{"id":8500,"date":"2026-07-07T14:50:09","date_gmt":"2026-07-07T14:50:09","guid":{"rendered":"https:\/\/www.spanglobalservices.com\/blog\/?p=8500"},"modified":"2026-07-07T15:00:35","modified_gmt":"2026-07-07T15:00:35","slug":"top-companies-using-fintech-software","status":"publish","type":"post","link":"https:\/\/www.spanglobalservices.com\/blog\/top-companies-using-fintech-software\/","title":{"rendered":"Top 100 Companies Using Fintech Software in 2026 (Mapped by Stack)"},"content":{"rendered":"
\n

Search “top fintech companies 2026” and you get the same article forty times. A ranked list of software development companies like Vention, ELEKS, SDK.finance, Netguru. Every one of them tells you who builds fintech software.<\/p>\n

The issue is, they do not tell you who is actually using it.<\/p>\n

That gap is the biggest challenge for anyone selling into financial technology. If you sell fraud tooling, a core banking module, a settlement rail, or a data layer, you do not care which agency builds the software. You care about which enterprise has Fiserv integrated into its issuer stack, which marketplace adopted Stripe two years ago and is now reaching its limits, and which mid-market bank is still relying on a legacy core system it is eager to replace.<\/p>\n

That is technographic data<\/a> and it is the difference between building a targeted pipeline and sending a spray of cold emails into the void.<\/p>\n

\n

This is not a vendor directory. It is a map of the fintech software install base.<\/p>\n

Below are 100 companies organized by the fintech software categories they use, along with the specific integrations powering their operations and the use cases behind them. Find the category that aligns with your product, and you have found your ICP.<\/p>\n<\/div>\n<\/div>\n

\n
\n
Table of Contents<\/span><\/div>\n

01<\/span>Why “who builds it” lists are useless for sales<\/a><\/p>\n

02<\/span>The 4 Technical Buckets (how to read this list)<\/a><\/p>\n

03<\/span>Bucket 1: Embedded Finance & Payments Infrastructure<\/a><\/p>\n

04<\/span>Bucket 2: AI Orchestration & Ops Automation<\/a><\/p>\n

05<\/span>Bucket 3: Core Banking & Infrastructure Solutions<\/a><\/p>\n

06<\/span>Bucket 4: Real-Time Settlement & Treasury Management<\/a><\/p>\n

07<\/span>The Technographic Targeting Play (4 steps)<\/a><\/p>\n

08<\/span>Where this list ends and your dataset begins<\/a><\/p>\n

09<\/span>Frequently Asked Questions<\/a><\/p>\n<\/div>\n<\/div>\n

 <\/p>\n

\n

Why “Who Builds It” Lists Are Useless for Sales<\/strong><\/h2>\n
<\/div>\n

Here is a number that should change how you think about every prospect list you own. Sales reps spend 27.3% of their time dealing with inaccurate data, according to ZoomInfo research cited across the industry. That is 546 hours per rep per year, more than 13 full working weeks, spent chasing wrong numbers and stale records.<\/p>\n

<\/p>\n\n\n\n
\n
27.3%<\/div>\n
of rep time lost to inaccurate data<\/div>\n<\/td>\n
\n
546<\/div>\n
hours per rep per year, over 13 work weeks<\/div>\n<\/td>\n
\n
22.5%<\/div>\n
annual B2B data decay, about 2.1% a month<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Now layer on the decay rate. B2B data degrades at roughly 22.5% per year, about 2.1% per month, and it compounds silently whether you touch the record or not (per Validity’s 2025 State of CRM Data Management report and multiple provider benchmarks). By December, one in four records you rely on is wrong. Wrong title, wrong email, or the company abandoned the tech stack you targeted them for eighteen months ago.<\/p>\n

That last point is the killer for technographic selling. A prospecting list built on stale install-base data does not just waste outreach. It sends you after the wrong problem.<\/strong> You pitch a Stripe migration to a company that ripped out Stripe last year. You lead with a core-banking replacement to a bank that finished its Temenos cutover in Q1. The filter looked precise. The data underneath was rotting.<\/p>\n

Generic “top fintech companies” content makes this worse, not better, because it gives you names with zero stack intelligence. A name is not an ICP. A name plus a verified integration plus a use case is an ICP. That is what the next four sections give you.<\/p>\n

<\/p>\n

\n
Key Takeaway<\/div>\n

The value is not the company name. It is the technographic profile: the software they run, the integration that proves it, and the use case that tells you what they are trying to solve. That is the layer competitors cannot copy from a Google search.<\/p>\n<\/div>\n<\/div>\n

\n
\n
Your prospect list is decaying while you read this.<\/span><\/div>\n

See what a continuously verified, install-base dataset looks like before you spend another hour chasing stale records.<\/p>\n

