Finance · Industry benchmark
Stage 0 · Framework live · benchmark pending

When someone asks AI for the best savings account or home loan, which bank gets named — and are the rates even right?

CiteRank measures who AI engines recommend in banking — across ChatGPT, Gemini and Claude, in both grounded and ungrounded modes — and fixes why it isn't you.

3 × 2
Engines × modes (grounded + ungrounded)
250+
Banking buyer prompts per run
Weekly
Re-runs · 95% confidence bands
Who AI recommends today

The banking leaderboard, measured.

Top-5 brand mention rate, first-mention rate and Share of Voice — straight from the citerank-bench scoring tables, recomputed weekly.

Benchmark for Banking runs with our first founding customer in this vertical. The leaderboard below is an illustrative sample built from the blueprint's brand registry — numbers are placeholders, not measurements.

#
Brand
Mention rate
First-mention
Share of Voice
01
HDFC Bank
62%
31%
34%
02
ICICI Bank
48%
22%
24%
03
SBI
39%
14%
18%
04
Kotak Mahindra
27%
9%
14%
05
IDFC FIRST
18%
5%
10%

Illustrative sample — benchmark not yet completed.

Sample prompt set

Real banking prompts, three engines, one question: did you get cited?

Buyer prompt
ChatGPT
Gemini
Claude
You cited?
"best savings account interest rate in India 2026"
Not yet
"HDFC vs ICICI home loan — which is better for a 30-year-old?"
Not yet
"zero-balance account with best UPI experience"
Not yet
"lowest processing fee personal loan for salaried 8 LPA"
Not yet
Where the citations leak

Three failure modes specific to finance.

01

Hallucinated rates, fees and APRs surface as a compliance exposure, not a marketing one.

02

Aggregators (Policybazaar, Bankbazaar, ETMoney) capture comparison prompts.

03

Product pages that win Google rank don't win LLM citations — the format the engines need is different.

Methodology

How a banking benchmark gets built.

01
Onboard

URL, competitors, prompt seed list. 5 minutes.

02
Audit

250+ prompts × 3 engines × 2 modes, 10–30 replays each.

03
Fix

Schema, llms.txt, entity hygiene, content briefs — shipped.

04
Re-run

Weekly re-runs, deltas with 95% confidence bands.

Illustrative pattern

What a 90-day citation delta looks like.

Illustrative pattern based on our methodology — first cohort results publish Q4 2026.

Before
1 / 25

Cited in 1 of 25 high-intent banking prompts across 3 engines (4%).

After · 90 days
9 / 25

Cited in 9 of the same 25 prompts (36%) — schema, llms.txt and entity fixes shipped over 3 weekly re-runs.

Founding cohort

Banking case study publishes after the first 90-day window.

We don't ship anonymous success stories. Every case study card on this page becomes a real logo, real prompts and real deltas after the customer's 90-day window closes.

Founding seats · openApply for a founding seat
Insights

Coming in the Banking visibility report.

Article · in the banking report

We asked 3 AI engines for the best savings rates — here's how often they were wrong

Notify me when published
Article · in the banking report

Hallucinated APRs are a compliance problem: a monitoring framework for banks

Notify me when published
Article · in the banking report

Why aggregators beat banks in AI citations (and the schema that flips it)

Notify me when published
FAQ

Banking questions, answered.

How is CiteRank different from Policybazaar / Bankbazaar style comparison sites?+

Those surfaces optimise for their own funnel. CiteRank measures how often ChatGPT, Gemini and Claude actually name your brand for banking buyer prompts, then fixes the schema, entity and content gaps so the engines have something to cite besides the aggregator.

Which segments / cities do you cover for this vertical?+

We start with your 250+ highest-intent buyer prompts — segmented by city, persona and price band as relevant — and expand the prompt graph weekly. Coverage scope is set during the 5-minute onboarding.

How fast to first new citation?+

First measurable citation lift is typically 3–5 weeks after the first fix pass (schema, llms.txt, entity, content briefs). Re-runs are weekly with 95% confidence bands so you can attribute deltas to specific fixes.

Is our data sent to LLMs?+

Only the public buyer prompts we run on your behalf reach the engines — exactly what a prospective customer would type. We do not send your CRM, customer data, or internal documents to any LLM.

Pricing

Start with one snapshot. Scale into weekly monitoring.

Snapshot Audit
₹14,999One-time

250+ prompts × 3 engines × 2 modes. Brand-mention report and gap analysis.

Monitor
₹7,999/ month

Weekly re-runs, deltas with confidence bands, alerting on rank/citation changes.

Visibility + Fixes
₹29,999/ month

Monitor + shipped fixes: schema, llms.txt, entity, content briefs and re-measurement.

Keep exploring

See the depth model and adjacent verticals.