The most useful fraud-prevention data is properly contextualized and subject to regular feedback about its validity, not just a static list of data points. There are three levels of data sources, from simple one-directional snapshots of names and addresses to richer sources that add context across related data points. Because breaches and sophisticated criminals make it easy to manipulate valid data, retailers should choose data that flows in both directions, with feedback from client to provider confirming what is still accurate.
We’re a society obsessed with getting feedback on everything from bagels to B2B vendors because we know that more information, especially up-to-date information, helps us make better decisions. This is as true in fraud prevention as it is in any other field, perhaps even more now that large-scale data breaches and increasingly sophisticated criminal practices have made it easier for thieves to manipulate valid data to commit fraud. Here’s why online retailers who want to stop the rising tide of e-commerce fraud should look for data that’s properly contextualized and subject to regular feedback about its validity.
In general, there are three levels of data sources available to businesses for use in marketing research: demographics, fraud prevention, and other business processes. Each level has its own limits of usefulness.
At the first level, the database is a straightforward collection of a type of data point, such as names, addresses, or phone numbers. Having a data point alone is simply a snapshot in time that shows, for instance, that a particular email address existed a certain number of months ago, but not what billing address or phone number were used with that email address in a valid online transaction. In these databases data points are relatively static, free of context, and only move in one direction—from the data provider to the business client. There’s no feedback from client to provider about the validity of the data, so it’s impossible to be certain that all the data is valid.
At the second data-source level, the provider adds context for multiple related data points.
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One-way data flows only from provider to client with no feedback about its validity, so it is impossible to be certain all of it is accurate. Two-way data includes regular feedback from client to provider confirming validity, which keeps the data current and more reliable for stopping fraud.
The article describes three levels: a straightforward collection of a single data point such as names or addresses, a level that adds context across multiple related data points, and data drawn from other business processes. Each level has its own limits of usefulness.
A standalone data point is just a snapshot in time. It might show that an email address existed some months ago, but not which billing address or phone number was used with it in a valid transaction. Without context, its usefulness for fraud prevention is limited.
Large-scale data breaches and sophisticated criminal practices make it easier for thieves to manipulate valid data to commit fraud. Properly contextualized data, which links related data points together, gives retailers a clearer picture than isolated, context-free records.
Regular feedback about validity lets a provider know whether data still reflects reality. Without that loop, static data can quietly become outdated. Feedback from client to provider helps confirm which data points remain accurate and useful.
Criminal practices evolve quickly and breached data circulates widely, so stale data loses value fast. Data that is contextualized and continuously validated through feedback helps retailers make better decisions and keep pace with the rising tide of e-commerce fraud.