See a Sample Dataset<\/a><\/p>\n<\/div>\n<\/div>\n

 <\/p>\n

\n

The 4 Technical Buckets and How to Read This List<\/strong><\/h2>\n
<\/div>\n

\"4<\/p>\n

We grouped 100 companies by what kind of fintech software they run<\/em>, not by industry or size. Each bucket maps to a different buyer, a different pain, and a different pitch.<\/p>\n

\n
01<\/span>
\nEmbedded Finance & Payments Infrastructure<\/span><\/div>\n

Companies running payments and financial services<\/a> natively inside a non-financial product.<\/p>\n

Stripe<\/span>Fiserv<\/span>Marqeta<\/span>Rapyd<\/span>Highnote<\/span><\/div>\n

Sell anything that plugs into a payment flow?<\/strong> This is your bucket.<\/p>\n<\/div>\n

\n
02<\/span>
\nAI Orchestration & Ops Automation<\/span><\/div>\n

Companies using AI for fraud detection, KYC\/AML, underwriting, and automated financial decisioning.<\/p>\n

Sell risk, compliance, or decisioning tooling?<\/strong> Start here.<\/p>\n<\/div>\n

\n
03<\/span>
\nCore Banking & Infrastructure Solutions<\/span><\/div>\n

Mid-market to enterprise institutions running heavyweight core platforms.<\/p>\n

Finastra<\/span>Fiserv<\/span>FIS<\/span>Temenos<\/span>Infosys Finacle<\/span>Jack Henry<\/span><\/div>\n

Sell modernization, middleware, or a rip-and-replace core?<\/strong> These are your accounts.<\/p>\n<\/div>\n

\n
04<\/span>
\nReal-Time Settlement & Treasury Management<\/span><\/div>\n

Companies live on FedNow, RTP, or programmable settlement and treasury workflows.<\/p>\n

Sell instant-payment enablement, treasury software, or reconciliation?<\/strong> This bucket is heating up fast.<\/p>\n<\/div>\n

Each entry below follows the same technographic format: Company. Fintech integration. Use case.<\/strong> That structure is the point. It is what a real install-base list looks like.<\/p>\n

\n

A note on method: integrations below reflect publicly disclosed partnerships, press releases, and product announcements. Company-to-vendor relationships change. The full, multi-verified, continuously refreshed version of this mapping is a dataset, not a blog post, and that distinction matters more than anything else on this page.<\/p>\n<\/div>\n<\/div>\n

 <\/p>\n

\n
BUCKET 01<\/span><\/div>\n

Embedded Finance & Payments Infrastructure<\/h2>\n

The category that quietly eats everything. Embedded finance lets any platform offer banking, cards, or payments without becoming a bank. If your product touches a checkout, a payout, or a card program, every company here is a live target.<\/p>\n<\/div>\n

<\/p>\n

\n
The anchor example: How to understand the table?<\/div>\n

DoorDash partnered with Fiserv to embed formal banking services into the Dasher app through the Crimson program. The integration enabled instant post-delivery payouts, a full deposit account, and driver-focused financial services, with Starion Bank serving as the sponsor bank.<\/p>\n

Use case:<\/strong> Embedded payouts and gig-worker banking portals.<\/p>\n

This is the template: a non-financial platform, a payments infrastructure partner, and a financial product embedded directly inside the app.<\/p>\n<\/div>\n

<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
#<\/th>\nCompany<\/th>\nFintech Integration<\/th>\nUse Case<\/th>\n<\/tr>\n<\/thead>\n
1<\/td>\nDoorDash<\/td>\nFiserv (Crimson program, Starion Bank sponsor)<\/td>\nEmbedded driver banking + instant payouts<\/td>\n<\/tr>\n
2<\/td>\nUber<\/td>\nMarqeta card issuing, Branch\/partner rails<\/td>\nDriver debit, instant earnings access<\/td>\n<\/tr>\n
3<\/td>\nShopify<\/td>\nStripe (Shopify Payments), Shopify Balance<\/td>\nEmbedded merchant payments + banking<\/td>\n<\/tr>\n
4<\/td>\nLyft<\/td>\nPayfare\/partner banking, Mastercard debit<\/td>\nDriver instant pay + financial portal<\/td>\n<\/tr>\n
5<\/td>\nInstacart<\/td>\nStripe payments infrastructure<\/td>\nMarketplace checkout + shopper payouts<\/td>\n<\/tr>\n
6<\/td>\nAmazon<\/td>\nCustom + Stripe\/partner rails<\/td>\nMarketplace payments, seller disbursement<\/td>\n<\/tr>\n
7<\/td>\nToast<\/td>\nEmbedded payments + Toast Capital<\/td>\nRestaurant POS payments + merchant lending<\/td>\n<\/tr>\n
8<\/td>\nKlarna<\/td>\nProprietary BNPL + card issuing<\/td>\nPoint-of-sale installment financing<\/td>\n<\/tr>\n
9<\/td>\nAffirm<\/td>\nProprietary BNPL rails, card program<\/td>\nEmbedded checkout financing<\/td>\n<\/tr>\n
10<\/td>\nMindbody<\/td>\nEmbedded payments platform<\/td>\nWellness studio billing + payments<\/td>\n<\/tr>\n
11<\/td>\nClassPass<\/td>\nStripe subscription + payments<\/td>\nRecurring membership billing<\/td>\n<\/tr>\n
12<\/td>\nSquarespace<\/td>\nStripe \/ Square integrations<\/td>\nCreator commerce + checkout<\/td>\n<\/tr>\n
13<\/td>\nWix<\/td>\nWix Payments (Stripe\/adyen backend)<\/td>\nSMB storefront payments<\/td>\n<\/tr>\n
14<\/td>\nSubstack<\/td>\nStripe Connect<\/td>\nCreator subscription payouts<\/td>\n<\/tr>\n
15<\/td>\nPatreon<\/td>\nStripe Connect payouts<\/td>\nCreator recurring revenue<\/td>\n<\/tr>\n
16<\/td>\nDeliveroo<\/td>\nAdyen payments<\/td>\nMarketplace order settlement<\/td>\n<\/tr>\n
17<\/td>\nGrab<\/td>\nProprietary GrabPay + partner rails<\/td>\nSuper-app embedded wallet<\/td>\n<\/tr>\n
18<\/td>\nGojek<\/td>\nEmbedded GoPay wallet<\/td>\nSuper-app payments + financial services<\/td>\n<\/tr>\n
19<\/td>\nAirbnb<\/td>\nAdyen \/ Stripe payout rails<\/td>\nHost disbursement + guest checkout<\/td>\n<\/tr>\n
20<\/td>\nEtsy<\/td>\nAdyen (Etsy Payments)<\/td>\nMarketplace seller settlement<\/td>\n<\/tr>\n
21<\/td>\nWayfair<\/td>\nEnterprise payments + BNPL partners<\/td>\nCheckout financing<\/td>\n<\/tr>\n
22<\/td>\nChewy<\/td>\nPayments + Autoship billing<\/td>\nSubscription commerce<\/td>\n<\/tr>\n
23<\/td>\nZocdoc<\/td>\nStripe healthcare payments<\/td>\nPatient billing embed<\/td>\n<\/tr>\n
24<\/td>\nFaire<\/td>\nEmbedded net-terms financing<\/td>\nB2B marketplace credit<\/td>\n<\/tr>\n
25<\/td>\nFlexport<\/td>\nEmbedded trade finance<\/td>\nFreight financing + payments<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

<\/p>\n

\n

So what?<\/strong> Every company in this bucket already crossed the “we do payments now” threshold. That means their next problem is optimization: fraud on the new volume, reconciliation across rails, or a second financial product. If you sell into the layer above<\/em> the payment rail, these are companies with budget already unlocked and a live pain waiting.<\/p>\n<\/div>\n

 <\/p>\n

\n

<\/p>\n

\n
BUCKET 02<\/span><\/div>\n

AI Orchestration & Ops Automation<\/h2>\n

Fraud detection, KYC\/AML, underwriting, and automated decisioning are where AI has moved beyond experimentation and become a core operational function. This is where AI stopped being a slide in a strategy deck and became a budget line item.<\/p>\n<\/div>\n

The buyers here are Heads of Risk, Chief Compliance Officers, and VPs of Fraud, teams overwhelmed by false positives, manual review queues, and the growing complexity of financial crime operations.<\/p>\n

<\/p>\n

\n

Position AI as an operational partner, not just another software tool.<\/strong> Its effectiveness depends entirely on the quality and relevance of the data powering it. An AI fraud engine running on outdated identity data is like a sports car running on contaminated fuel: powerful technology, but limited by what feeds it.<\/p>\n<\/div>\n

