Finance AI Index
Index Vendors › Fiserv · September 2026 Edition
3 categories · Named, not ranked

Fiserv

14Judge labels
0First choices
7Negative labels
8 of 12Models named it
3Categories
September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.
Standing
6 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Fiserv was named 6 times in B2B payment acceptance and 2 other categories, where Helcim led with 17%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
In b2b payment acceptance · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named Fiserv for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
CategoryFunctionShareRankNegative rateLabelsQuadrant
B2B payment acceptanceReceivables and billing0%89 of 9175%4under 10 labels · led by Helcim at 17%
Cash applicationReceivables and billing0%60 of 830%1under 10 labels · led by HighRadius at 16%
Treasury management systemsTreasury and cash0%82 of 1240%1under 10 labels · led by Trovata at 18%

Movement

This is the first edition on this tier, so no move can be computed for Fiserv yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated Fiserv across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500000
GPT-5.4 mini00000
Gemini 3.5 Flash00000
Perplexity Sonar00000
Grok 4.1 Fast00011
Mistral Small00011
DeepSeek V4 Flash00101
Llama 4 Maverick00000
Qwen 3.7 Flash00202
Kimi K200000
GLM 4.7 FlashX00011
MiniMax M2.500000

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct1 labelNone
Paraphrase0 labelsNone
Comparative4 labels1not counted in share
Budget-constrained0 labelsNone
Scale-constrained2 labelsNone
Negative7 labelsNone
First choiceAlternativeMentionNegative14 labels in all, every segment counted; 0 of the 1 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

No positive label carried a quote.

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

“First Data is criticized for high costs, poor communication, hidden fees, and complicated cancellation procedures.” GLM 4.7 FlashX · B2B payment acceptance · negative prompt · hard negative
“First Data has many dissatisfied customers... high costs, poor communication, hidden fees” Mistral Small · B2B payment acceptance · negative prompt · hard negative
“Hidden fees, deceptive billing, complicated cancellations, and excessive charges” Grok 4.1 Fast · B2B payment acceptance · negative prompt · soft negative

Named alongside

The products named in the same answers as Fiserv, over the 14 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Fiserv was named but was not.
ProductSame answerTook the first choice insteadHead to head
FIS6 of 140Not in the top three
Worldpay6 of 140Not in the top three
Stripe5 of 142Not in the top three
BlueSnap5 of 140Not in the top three
BILL4 of 140Not in the top three
Kyriba4 of 140Not in the top three
Trovata3 of 141Not in the top three
Finastra3 of 140Not in the top three
Melio3 of 140Not in the top three
Payza3 of 140Not in the top three
A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named Fiserv. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 11 of the 14 answers that named Fiserv and are not a share of its labels.

Domains cited

checkthat.ai6
gartner.com6
g2.com4
linkedin.com4
rfp.wiki4
airwallex.com3
connectpay.com3
fintelegram.com3
listings.ratex42.com3
triple-a.io3

Thirty-nine of the thirty-nine domain citations in answers naming Fiserv came from somebody else's page.

Pages cited

Pages are listed as the models cited them.

Names read as Fiserv

What the judge wrote, as written, with how often. The vendor table decides that these count as Fiserv; a claim can dispute any of them.
First Data 2FIS / Fiserv 1Fiserv (formerly First Data) 1
Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Fiserv's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Fiserv, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at fiserv.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.