Data Integration Guide - E-Marketplace Seller Risk

Seller Onboarding & Vetting

Marketplace seller onboarding, as well as merchant vetting, are important drivers of DataVisor’s ability to identify marketplace seller risk. This document serves as a general integration guideline for identifying registration and catalog data as it pertains specifically to marketplace sellers.

Recommended Seller Registration Event Data

Fields that are helpful for marketplace registration use cases include (but are not limited to):

Category	Data Field	Description	Required
Datavisor Required Fields	Event Type	Seller Sign-Up/Registration	X
	Event Time	Sign-up/Registration Time	X
	Entity ID	Seller ID	X
Seller Profile Information	First Name	First Name	X
	Last Name	Last  Name	X
	Date of Birth	Sellers Date of Birth	X
	SSN	Social Security # - Individual Seller
	Business Entity Name	Legal Business Name	X
	DBA Name	“Doing Business As” Name	X
	Tax ID Number (EIN)	Tax Identification Number	X
	Tax Classification	U.S.(W9 or W-8 ECI) or LLC, S-Corp
	Business Entity Type	Private/Public	X
	Incorporation State	State where business is registered	X
	Business Address	Registered Business Address	X
	Business Email	Email	X
	Business Phone	Phone	X
	Company URL	Associated Business URL
	Bank Name	Bank Name Linked to Business
	Bank Account Number	Bank Account Linked to Business
	Registration Status	Status of Seller Registration	X
Device Intelligence	Device Identifier	Unique Device ID
	Device Model	IPhone10
	Device Operation System	IOS
	Device OS Version	Operating System Version
	Device User Agent	User Agent String
	Device RIsk Score	Risk level of device
Geolocation	Ip Address	Ip

Existing Seller Data Hydration

The data fields listed above have proven effective when utilized in risk detection for marketplaces looking to onboard and verify prospective sellers. To ensure data from existing sellers is available within Datavisor, we typically execute a one-time profile dump to warm up the system. This seller profile dump can contain the same data elements seen with new registrations but the event time should reflect the original registration timestamp.

An example of seller registration data:

US Business Tax ID (EIN)	Legal Business Name	DBA Name	Entity Type	Seller First Name	Seller Last Name	Tax Class	Company URL
12-3456789	DataVisor, Inc.	Datavisor	Private	Mark	Frost	W9	[e-mail]
12-9932424	LowPrices Co.	Low Prices	Public	Grace	Smith	W-8 ECI	lowpricesco.com

Recommended Seller Catalog Listing Event Data

Fields that are helpful for marketplace catalog risk use cases include (but are not limited to):

Category	Data Field	Description	Required
Datavisor Required Fields	Event Type	Item_Listing	X
	Event Time	Sign-up/Registration Time	X
	Entity ID	Seller ID	X
Catalog Information	Item ID	SKU/UPC/ISBN	X
	Item Name	Item Name	X
	Item Description	Description of listed Item	X
	Item Category	Category of Listed Item	X
	Item Sub-Category	Product Sub-Category	X
	Item Unit Price	Base Unit Price for Item	X
	Available Item Quantity	Number of available units	X
	Active Item Discount	Current Item Discount Rate	X
Device Intelligence	Device Identifier	Unique Device ID
	Device Model	IPhone10
	Device Operation System	IOS
	Device OS Version	Operating System Version
	Device User Agent	User Agent String
	Device Risk Score	Risk Level of Device
Geolocation	Ip Address	Ip

An example of catalog listing data:

Item UPC	Item Name	Item Category	Item Sub-Category	Item Unit Price	Item Quantity	Item Discount
03600029145	Fraud Fighter	Collectibles	Action	45.89	400	0.00
03600023345	ATO Shield	Toys	Outdoor	123.65	50	0.00

Data Structure Report

Before sending the full data over, please send over a data structure report to your Technical Account Manager. This process allows DataVisor to ensure a faster turnaround time and a higher quality of results during the fraud assessment. This data structure report should contain the following for each data set:

• Schema
• Preview of 10 rows
• Earliest date and latest date (If there is an associated timestamp)
• Number of rows

Data Anonymization

DataVisor’s algorithm is able to work with anonymized data fields if required. For sensitive PII information, such as name and address, anonymized information can be processed by our algorithm as long as the data structure remains.

Choosing a Connection Type: Batched or Real-time data transfer

For a batch data transfer, the client sends data in bulk to DataVisor. The frequency of batch data transfer is determined by client’s use case and business requirements. Batch transfers are faster to implement, as they don’t require an integration with DataVisor’s APIs. Account application data should be given as is at the end of the batch time period, and included in the batch. 

Real-time data connection

For a real-time data connection, the client integrates DataVisor’s APIs to send a real-time stream of data. After a ~2 week observation and tuning period, DataVisor returns real-time results, which can be used as an additional signal in the client’s fraud detection infrastructure. All account application data should be uploaded at the beginning of the fraud assessment. 

Formatting and transferring data

Formatting Data

DataVisor can accept data in the following formats:

1. JSON (preferred) 
2. Tab Delimited
3. CSV

For other formats, like TXT or report exports, please contact your Technical Account Manager.