B2B Data Providers for Finance Contacts Compared: Apollo, AI Ark, ZoomInfo, Cognism, Lusha

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Apollo and AI Ark tested first-hand on the same searches; ZoomInfo, Cognism and Lusha from their public pricing pages. Prices checked 2 November 2026.

By Moe Alhosni · Published 2 November 2026 · 9 min read

Short answer: For finance contacts, Apollo is the one we would start with on price and coverage, and AI Ark is the one we use when we need cleaner industry targeting. We have run both ourselves. ZoomInfo and Cognism publish no prices, and Lusha publishes only its free plan, so for those three this post reports their public pricing pages and nothing more. Whichever you pick, test it on 50 people at finance firms you already know before you pay for a year.

I run a cold email agency that books meetings with banks, fintechs, insurers and asset managers. Data is the first thing that breaks a finance campaign, so I have opinions. Where they come from matters, so every section says whether it is first-hand or taken from the vendor's public pages. Prices were read on the vendors' own pricing pages on 2 November 2026. They change often. Check them again before you buy.

What we have used and what we have not

We have no affiliate deal with any of them, and none of them has paid us.

Prices, as published on 2 November 2026

Two of the five will not tell you a price until you take a sales call. That is normal for enterprise data, and it is not a reason to rule them out. It does mean you should go into that call with your own test plan, set out below, rather than theirs.

Apollo: what we saw first-hand

Apollo is the broadest of the two we use. When we rebuilt the same three searches in Apollo that we had built in AI Ark, with the same titles, headcount, revenue floor, locations and a 300-company exclusion list, Apollo returned more people every time. On a Europe-wide search it found 27,585 against AI Ark's 23,488. On a smaller regional search it found 1,129 against 727. Those searches were for hotel and travel marketing buyers, not finance, so treat the gap as a sign of how the two tools differ, not as a finance benchmark.

The reason for the gap is how industries are tagged. Apollo gives each company one primary industry. AI Ark tags a company with several. Neither is wrong, but they produce different lists, and in finance that matters: a payments company might sit under financial services, software or banking depending on who tagged it.

Three Apollo traps cost us time:

• Excluding companies by domain silently fails. The domain exclusion parameter is ignored and you get the unfiltered count. Apollo excludes companies only by its own organisation ID, so we had to look up each domain's ID first. If you keep a do-not-contact list of existing clients and competitors, check that it actually applied.
• Location spellings fail quietly. "British Virgin Islands" returned 3,643 contacts. "Virgin Islands (British)" returned 12. Same place. A misspelt location does not error; it just shrinks the list. Count-test every location you add, one at a time.
• Keyword tags pull in vendors. Searching on a keyword such as "banking" returns the people who sell to banks as well as the banks. For finance, use industry filters and then exclude the obvious vendor categories.

On the API, per_page above 25 returned an error for us, so large pulls have to be chunked.

AI Ark: what we saw first-hand

AI Ark's industry list is a real taxonomy, so it is cleaner than keyword searches once you know the exact names. Two things to know before you start:

• Industry names are not checked. If you type an industry that is not in their list, the search does not warn you. "data analytics" returned nothing; "analytics" worked. Type a term and read the dropdown before trusting it.
• There is no verified-email filter. AI Ark verifies at export, not at search time, so the count you see is not the count you can send to. Pull a small batch, bounce-test it, then pull the rest.

Two things we liked. The whole search can be pasted in and out as JSON, which makes it easy to copy a search exactly. And title matching has three modes, so you can control how loosely "head of treasury" matches. Use the word-match mode and add exclusions: a plain "owner" also catches "product owner", and "president" catches "vice president".

ZoomInfo, Cognism and Lusha: public information only

We have not used these three, so we will not tell you how good their finance data is. Here is what their own pricing pages say.

• ZoomInfo sells packages built on credits and asks you to contact sales for a price.
• Cognism sells Standard and Pro packages. Its page defines one credit as one reveal, enrich or export.
• Lusha has a free plan with 40 credits a month and says unused credits roll over on monthly plans.

