Clean identity before every campaign.
Lenders, credit unions and finance marketers use DarkMath to remove duplicate records, unify fragmented relationships and aim campaigns at the right households.
What breaks today
One borrower, five records
Accounts opened across products and channels never roll up to one relationship, so risk and value both hide.
Wasted mail
Duplicates and stale addresses mean paying twice to reach the same household, or not reaching it at all.
Offers to the wrong people
Campaigns reach existing customers as prospects, or miss the households most likely to respond.
How DarkMath helps
Three products, used in the order your question needs them.
| Product | What it does for you |
|---|---|
| DarkMatch | Deduplicates and households your customer base before every campaign, with updated addresses. |
| DarkAgent | Sizes audiences with contact coverage, applies suppressions and prepares campaign orders for review. |
| DarkMirror | Extends prospect reach for marketing, with semantic attributes never used for eligibility. |
Regulated by design
Lending marketing carries rules that general-purpose tools don’t know. DarkMath works within them:
- Credit-based campaigns follow prescreen rules, including a firm offer of credit, and honor opt-outs and suppression lists
- Protected characteristics are never used to select or exclude households
- Semantic and inferred attributes support marketing reach only, never credit decisions
Questions teams bring us
- How do lenders remove duplicate customer records before a direct mail campaign?
- How do we unify one borrower across products and channels?
- How many of our prospects can we reach by email?
Lending and finance questions
Can DarkMath be used to make credit decisions?
No. DarkMath supports identity resolution and marketing. Inferred attributes are never used for eligibility, and credit-based marketing runs through compliant prescreen processes.
Can we test without sending personal data?
Yes. Tests can run on hashed values, and vector-only ingestion keeps raw records inside your environment.
Start with your own records.
Send a sample and get a standard report: duplicates, households, match rate and what we can add.