Three list types cover most of what a fraud operation needs to track about repeat customers — good and bad.
The three lists
- Hot lists — consumers who've initiated a chargeback on a previous order. Excellent at stopping repeat fraud, and fairly good at catching some forms of identity morphing.
- Warm lists — customers with a history of returns, non-payment, or dissatisfaction. Not necessarily fraud, but a real pattern worth tracking.
- Positive lists — repeat customers with a clean transaction history. An excellent way to cut down how many orders you have to send out to external fraud screening at all.
How matching actually works
List checks match incoming transaction data against stored records using fields like address, ZIP code, phone number, card number, and email. Name-based matching should be avoided — too many people share similar or identical names for it to be reliable on its own.
Best practice
- Keep hot list data for at least 12–18 months
- Run hot list checks before the authorization request, not after
- Normalize addresses so formatting differences don't cause false negatives
- Require a 100% match before trusting a positive list result
- Share list data across every sales channel, not just one
These can be built in-house with a database and the right setup, or picked up as a built-in feature of most fraud-screening services and decision engines.