That framing creates the entry point into every account listed below.<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
#<\/th>\nCompany<\/th>\nFintech Integration<\/th>\nUse Case<\/th>\n<\/tr>\n<\/thead>\n
26<\/td>\nStripe<\/td>\nRadar (proprietary ML fraud)<\/td>\nReal-time transaction fraud scoring<\/td>\n<\/tr>\n
27<\/td>\nPayPal<\/td>\nProprietary AI risk + Simility<\/td>\nFraud detection, dispute automation<\/td>\n<\/tr>\n
28<\/td>\nBlock (Square\/Cash App)<\/td>\nIn-house ML risk models<\/td>\nFraud + AML monitoring<\/td>\n<\/tr>\n
29<\/td>\nPlaid<\/td>\nSignal + Identity Verification<\/td>\nAccount risk, KYC orchestration<\/td>\n<\/tr>\n
30<\/td>\nCoinbase<\/td>\nIn-house + chain-analytics partners<\/td>\nTransaction monitoring, AML<\/td>\n<\/tr>\n
31<\/td>\nRobinhood<\/td>\nML surveillance + KYC vendors<\/td>\nTrade surveillance, onboarding<\/td>\n<\/tr>\n
32<\/td>\nChime<\/td>\nFraud ML + partner KYC<\/td>\nAccount-opening fraud prevention<\/td>\n<\/tr>\n
33<\/td>\nSoFi<\/td>\nAI underwriting models<\/td>\nAutomated lending decisions<\/td>\n<\/tr>\n
34<\/td>\nUpstart<\/td>\nProprietary AI underwriting<\/td>\nML-based loan approval<\/td>\n<\/tr>\n
35<\/td>\nNubank<\/td>\nIn-house AI credit models<\/td>\nCredit decisioning at scale<\/td>\n<\/tr>\n
36<\/td>\nRevolut<\/td>\nAI fraud + transaction monitoring<\/td>\nReal-time fraud interdiction<\/td>\n<\/tr>\n
37<\/td>\nWise<\/td>\nML risk + compliance automation<\/td>\nCross-border AML screening<\/td>\n<\/tr>\n
38<\/td>\nMarqeta<\/td>\nRisk controls + tokenization<\/td>\nCard fraud, spend controls<\/td>\n<\/tr>\n
39<\/td>\nRamp<\/td>\nAI spend + receipt automation<\/td>\nAutomated expense + fraud flags<\/td>\n<\/tr>\n
40<\/td>\nBrex<\/td>\nML underwriting + spend controls<\/td>\nCorporate card risk decisioning<\/td>\n<\/tr>\n
41<\/td>\nMercury<\/td>\nRisk engine + KYB automation<\/td>\nBusiness onboarding compliance<\/td>\n<\/tr>\n
42<\/td>\nKlarna<\/td>\nAI risk + BNPL underwriting<\/td>\nReal-time credit decisioning<\/td>\n<\/tr>\n
43<\/td>\nAffirm<\/td>\nML underwriting models<\/td>\nPoint-of-sale credit scoring<\/td>\n<\/tr>\n
44<\/td>\nKabbage\/Amex<\/td>\nAI SMB underwriting<\/td>\nAutomated small-business lending<\/td>\n<\/tr>\n
45<\/td>\nDeserve<\/td>\nAI credit + card decisioning<\/td>\nML card issuance<\/td>\n<\/tr>\n
46<\/td>\nAlloy<\/td>\nIdentity decisioning platform<\/td>\nKYC\/KYB orchestration layer<\/td>\n<\/tr>\n
47<\/td>\nSocure<\/td>\nAI identity verification<\/td>\nFraud + identity scoring<\/td>\n<\/tr>\n
48<\/td>\nPersona<\/td>\nIdentity verification workflows<\/td>\nOnboarding KYC automation<\/td>\n<\/tr>\n
49<\/td>\nFeedzai<\/td>\nAI financial crime platform<\/td>\nEnterprise fraud + AML<\/td>\n<\/tr>\n
50<\/td>\nSardine<\/td>\nFraud + compliance ML<\/td>\nReal-time behavioral risk<\/td>\n<\/tr>\n
51<\/td>\nHummingbird<\/td>\nAML case management<\/td>\nCompliance investigation automation<\/td>\n<\/tr>\n
52<\/td>\nUnit21<\/td>\nRisk + AML orchestration<\/td>\nAutomated case + rules engine<\/td>\n<\/tr>\n
53<\/td>\nComplyAdvantage<\/td>\nAI AML screening<\/td>\nSanctions + transaction monitoring<\/td>\n<\/tr>\n
54<\/td>\nTaktile<\/td>\nDecision automation platform<\/td>\nCredit + risk decision workflows<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

<\/p>\n

\n

So what?<\/strong> These companies do not need to be convinced that AI matters. They need the underlying input layer to be accurate, reliable, and clean. The pitch that resonates is not “add AI.” It is: “Your AI is only as accurate as the identity and firmographic data powering it.” That reframes a crowded AI market into a data infrastructure problem, and that is where the real leverage lies.<\/p>\n<\/div>\n<\/div>\n

 <\/p>\n

\n

<\/p>\n

\n
BUCKET 03<\/span><\/div>\n

Core Banking & Infrastructure Solutions<\/h2>\n

The unglamorous foundation. Mid-market and enterprise institutions running heavyweight core platforms. Long sales cycles, deep switching costs, and enormous contract value. The buyer is a CIO, a Head of Payments, or a core-transformation program lead who has been fighting a legacy system for years.<\/p>\n<\/div>\n

