Handling Potentially Fraudulent Orders

The Open Dining system has a built-in fraud detection system to flag unusual-looking orders and alert you, to potentially mitigate the risk.

Handling Alerts

When a flagged order comes in, the order notification will indicate that it's potentially fraudulent.

  • On-screen displays will say "Potential Fraud"
  • Print-outs will say "See ID" or "Check ID"

When this happens, your team should take the following actions:

  1. Note down the card type and digits from the order, such as Visa ending in 1234
  2. When giving the customer their order, ask to see the card and an ID
  3. Check that the card matches the type and digits from the order, and that the name on the ID matches the name on the card
  4. If everything looks good, give the customer their order!
  5. If there's a problem, use your judgment and any company policies that may apply

Is Each Alert Definitely Fraud?

No!  Although our system may flag an order as risky, it is not necessarily fraudulent.

The most common reason for orders being flagged is simply that they're much larger than your typical order.  If you normally receive $35 orders, a $250 order is definitely an outlier, and will usually be flagged for review.

However, the order may simply be a large order for a company event, office lunch, or party.  Even so, checking the card and/or ID helps to keep you and your customers safe and happy.

Updating Our Database

If you know that a certain order/customer is definitely good or definitely fraudulent, you can tell the Open Dining system, so we can better improve our fraud-scoring results in the future.

To update an order's fraud status:

  1. In the Order History section, find the order in question and load the details
  2. Under the Fraud Scoring section, click the Good button for a good order, or the Bad button for a bad order
    1. Selecting Good will exclude the customer from future fraud reviews, making it easier for everyone with future orders
    2. Selecting Bad will ban the customer from ordering in the future, and help train our system to better spot similar fraud

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