If a review site or a salesperson tells you one of them is "best for finance", ask what it was tested on. Then run the test below yourself.

How to test any provider on finance contacts

Finance is harder than most sectors for data vendors. Titles are inconsistent (a "director" at a bank can be junior), people move between firms often, and large institutions block a lot of mail at the gateway. A general accuracy claim tells you little. This test takes an afternoon:

1. List 50 people you can check. Pick named people at finance firms you already know: current clients, past prospects, people you have met. You need to know where they work today.
2. Look them up in each trial. Count how many each tool finds, how many are at the right firm, and how many have the right title.
3. Count job changes. In an October 2026 audit of two of our B2B campaigns, roughly one lead in five had already left the company on the list. Ask each vendor how often it refreshes job changes, then check it on your 50.
4. Bounce-test before you send. Verify the emails with a separate tool and send to a small batch first.
5. Check exclusions work. Load your client and competitor list and confirm the count drops by the right amount.

Whichever tool wins, the list still has to match a written definition of who counts. Our finance lead generation guide covers that, and whether cold email to finance firms is legal covers the rules.

What we would choose

For a small team starting in finance, Apollo's published entry price and its coverage make it the sensible first trial. Add AI Ark if Apollo's industry tagging keeps giving you the wrong companies. Take ZoomInfo or Cognism calls if your team wants a sales-led contract, and hold them to the same 50-person test. I am biased: we sell meetings, not data, so I care more about whether the list books calls than about headline database size. If you would rather not run any of this yourself, we charge $200–$500 per qualified meeting that takes place, and the data is our cost, not yours.

Frequently asked questions

Which B2B data provider is best for financial services?

There is no single answer we can stand behind for all five. Of the two we have used, Apollo found more people on the same searches, and AI Ark gave us cleaner industry targeting. We have not tested ZoomInfo, Cognism or Lusha.

Is Apollo accurate enough for banks and insurers?

We have no finance-only bounce figure to give you, so test it. Two things we know: titles at large institutions need exclusions, and company exclusions must use Apollo's organisation IDs, not domains.

Why do two providers return different counts for the same search?

Mostly industry tagging. Apollo tags each company with one primary industry; AI Ark tags several. Location spellings and title-matching rules also change counts.

Do I need phone numbers for finance outreach?

Not for cold email. If your process includes calls, phone coverage is where providers differ most, and it is worth testing on your own 50 names.

Before you sign with anyone

If you are buying data, get a trial and run the 50-person test before an annual contract. If you are buying meetings, get three things in writing:

1. The qualified-meeting definition: titles, company size, budget authority, held versus booked.
2. No-show terms: replaced, credited or charged.
3. What a zero-meeting month costs, and whether data costs are included.

The clause-by-clause version of the first is what counts as a qualified meeting. To see what a meeting is worth to you, use the financial services cost per meeting calculator.

About the author: Moe Alhosni, founder of ROI Maximizer. Before that he spent 4 years and 7 months at Moody's in London (October 2019 to April 2024), most recently as Assistant Director, Sales, working with banks, asset managers and private equity firms. He runs the campaigns behind the first-hand data on this site. LinkedIn

After Moody's he was Sales Director at Coinshift, a Web3 treasury management company (2024). Before Moody's he worked at BLOM Bank France in London on settlements and customer service, including AML/KYC record updates. Full career

Keep reading

• Instantly vs PlusVibe (and Smartlead): Cold Email Platforms Compared From Our Campaigns — What we saw sending on Instantly and PlusVibe, aggregate stats from our own workspaces, current published prices, and how to move between them safely.
• Finance Lead Generation Questions, Answered: 16 Things Buyers Ask — Short answers to what financial services firms ask about lead generation: legality, cost, pay per meeting, agencies and timelines, each linked to the full guide.
• Open banking and BaaS lead generation: sponsor banks, fintechs and data buyers — Lead generation for open banking and banking-as-a-service firms: the four campaigns, who decides at sponsor banks, and the rules buyers follow.

Or run the free GTM teardown on your own site for the ICP, cold email and numbers specific to your company.

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