<\/p>\n

\n
The 2026 Signal to Watch<\/div>\n

Enterprises are consolidating fintech vendors aggressively. The average enterprise runs six to ten payment vendors<\/strong>, each with its own integration and maintenance burden (per Modern Treasury’s 2026 predictions). Integration debt is the pain. If your product reduces vendor sprawl or modernizes the core, every institution here is carrying that weight right now.<\/p>\n<\/div>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
#<\/th>\nCompany<\/th>\nFintech Integration<\/th>\nUse Case<\/th>\n<\/tr>\n<\/thead>\n
55<\/td>\nWells Fargo<\/td>\nFIS + internal core<\/td>\nCore deposit + payment processing<\/td>\n<\/tr>\n
56<\/td>\nCitizens Bank<\/td>\nRTP + treasury core<\/td>\nCommercial banking infrastructure<\/td>\n<\/tr>\n
57<\/td>\nTruist<\/td>\nCore modernization program<\/td>\nPost-merger core consolidation<\/td>\n<\/tr>\n
58<\/td>\nFifth Third<\/td>\nFiserv + modernization<\/td>\nRetail + commercial core<\/td>\n<\/tr>\n
59<\/td>\nRegions Bank<\/td>\nFIS core platform<\/td>\nDeposit + lending core<\/td>\n<\/tr>\n
60<\/td>\nKeyBank<\/td>\nEnterprise core + payments<\/td>\nCommercial banking stack<\/td>\n<\/tr>\n
61<\/td>\nHuntington<\/td>\nCore + digital banking layer<\/td>\nRetail banking infrastructure<\/td>\n<\/tr>\n
62<\/td>\nM&T Bank<\/td>\nCore banking modernization<\/td>\nDeposit processing<\/td>\n<\/tr>\n
63<\/td>\nVaro Bank<\/td>\nTemenos-class core (neobank charter)<\/td>\nFull-stack digital bank core<\/td>\n<\/tr>\n
64<\/td>\nGreen Dot<\/td>\nBaaS core infrastructure<\/td>\nBanking-as-a-service platform<\/td>\n<\/tr>\n
65<\/td>\nBancorp<\/td>\nBaaS + sponsor-bank core<\/td>\nEmbedded finance sponsor rails<\/td>\n<\/tr>\n
66<\/td>\nCross River<\/td>\nCore + API banking platform<\/td>\nFintech sponsor-bank infrastructure<\/td>\n<\/tr>\n
67<\/td>\nColumn<\/td>\nDeveloper-first core banking<\/td>\nAPI-native bank infrastructure<\/td>\n<\/tr>\n
68<\/td>\nCoastal Community Bank<\/td>\nBaaS core (fintech sponsor)<\/td>\nSponsor-bank programs<\/td>\n<\/tr>\n
69<\/td>\nPathward<\/td>\nBaaS + core platform<\/td>\nEmbedded banking sponsor<\/td>\n<\/tr>\n
70<\/td>\nAxos Bank<\/td>\nDigital-first core<\/td>\nOnline banking infrastructure<\/td>\n<\/tr>\n
71<\/td>\nLive Oak Bank<\/td>\nFinxact\/Jack Henry-class core<\/td>\nSBA lending + digital core<\/td>\n<\/tr>\n
72<\/td>\nCustomers Bank<\/td>\nCore + real-time platform<\/td>\nCommercial + fintech banking<\/td>\n<\/tr>\n
73<\/td>\nGrasshopper Bank<\/td>\nModern API core<\/td>\nDigital SMB banking<\/td>\n<\/tr>\n
74<\/td>\nMercury (BaaS partners)<\/td>\nSponsor-bank core stack<\/td>\nBusiness banking infrastructure<\/td>\n<\/tr>\n
75<\/td>\nDave<\/td>\nCore + partner-bank rails<\/td>\nNeobank infrastructure<\/td>\n<\/tr>\n
76<\/td>\nMoneyLion<\/td>\nCore + embedded finance stack<\/td>\nConsumer financial platform<\/td>\n<\/tr>\n
77<\/td>\nCurrent<\/td>\nPartner-bank core<\/td>\nDigital banking infrastructure<\/td>\n<\/tr>\n
78<\/td>\nBluevine<\/td>\nCore + SMB lending platform<\/td>\nBusiness banking + credit<\/td>\n<\/tr>\n
79<\/td>\nNovo<\/td>\nPartner-bank core<\/td>\nSMB digital banking<\/td>\n<\/tr>\n
80<\/td>\nRelay<\/td>\nCore banking + multi-account<\/td>\nSMB financial operations<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

<\/p>\n

\n

So what?<\/strong> Core transformation is a multi-year, multi-million-dollar commitment. The reps who win in this space do not sell features; they enter conversations already knowing which core system the institution runs, when the contract is up for renewal, and what integration debt is slowing down the roadmap. That level of intelligence is technographic data, and it is exactly what a raw name list cannot provide.<\/p>\n<\/div>\n<\/div>\n

