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Industries · Financial Services · Banking · Lending · Wealth · Fintech

Financial services, from a tag on the rates page to a modeled book of business.

A community bank starts by seeing which households looked at its mortgage rates last night. A national lender ends with the graph licensed into its warehouse, every brand's customers resolved to one person, and CTV bought against modeled net worth. Both use the same 240M-person graph. This page walks the rungs in between, with the products, the steps and a company at each one.

Book a free demo Start at rung one

Everything here is marketing data, modeled and non-FCRA: it finds and describes prospects, and it never decides who qualifies for credit.

  • Household, property, modeled income and net worth, card types held, investments and a credit range that is modeled, not pulled, on roughly every US adult.
  • Every phone carries a do-not-call flag and every email a validation status before a banker or a dialer sees it.
  • Intent read daily across 65K topics, so a household researching refinancing or high-yield savings shows up the week it starts.
Ad Audiences · Refinance in-market · 3 counties · 24,600 households
SegmentHomeownerMortgage on fileModeled equity: highWithin footprint
SignalsRefinance ratesHELOCVisited 2 competitor sites
ExcludedCurrent mortgage customers · 11,900
PushedMeta · 61% matchedGoogle · 54%CTV via DSP
Push to MetaMail the top 5,000Send to Salesforce
240M+US adults in the graph
120Mbusinesses, with the people in them
2Tintent signals read a day
400+attributes on a person

What the graph holds for financial services

The fields a lender or a banker actually asks for.

Four of the sixteen bundles, picked for this industry. See everything in the graph →

FinanceModeled, not a credit reportEstimated household income and net worth, a modeled credit range, card types held, investments and a CRA income tier: enough to describe a prospect, never enough to decide an application.Open this table in the data dictionary → Household & propertyAbout 250 fields on where they liveOwner or renter, dwelling type, home value, mortgage presence and amount band, years in home, and the recent-mover flag that drives every deposit campaign.Open this table in the data dictionary → PhonesChecked before you see themLine type, carrier, do-not-call status and a quality score on every number, so the call center only gets numbers it may dial.Open this table in the data dictionary → Intent & linkagesWhat they research, and how the keys joinDaily topic scores for refinancing, HELOCs, savings rates, business banking and wealth management, joined to the person and to the household.Open this table in the data dictionary →

The ladder · simplest to largest

Five rungs, from one afternoon to a warehouse contract.

Start where your team is. A credit union with one marketer lives on rungs one and two for a year and does fine. A top-50 bank starts the conversation at rung five and still wants rung one running on every brand site.

Rung 1 · Day one

A community bank, a credit union, a mortgage broker or a fintech with one marketer

See who looked at the rates page, and pull the first list.

Put the Website ID tag on the site and tomorrow morning the companies and, with the people turned on, the households that visited the rates, HELOC and business-banking pages are in the app with their fields. In the same afternoon, pull one US Consumer Data segment: homeowners in your branch footprint with a mortgage on file and a modeled equity band.

  1. Install the Website ID tag; five minutes, one script.
  2. Open the first morning's visitors; sort by the pages that matter (rates, apply, locations).
  3. Build one US Consumer Data segment on your footprint and download the records for the first mailing or the first calling list, do-not-call flags included.
Website IDUS Consumer Data
Rung 2 · First month

A regional lender, a growing fintech, or any marketing team with a paid-media budget

Run the first campaigns on the graph instead of the platform's guess.

Build the exact audience in Ad Audiences: homeowners with equity, in footprint, researching refinancing this week, and push it to Meta, Google and a DSP as hashes. Put your account holders in Smart Exclusion so you stop paying to acquire people you already have. Put the top of the segment in the mail with Direct Mail, in home within a week, matched back to applications.

  1. Build the segment in Ad Audiences; the match rate per platform shows before you spend.
  2. Load the customer file into Smart Exclusion; it expands to every email, phone and device the graph knows and excludes them everywhere, refreshed as accounts open.
  3. Mail the best households through Direct Mail with a rate offer; match the responders back by address.
Ad AudiencesSmart ExclusionDirect Mail
Rung 3 · First quarter

A bank or lender with a CRM, a call center and more than one product line

Make the CRM know what the graph knows.

Run Enrich across leads and account holders: household, property, vehicle, finance and life-stage fields appended to every record and written back to Salesforce, HubSpot or GoHighLevel through Integrations. Put the lead list and the book on Pulse so the people shopping for a HELOC, an auto refinance or a wealth advisor this week light up for the banker who owns them.

  1. Enrich the CRM with Enrich; the match rate shows first, and only matched fields count.
  2. Monitor the book with Pulse: a weekly list of account holders in market for products they do not have yet.
  3. Route alerts to bankers and branches through Integrations; score leads by product fit and intent heat.
EnrichPulseB2B Data
Rung 4 · First year

A lender or fintech with an analyst, a warehouse and a model to train

Model it: which visitors fund, which households convert, which topics predict a close.

Export your identified Website ID visitors with their 400 fields, join them to funded applications, and fit a regression or a gradient-boosted model in your warehouse or a notebook. Score new visitors daily through the API, push the top decile to Ad Audiences as a seed, and give the sales team only the scores. Scope a Custom Intent Feeds feed on your products, your rates and the three lenders you lose to; it lands in the app, by API, in your S3, or as a hashed audience.

