Overview Leaderboard Methodology
Industry Report

Merchant Acquirers

Financial institutions and companies that process card payments on behalf of merchants and settle funds into merchants' bank accounts.

Brands tracked: 36
Brands analyzed: 36
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 281
Total responses: 1,435
Top Brand Overall?
Adyen
72/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Adyen
88

Highest score on the LBA metric.

Authority Leader?
Adyen
45

Highest score on the Authority metric.

TOM Leader?
Adyen
92

Highest score on the TOM metric.

Adyen leads the Merchant Acquirers category in AI responses

Adyen is the most consistently-surfaced brand when users ask AI assistants about Merchant Acquirers, but not to the point of monopolising the conversation. Worldpay and a handful of others appear regularly, giving users a reasonable set of alternatives to compare. The top of this leaderboard is where most of the AI-driven traffic will go.

Brand Leaderboard All 36 Merchant Acquirers brands ranked

Ranked by overall AI Visibility Score (geometric mean of LBA, Authority, and TOM). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
Adyen
adyen.com
 88
45
92
72
2
Worldpay
worldpay.com
 79
34
88
62
3
Fiserv
fiserv.com
 72
22
83
51
4
Checkout.com
checkout.com
 74
15
52
39
5
Elavon
elavon.com
 64
9
70
34
6
Clover
clover.com
 73
6
42
27
7
Braintree
braintreepayments.com
 82
7
32
26
8
Global Payments
globalpayments.com
 70
3
70
25
9
Chase Merchant Services
chasemerchantservices.com
 68
3
32
19
10
Helcim
helcim.com
 71
11
6
17
11
Chase Payment Solutions
chase.com
 63
2
21
14
12
C
Chase Paymentech
paymentech.com
 61
2
11
11
13
Zettle
zettle.com
 68
2
4
8
14
Barclaycard Payments
barclaycard.co.uk
 67
0
13
2
15
Nexi
nexigroup.com
 51
0
23
2
16
Nuvei
nuvei.com
 59
0
10
2
17
TSYS
tsys.com
 61
0
19
2
18
Worldline
worldline.com
 58
0
11
2
19
N
Nets
nets.eu
 67
0
4
1
20
Shift4
shift4.com
 71
0
1
1
21
SumUp
sumup.com
 73
0
1
1
22
AIB Merchant Services
aibms.com
 53
0
0
0
23
Bambora
bambora.com
 52
0
0
0
24
Cielo
cielo.com.br
 60
0
0
0
25
EVO Payments
evopayments.com
 43
0
0
0
26
L
Lloyds Cardnet
lloydsbankcardnet.com
 47
0
0
0
27
Mercado Pago
mercadopago.com
 78
0
0
0
28
Moneris
moneris.com
 67
0
0
0
29
Network International
network.ae
 58
0
0
0
30
PagSeguro
pagseguro.uol.com.br
 69
0
0
0
31
PayPal Invoicing
paypal.com
 74
0
0
0
32
PayTrace
paytrace.com
 53
0
0
0
33
PayU
payu.com
 68
0
0
0
34
Square Appointments
squareup.com
 75
0
0
0
35
Stone
stone.com.br
 63
0
0
0
36
Stripe Billing
stripe.com
 81
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 281 shared Merchant Acquirers prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Geometric mean of LBA, Authority and TOM: (LBA × Authority × TOM)^(1/3). Geometric mean is used so that any single weak metric pulls the overall score down, rather than being masked by strength elsewhere.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 281 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →