DATA SHARING BUREAUS

DATA SHARING BUREAUS TECHNICAL OVERVIEW


Data sharing bureaus are organizations that collect and aggregate information from multiple companies to improve fraud detection through breadth of data. Key considerations when implementing or buying this functionality include:

  • How many companies are participating?

  • What information is shared, and how is it standardized?

  • How often is information provided by the companies?

  • How is the data secured?

  • Are the other companies in the same vertical markets as you?

  • Is the service sharing positive and negative experiences?

  • How are the negative experiences validated?

  • What types of organizations are participating in the data sharing, merchants, issuers or both?

HOW DOES IT WORK?


Vendors collect information either in a real time or batch mode from the participating merchants. The vendor cleans the data and updates their core data sets. The merchants call the vendor when they are processing a transaction to get a real time check on data associated with a consumer.

The merchants also have to periodically notify the vendor when they get information indicating a past transaction has gone bad. The vendor updates the core data set with the negative information, so all participating merchants can make use of it.

HOW DO YOU USE THE RESULTS?


The results from this type of a service should be used to augment your fraud prevention strategy. Getting a negative hit with this type of service is a strong indicator, but a "no hit" or "high velocity hit " are not necessarily strong indicators of risk.

DID YOU KNOW


Data Sharing Bureaus improve fraud detection with breadth of data aggregating information from multiple companies including velocities and negative lists.


Nobody in this business is ever going to say that sharing information isn't a good concept and could help prevent fraud. The problem isn't the concept, it is how do you get companies to share their data? This is a critical component to determining how effective a service provider is, since the service will only provide value if the vendor is able to get enough companies to participate.


Achieving critical mass is not a small problem to overcome as there are a number of barriers the vendors have to overcome to get a company to agree to share their data; and for the vendor to make it of value to the other participants.


In short, the value of data sharing bureaus is their ability to build models based on a much larger data set than any one merchant has. A single merchant's internal fraud scoring system will only have limited effectiveness, as the breadth of data that is being looked at is only their data. This will affect any and all velocity checks such as velocity of change and velocity of use. For example, modeling and neural nets that are built, and/or used, solely in a one-merchant implementation don’t get the benefit of seeing consumer activity outside of their business. For data sharing bureaus the more data that goes into building the service, the better it will predict and catch fraud.

It is important to know that:


1) Modeling and neural nets that are maintained in-house suffer from breadth of data, missing key information from attempts on cross-merchant data.


2) It is only a tool: It provides good information, but merchants will still have to perform other fraud checks.


3) Verify the vendor's validation of negative information; in some cases some participating companies could purposely flag their best clients to prevent competitors from doing business with them.

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THE FRAUD PRACTICE

KEY NOTES


Alternative Solutions - Use of fraud scoring, identity authentication.


Building this In-House - Any company can setup their own data sharing relationships with other companies.


Estimated Cost - Costs will vary based on the vendor you select. Typically this service is offered on a subscription pricing plan, but some offer it on a transactional basis. As with most services volume and the length of your agreement can greatly effect your cost.


Sample Vendors - Ekata, LiveRamp, Experian, Neustar