  1. Train on visitors plus outcomes: equity band, mover flag, topic recency and page depth usually carry most of the signal.
  2. Score daily by API or let an agent do it through the MCP server; refresh the audience and the suppression list on the same schedule.
  3. Build the Custom Intent Feeds feed to a written scope: your product terms, your competitors' names, your footprint.
Custom Intent FeedsWebsite IDAd AudiencesEnrich
Rung 5 · At scale

A top-100 bank, a national lender, an insurer with a banking arm, a wealth platform

License the graph. Resolve every brand to one person. Buy CTV against the model.

Enterprise licenses the graph itself: scheduled delivery to Snowflake, BigQuery or S3, the API at volume, identity resolution across the bank, the wealth arm and the card portfolio, and the Clean Room for matching account holders to campaigns without moving PII. Audiences stream to The Trade Desk integration and other DSPs for CTV and programmatic; mail, digital and branch outcomes are measured on the same person key. A proof of concept on your own data comes first.

  1. Proof of concept: your customer file against the graph, match rate and field coverage reported before any contract.
  2. Warehouse delivery on a schedule; the person key joins the brands and the channels.
  3. Clean-room measurement of CTV, programmatic and mail; Custom Intent Feeds feeds by API into the marketing warehouse.
EnterpriseCustom Intent FeedsAd AudiencesSmart Exclusion

What financial services teams use it for

What banks, lenders and fintechs run on the graph.

HELOC and refinance campaigns

Homeowners with a mortgage on file and modeled equity, researching rates this week, suppressed against your own borrowers.

Ad AudiencesSmart ExclusionPulse

Auto refinance

Vehicle year, lien on file and purchase date from the vehicle bundle, matched to households in footprint.

US Consumer DataDirect Mail

Wealth management prospecting

Modeled net worth, investing affinity and life-stage fields, with a validated phone and a do-not-call flag for the advisor.

US Consumer DataEnrich

Small-business banking

Owners and finance leads at every business in the footprint, phones checked, with intent on lines of credit and equipment financing.

B2B DataPulseChrome extension

Card acquisition

Online-buyer, card-types-held and spend fields from the purchase bundle, as an audience and as a mail list.

Ad AudiencesDirect Mail

Who visited the rates page

Households and companies on the site last night, with their fields, pushed to the banker who covers that branch.

Website IDEnrich

Win-back of abandoned applications

Applicants who stopped halfway and are researching the same product again this week, flagged on the lead list.

PulseAd Audiences

Credit union membership drives

Employees of select employer groups through the business graph, joined to the consumer record for the household view.

B2B DataUS Consumer Data

Visitor-to-funded modeling

A regression on identified visitors and outcomes, scored daily by API, feeding seeds and suppression lists.

Website IDEnrichAd Audiences

Multi-brand identity resolution

The bank, the wealth arm and the card portfolio resolved to one person in your warehouse.

Enterprise

Marketing agencies serving lenders

Every product under the agency's brand, one sub-account per lender client.

Agency DaaS

Use it right

Marketing data, never a credit decision.

The rules that apply when the buyer is a bank or a lender. Your counsel decides; this is what the data is and is not.

  • Non-FCRA. DataMoon data is not a consumer report. Use it to find and describe prospects; never to decide eligibility for credit, insurance or employment, and never as a prescreened firm offer of credit, which needs a credit bureau.
  • Special ad categories. Credit offers on Meta and Google fall under their special ad category rules, which limit targeting options. The rep walks through what each platform accepts before you build.
  • Calling and texting. Every phone carries a do-not-call flag and a line type. TCPA consent for texts and autodialed calls is yours to obtain; the flag keeps the list honest.
  • Your own customer data. Account holder files loaded for suppression or enrichment are hashed and matched; GLBA treatment of your own data stays with you, and the clean room exists for the cases where PII must not move.
  • Opt-outs honored. People who opt out through datamoon.com are removed from every product, including the records you have already pulled on the next refresh.

Questions

Questions a bank's marketing team asks first.

Can we use this to approve or price loans?

No. The finance fields are modeled from marketing data and the credit range is a model, not a bureau pull. The graph finds and describes prospects; underwriting uses a credit report.

Does the pixel work on a bank website with a login?

Yes. The tag identifies visitors on the public pages (rates, products, locations, apply). It is not placed on authenticated pages, and it never reads what is behind the login.

How does suppression handle a bank with several brands?

Load one customer file per brand or one combined file. Each record expands to every identifier the graph knows for the person, so a customer who used a different email with the wealth arm is still excluded.

Can intent be scoped to our competitors by name?

Yes. A Custom Intent Feed is built to a written scope: your product terms, your rates pages, the lenders you lose to, your footprint. It is live within days of the scoping call.

What does an enterprise engagement look like?

A proof of concept on your own customer file first: match rate and field coverage, reported. Then a scheduled delivery of the graph to your warehouse on an annual contract, with the API and the clean room as needed.

Other industries

The same graph, a different ladder.

InsuranceReal EstateMarketing AgenciesTechnologyAll industries

Book a free demo

Bring a branch footprint and one product. We count the segment on the call.

Thirty minutes: the tag on your site, a refinance or deposit segment counted in your footprint with the match rate per platform, and a written plan for the next rung.

Book a free demoSee how it works

Thirty minutes, live on your own data. A person who knows the products and this industry runs the call. By continuing you accept the DataMoon terms.