 <\/p>\n

\n

<\/p>\n

\n
BUCKET 04<\/span><\/div>\n

Real-Time Settlement & Treasury Management<\/h2>\n

The fastest-moving bucket in 2026. Companies live on FedNow, RTP, or programmable settlement and modern treasury. Adoption just crossed from experiment to standard.<\/p>\n<\/div>\n

<\/p>\n\n\n\n
\n
1,500<\/div>\n
FIs on FedNow, late 2025<\/div>\n<\/td>\n
\n
58%<\/div>\n
of instant-pay banks run both RTP and FedNow<\/div>\n<\/td>\n
\n
40%+<\/div>\n
of $100M+ firms already use RTP<\/div>\n<\/td>\n
\n
~70%<\/div>\n
expect to adopt within two years<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Roughly 1,500 financial institutions are on FedNow as of late 2025, and 58% of U.S. banks that enable instant payments now run both RTP and FedNow (per Softjourn and Federal Reserve data). More than 40% of companies over $100M in revenue already use RTP, and nearly 70% of businesses expect to adopt instant payments within two years (U.S. Bank study via Dwolla).<\/p>\n

Translation: the buyers here are moving now, and the ones who have not moved are feeling the pressure. This is the bucket where “why now” writes itself.<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
#<\/th>\nCompany<\/th>\nFintech Integration<\/th>\nUse Case<\/th>\n<\/tr>\n<\/thead>\n
81<\/td>\nJPMorganChase<\/td>\nRTP (founding), FedNow, in-house treasury<\/td>\nReal-time corporate settlement<\/td>\n<\/tr>\n
82<\/td>\nCitizens Bank<\/td>\nRTP (since 2019), treasury platform<\/td>\nCommercial instant payments<\/td>\n<\/tr>\n
83<\/td>\nUS Bank<\/td>\nRTP + FedNow enablement<\/td>\nCorporate instant disbursement<\/td>\n<\/tr>\n
84<\/td>\nPNC<\/td>\nRTP + treasury management<\/td>\nCommercial real-time payments<\/td>\n<\/tr>\n
85<\/td>\nBNY<\/td>\nNetwork-level fraud + real-time rails<\/td>\nInstitutional settlement<\/td>\n<\/tr>\n
86<\/td>\nWealthfront<\/td>\nRTP + FedNow withdrawals<\/td>\nInstant account transfers<\/td>\n<\/tr>\n
87<\/td>\nDwolla<\/td>\nSingle API to RTP + FedNow<\/td>\nEmbedded pay-by-bank for enterprises<\/td>\n<\/tr>\n
88<\/td>\nModern Treasury<\/td>\nPayment ops + programmable settlement<\/td>\nTreasury + money-movement automation<\/td>\n<\/tr>\n
89<\/td>\nFinzly<\/td>\nISO 20022-native, all-rails platform<\/td>\nUnified FedNow\/RTP\/ACH\/Fedwire<\/td>\n<\/tr>\n
90<\/td>\nVolante Technologies<\/td>\nPayments hub + ISO 20022<\/td>\nReal-time payment processing<\/td>\n<\/tr>\n
91<\/td>\nMetropolitan Commercial Bank<\/td>\nACH cloud migration (Finzly)<\/td>\nPayments modernization<\/td>\n<\/tr>\n
92<\/td>\nJack Henry<\/td>\nFedNow + RTP enablement<\/td>\nCommunity-bank instant payments<\/td>\n<\/tr>\n
93<\/td>\nACI Worldwide<\/td>\nReal-time payments platform<\/td>\nEnterprise + bank settlement<\/td>\n<\/tr>\n
94<\/td>\nTemenos<\/td>\nPayments hub + core<\/td>\nBank real-time processing<\/td>\n<\/tr>\n
95<\/td>\nTrovata<\/td>\nAI treasury + cash management<\/td>\nAutomated cash forecasting<\/td>\n<\/tr>\n
96<\/td>\nHighRadius<\/td>\nAI treasury + O2C automation<\/td>\nReceivables + reconciliation<\/td>\n<\/tr>\n
97<\/td>\nTesorio<\/td>\nCash-flow + treasury platform<\/td>\nWorking-capital automation<\/td>\n<\/tr>\n
98<\/td>\nCircle<\/td>\nUSDC programmable settlement<\/td>\nStablecoin treasury + cross-border<\/td>\n<\/tr>\n
99<\/td>\nFireblocks<\/td>\nOn-chain settlement infrastructure<\/td>\nDigital-asset treasury + custody<\/td>\n<\/tr>\n
100<\/td>\nStripe (Treasury)<\/td>\nProgrammable treasury + settlement<\/td>\nEmbedded treasury for platforms<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

