While we include past negative activity in scoring, blacklisting and automatic declines can negatively impact decision accuracy and make fraudsters more effective. Why? Negative activity is not 100% accurate and may contain stolen data. Transactions that would be declined by a blacklist should be analyzed by a human to verify if they are legitimate orders placed by people who have had their data stolen.
One-way fraud data lists (from the source to the list) are static — once it’s on the list, it stays on the list. But the information deteriorates over time. From a statistical perspective, a static blacklist (or whitelist) that’s never cleaned or updated would eventually include data on every consumer — and that’s obviously not helpful for merchants. Hence, static lists can lead to more fraud claims and more false declines for merchants.
Dynamic blacklists (or whitelists), on the other hand, can be a valuable tool for fraud prevention. ClearSale relies on these dynamic lists to learn whether our lists of combined data yield good or bad transactions over time, and we’re continuously updating them.
This approach solves two problems:
- Keeping up with fraudsters to avoid approving transactions that use corrupted consumer data
- Avoiding declines of good orders simply because they look suspicious, come from a region with a higher-than-average fraud risk, or contain a bad data point — such as a billing address or phone number that was used fraudulently in the past
Blacklists are just one of several layers of protection that ClearSale has in place to guard against transaction fraud, looking to have every layer performing optimally.