Fraud Blacklist: Blocking Fraudulent Buyers and Addresses in OMS

Keep one list of known fraud signals — names, emails, phones, addresses, IPs and device fingerprints — and every new order is checked against it automatically. A hit flags the order for review instead of letting it sail through.

The fraud blacklist is your “never again” list. When a buyer cheats you — a chargeback scammer, a stolen-card order, a reshipper address that keeps coming back — you write down the signals they used: a name, an email, a phone, an address, an IP, a device fingerprint. From then on, every new order in OMS is checked against this list automatically, right after it is created. A hit does not cancel the order and does not block you from working on it; it flags the order with a risk level so a person reviews it before the goods leave. You maintain the list in OMS → Fraud Blacklist.

Step 1: Add a blacklist entry#

1. Go to OMS → Fraud Blacklist and click Add Entry.
2. Choose the entry type — Name, Email, Phone, Address, IP or Device fingerprint — and type the value exactly as the fraudster used it. Under the field you will see a normalized preview: the cleaned-up form the system actually compares (see the next section).
3. Check the match type. The form pre-picks it for you: names and addresses default to Fuzzy (very similar counts as a hit), emails, phones, IPs and devices default to Exact. Change it only if you know why.
4. Set the risk level — High, Medium or Low — and write the reason (what happened: chargeback, stolen card, repeat abuse…). The reason is what your team reads later when an order hits this entry, so write it like a note to a colleague. Notes are optional extra detail.
5. Save. The entry is active immediately and takes part in checking new orders from that moment on.

The six entry types and how they match#

• Email, Phone, IP and Device match exactly — but only after cleanup (next section), so “+1 (555) 123-4567” and “5551234567” are the same phone to the system.
• Name and Address match fuzzily: a buyer who changes one letter, drops a punctuation mark or writes “Street” instead of “St” is still caught, because the comparison scores how similar the two texts are instead of demanding they be identical.
• One entry is one signal. A determined fraudster rarely reuses everything, so add each signal you have as its own entry — the combination rules in the review section reward having several.

Cleanup before comparison (normalization)#

• Phone: only the digits count — spaces, dashes and brackets are stripped, and a leading 1 on an 11-digit US number is dropped.
• Email: compared in lower case, so capitals never hide a match. Name: lower case, punctuation removed, extra spaces collapsed.
• Address: common words are shortened to their standard abbreviations first (Street → St, Avenue → Ave, Apartment → Apt, North → N and so on), then punctuation and extra spaces are cleaned up.
• The list page shows both the value you typed and its normalized form, and the add/edit dialog previews the normalized form live — what you see is exactly what gets compared.

What happens when an order hits#

• The check runs right after the order is created and does not slow order creation down. On a hit, the order gets a fraud flag recording which entries matched, on which fields, and at what risk level.
• How the flag’s risk level is decided: one exact hit takes the risk level of the entry it hit. Several weak signals add up — two or more fuzzy hits, or one fuzzy plus one exact hit, are forced to High. A single fuzzy hit on its own lands at Medium, so a vaguely similar name alone never brands an order high-risk.
• A flagged order is a warning, not a wall: work on it continues, and when you go to ship it, the shipping step shows a fraud warning so the shipper makes the final call with eyes open. Nothing is cancelled automatically.

Step 2: Review a flagged order#

1. Open the order in OMS → Orders and look at the fraud flag: which blacklist entries it hit and why those entries exist (their reason text). To see every order a single entry has ever caught, open the blacklist page, find the entry and click View hits.
2. False alarm? Clear the flag from the order and give the clearing reason (it is required). The order goes back to normal handling, and the clearing is written to the audit trail with who did it and why.
3. Confirmed fraud? Confirm it from the order, and — in the same action — you can have the buyer’s name, phone, email and ship-to address from that order added to the blacklist automatically (high risk), so the next order from the same signals is caught before anyone has to remember this case.
4. You can also flag an order by hand — mark it as a fraud risk with your own reason and risk level — when something looks wrong but no blacklist entry matched, and you can re-run the check on an order after editing the blacklist.

Step 3: Bulk import, export, and switching entries off#

1. Already have a list somewhere else? Click Import on OMS → Fraud Blacklist and upload a CSV with the columns entry_type, value, match_type, risk_level, reason, notes. Rows that duplicate an existing entry are skipped, and the result tells you how many were imported and how many were skipped.
2. Click Export to download the whole current list as a CSV file — handy for a backup or for sharing the list with another team.
3. Filter the list by type, risk level and status, or search by value, to find an entry again. To stop an entry from matching without deleting it, use Deactivate — it stays visible as Inactive, and Activate brings it back. Only active entries take part in checking.

Docklify features used in this article#

• Fraud blacklist list with type / risk / status filters and search — OMS → Fraud Blacklist (/oms/fraud-blacklist)
• Add / edit entry (six types, exact or fuzzy, risk level, reason) with live normalized preview — OMS → Fraud Blacklist → Add Entry
• Automatic order check and fraud flags with combined risk levels — runs when orders are created in OMS → Orders
• Flag review on the order: clear with a reason, confirm fraud (optionally auto-blacklist the buyer), manual mark, re-check — OMS → Orders → open an order
• View hits per entry — OMS → Fraud Blacklist → an entry → View hits
• CSV import (duplicates skipped) and CSV export — OMS → Fraud Blacklist → Import / Export