you.
Every fix ships as a draft your team approves before anything goes live. Nothing touches your site, your rates, or your name without sign-off.
The AI scan is free, no sales call required. The audit is a one-time engagement, Scanley, the managed agent, is monthly, and implementation is scoped with your team. Figures come with your scan results, once you have seen what your market actually looks like.
Keep them. An agency optimizes the list of links; we measure and win the single answer above it. Your scan will show how different those two leaderboards are, and every fix we draft is visible to your team, so the work never blurs together.
Yes, and smaller companies often have the most to gain: an AI answer names two or three, not ten, so a single earned citation moves real share. The scan is scoped to your market, whether that is your metro or your product category.
Yes. Community banks and credit unions lose the same answers to the same national names, and the measurement and the fix loop are identical. Every fix still ships as a draft your team approves, whether your examiners are FDIC, OCC, or NCUA, and the scan reads only the public web either way.
Yes. The loop is identical; only the question set changes. For a lender or a platform the scan asks the questions your buyers ask, scoped to your product category rather than a metro, and every fix still ships as a draft your team approves before anything goes live.
A monthly public ranking of who AI actually recommends for banking, metro by metro. Your metro's edition is free by email.
Whatever you decide. Some teams take the report and act on it themselves; most ask for the audit to learn why the misses are misses. There is no obligation either way, and no sales call unless you ask for one.