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.

Step 1: 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

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Address normalization

Datafiniti uses our own street name normalization. Converting street to st, drive to dr, etc, you can learn more about our address normalization here: Normalized Property Addresses

Step 2: Check each address against Datafiniti

Pass each address 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",

Step 3: 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.

Example Records

Here are example bulk download files of our previous query:

• E-Commerce Record CSV
• E-Commerce Record Json

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.