<\/p>\n

\n

So what?<\/strong> Instant-payment adoption is the clearest “why now” trigger in fintech right now. A company that just went live on FedNow has a fresh reconciliation problem, a new fraud surface, and a treasury workflow that needs rebuilding. Timing outreach to that live event is worth more than any amount of persona guessing. But you can only time it if your data flags the change the moment it happens.<\/p>\n<\/div>\n<\/div>\n

<\/p>\n

\n
\n
You have seen the map. Now get the territory.<\/div>\n

These 100 companies are a preview. The full dataset covers thousands, mapped to verified decision-makers and refreshed continuously.<\/p>\n

Filter by software category, revenue band, geography, and buying signal to reach your exact ICP at the right moment.<\/p>\n

Get the Full Dataset<\/a>Talk to Our Data Team \u2192<\/p>\n<\/div>\n<\/div>\n

 <\/p>\n

\n

The Technographic Targeting Play (4 Steps)<\/h2>\n
<\/div>\n

A list is static. A play is repeatable. The goal is not just to identify companies, it is to turn install-base intelligence into a predictable pipeline. Here is the four-step framework to put that intelligence into action.<\/p>\n

\n
01<\/span>
\nStep 1: Match your product to the right bucket<\/span><\/div>\n

Do not target “fintech companies” as a broad category. Target the specific fintech segment where your product solves a clear operational challenge.<\/p>\n

\n
Fraud tooling<\/span>\u2192<\/span>target companies managing fraud and risk workflows<\/span><\/div>\n
Core middleware<\/span>\u2192<\/span>target companies undergoing core modernization<\/span><\/div>\n
Settlement enablement<\/span>\u2192<\/span>target companies scaling payment infrastructure<\/span><\/div>\n<\/div>\n

Precision beats volume every time. The closer the match between your product and the technology gap, the stronger the buying signal.<\/p>\n<\/div>\n

\n
02<\/span>
\nStep 2: Filter by the integration, not the industry<\/span><\/div>\n

The strongest signal is not “companies in fintech.” It is companies running a specific technology that have a gap your product can solve.<\/p>\n

For example, if you sell sales engagement software, a company running Salesforce without a sequencing platform represents a clear opportunity. The same logic applies across fintech categories. The technology stack reveals the opportunity. The integration points reveal where the gap exists.<\/p>\n<\/div>\n

\n
03<\/span>
\nStep 3: Time outreach around stack events<\/span><\/div>\n

Technology changes create buying windows. A new FedNow Service rollout, an embedded payments launch, or a core transformation announcement creates a “why now” moment.<\/p>\n

These signals are time-sensitive and often lose relevance within weeks. Reaching a company shortly after it announces a major technology shift is significantly more effective than approaching it a year later. This only works when your data is continuously updated rather than sitting as a static list.<\/p>\n<\/div>\n

\n
04<\/span>
\nStep 4: Enrich before you engage<\/span><\/div>\n

With B2B data decaying at roughly 22.5% annually, any record that has not been verified recently carries increasing risk. Re-verify data at the point of outreach rather than relying on periodic cleanup.<\/p>\n

The teams that treat data as a continuously maintained system, rather than a one-time purchase, build stronger pipelines and make better targeting decisions. Everyone else is running outbound on assumptions.<\/p>\n<\/div>\n

<\/p>\n

\n
The Framework in One Line<\/div>\n

Match the bucket. Filter by the install. Time the trigger. Enrich continuously.<\/p>\n<\/div>\n

That is technographic selling. It is not more work than spray-and-pray outreach, it is more efficient, because it eliminates wasted effort caused by poor-fit accounts and outdated data. In fact, sales reps spend an estimated 27% of their time dealing with poor-quality data and related inefficiencies, time that can be redirected toward higher-intent opportunities when the right intelligence is in place.<\/p>\n<\/div>\n

 <\/p>\n

\n

Where This List Ends and Your Dataset Begins<\/h2>\n
<\/div>\n

One hundred companies are a starting map. They provide direction, but they are not the full territory.<\/p>\n

The moment you begin executing the play above, the limitations of a blog post become clear. A static table cannot:<\/p>\n

<\/p>\n

\n
\u2717<\/span>Filter by revenue band, geography, employee count, or the exact software version a company runs<\/span><\/div>\n
\u2717<\/span>Provide direct contact details for decision-makers, such as Heads of Risk, CIOs, or VPs of Payments<\/span><\/div>\n
\u2717<\/span>Alert you when a company enters a major technology transition, like a core migration or a new fintech partnership<\/span><\/div>\n
\u2717<\/span>Keep every data point accurate as company stacks, vendor relationships, and buying teams evolve<\/span><\/div>\n<\/div>\n

That is the difference between content and infrastructure. This article provides the market view about the companies, categories, and fintech stacks shaping the landscape. To fully access and act on this intelligence, you need a maintained dataset that includes:<\/p>\n

