Identify e-commerce orders shipping to frauded addresses

Introduction

One of the more creative uses of Datafiniti is using our property data to track potentially fraudulent e-commerce orders. Fraudsters may attempt to ship packages to a vacant home and pick up those packages from those houses because they know no one is living there. Online retailers can use our property data to check the shipping addresses against the addresses of recently listed or sold homes to flag potentially fraudulent orders.

Here's a brief overview of how this process works.

Collect a list of recent shipping addresses

As new orders come in, collect the shipping addresses on each order. That list might look something like:

Address, City, State, Zip
1023 Jollyville Rd, Houston, TX, 77079
451 Webber Ln, Austin, TX 78721
200 Tennyson Ave, Pittsburgh, PA, 15213

Address normalization

Datafiniti normalizes street designations to standardized abbreviations, such as converting street to st and drive to dr. Use the normalized form of an address for exact matching against Datafiniti property data. Learn more in Normalized Property Addresses.

Check an MLS address against Datafiniti

Pass each address (provided by your or an MLS network) to the Datafiniti Property API and check the status of the property. Your query will look like:

{
  "query": "address:"200 Tennyson Ave" AND city:Pittsburgh AND province:PA AND postalCode:15213"
}

If there's a match, you'll get data back on the most recent status of the property. The data in the returned property record will look like:

"mostRecentStatus": "For Sale",
"mostRecentStatusDate": "2023-03-28T15:34:20.727Z",

Flag properties recently listed or sold

If the address matches a property that is recently listed (For Sale) or sold (Sold), then you can decide to flag that corresponding order as potentially fraudulent.

Flag orders shipping to business addresses

Vacant homes aren't the only shipping-address risk. Fraudsters also route stolen-card orders to commercial locations disguised as residences — freight forwarders, reshipping "drops," and commercial mail-receiving agencies (CMRAs) — or to addresses that don't correspond to a real delivery point at all. The businessesAtAddress field lets you confirm whether an address a customer entered as a home is actually a place of business, adding a second layer of fraud coverage on top of the vacant-home check.

Check the shipping address for a business

Reuse the same property query from above. In the returned record, inspect the businessesAtAddress field, which lists any businesses Datafiniti has seen operating at that address:

"businessesAtAddress": [
  {
    "name": "Alderbrook Boutique",
    "key": "us/me/eastbrook/1212eastbrookrd/alderbrookboutique",
    "dateSeen": "2026-09-26T00:22:11Z"
  }
]

Flag residential orders tied to a business

If a shipping address entered as a home comes back with one or more entries in businessesAtAddress, treat it as a commercial location rather than a residence. Pair it with propertyType to separate a genuine home from a storefront, mailbox service, or freight forwarder. When no business has ever been seen at the address and the address matches no property record, that absence can point to a fake or non-deliverable address worth holding for review.

Weigh home-based businesses

A legitimate home-based business can also appear in businessesAtAddress, so treat this field as one signal among several (order value, billing/shipping mismatch, vacancy status) rather than an automatic decline.

A record flagged by the business-address check looks like this — the address was entered as a residence, but Datafiniti shows a business operating there:

{
  "address": "1212 Eastbrook Rd",
  "city": "Eastbrook",
  "province": "ME",
  "postalCode": "04634",
  "businessesAtAddress": [
    {
      "name": "Alderbrook Boutique",
      "key": "us/me/eastbrook/1212eastbrookrd/alderbrookboutique",
      "dateSeen": "2026-09-26T00:22:11Z"
    }
  ]
}

A genuine residence returns no business at the address:

{
  "address": "451 Webber Ln",
  "city": "Austin",
  "province": "TX",
  "postalCode": "78721"
}

Example Records

Here are example bulk download files of our previous query:

Conclusion

You can utilize the address with property status to build a report to calculate if the address is legitimate or not. This will help online retailers protect themselves against fraudulent orders.

Was this page helpful?