<\/p>\n

\n
\u2713<\/span>Verified install-base data across companies and fintech categories<\/span><\/div>\n
\u2713<\/span>The specific software, platforms, and integrations powering each business<\/span><\/div>\n
\u2713<\/span>Decision-maker contacts mapped to each account<\/span><\/div>\n
\u2713<\/span>Filters by industry, revenue band, geography, employee count, and technology stack<\/span><\/div>\n
\u2713<\/span>Real-time updates on technology changes, vendor relationships, and buying signals<\/span><\/div>\n<\/div>\n

The play requires infrastructure: a continuously refreshed, multi-verified install-base dataset mapped to real decision-makers and built to identify your exact ICP.<\/p>\n

<\/p>\n

\n

That is what Span Global Services builds<\/p>\n

Not a list. An intelligence layer.<\/p>\n<\/div>\n<\/div>\n

 
\n <\/p>\n

\n

Frequently Asked Questions<\/h2>\n
<\/div>\n
\n
Q<\/span>
\nWhich companies use fintech software in 2026?<\/span><\/div>\n

Thousands, and the useful way to map them is by category, not name. The four buckets: embedded finance and payments (DoorDash on Fiserv, Shopify on Stripe), AI orchestration (Stripe Radar, Feedzai, Alloy), core banking (Wells Fargo, Cross River, Column), and real-time settlement (JPMorganChase on RTP, Circle). The category tells you what problem each company is solving, which is what matters for targeting.<\/p>\n<\/div>\n

\n
Q<\/span>
\nWhat is technographic data and why does it matter for fintech sales?<\/span><\/div>\n

It maps the technology stack a company runs: which core platform, which payment rail, which fraud engine. It is one of the highest-signal inputs for ICP qualification because it reveals gaps and replacement windows. It turns “companies in fintech” into “companies running X that lack Y,” a far more precise target than any firmographic filter.<\/p>\n<\/div>\n

\n
Q<\/span>
\nHow accurate is this list of companies and their fintech stacks?<\/span><\/div>\n

The integrations reflect publicly disclosed partnerships and announcements at the time of writing. Vendor relationships change and B2B data decays at roughly 22.5% per year, so any static list drifts over time. That is exactly why a continuously verified, multi-source dataset beats a static web page you cannot act on today.<\/p>\n<\/div>\n

\n
Q<\/span>
\nHow often does B2B install-base data need to be updated?<\/span><\/div>\n

Continuously. Account data decays at about 2.1% per month, and reps already lose roughly 27% of their time to inaccurate data. Any record untouched for 90 days is a candidate for re-verification. Top teams treat data as a live feed with enrichment at the point of entry, not a one-time purchase that rots in the CRM.<\/p>\n<\/div>\n

\n
Q<\/span>
\nCan I get contact data for the decision-makers at these companies?<\/span><\/div>\n

Yes. This blog gives you company names and stack insights. The full dataset connects them to verified decision-makers, Heads of Risk, CIOs, VPs of Payments, with direct contact details, filterable by software category, revenue, geography, and buying signals. That is the layer the article does not provide and the dataset is built to deliver.<\/p>\n<\/div>\n

\n
Q<\/span>
\nWhat is the difference between a fintech vendor list and an install-base list?<\/span><\/div>\n

A vendor list ranks the companies that build fintech software. An install-base list maps the companies that run it. Hiring a developer? You want a vendor list. Selling into fintech to find your ICP? You want an install-base list. Almost every “top fintech companies” article is the former. This one is the latter.<\/p>\n<\/div>\n<\/div>\n

 
\n <\/p>\n

\n
\n

<\/p>\n

<\/div>\n
Need a Custom Install-Base List for Fintech Software?<\/div>\n

Do not stop at these 100 companies.<\/h2>\n

Chasing outdated data wastes up to 27% of a sales team’s pipeline velocity. If you are targeting enterprises running specialized fintech stacks, the full dataset picks up where this list ends.<\/p>\n

Get access to thousands of multi-verified global companies mapped by custom geographic segments, accurate tech-usage adoption insights, and direct contact data. Filter by software category, revenue band, region, and buying signal. Reach the right stack, at the right moment, with data refreshed continuously instead of scraped once.<\/p>\n

<\/p>\n

Download the Complete Fintech Dataset<\/a><\/div>\n<\/div>\n<\/div>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Search “top fintech companies 2026” and you get the same article forty times. 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Discover enterprise adoption trends, leading fintech platforms, and key buyer insights.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.spanglobalservices.com\/blog\/top-companies-using-fintech-software\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Top 100 Companies Using Fintech Software in 2026\" \/>\n<meta property=\"og:description\" content=\"Explore the top 100 companies using fintech software in 2026, mapped by technology stack. 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