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Underpinning Fraud Prevention with Email Data

The world is increasingly digital.

Even prior to the pandemic we were on a trajectory towards digital-first engagement, and in just a few months’ time the COVID-19 crisis accelerated the digitization of companies’ customer interactions by 3 to 4 years (McKinsey).

As of 2022, close to 4.26 billion people – over 50% of the world’s population – were using email (Radicati). That number is expected to grow close to 100 million per year. On top of that, the total number of active email accounts is close to 5.6 billion (Bizcognia).

The email address is becoming synonymous with digital identity. It is a key identifier that forms the basis for the way most people are engaging with the world – through apps, social media, bank accounts, and so much more. But it is also an ideal target for malicious activity, with 75% of companies having experienced an increase in email-based threats (Mimecast).

Organizations need to focus as their digital efforts are opening them up to increasing risk. And as they scale, so too does the increased possibility of fraud. A great place to start is with the underlying data.

Prioritizing Email Data

Email is the central data element for most account creations, account changes, verifications, and transactions. Therefore, it is the most logical data point to detect and stop fraud. The problem is that there are constantly new email addresses being created as well as an average of 33,000 domain name registrations per day (DiggityMarketing).

The influx of email address possibilities makes prioritizing verification and validation a necessity. While just knowing an email is valid will not negate fraud entirely, it will lay a foundation for more advanced methods at identifying intent. 

Once validated, layering on identification of spamtraps, honeypots, and dangerous domains will alleviate some of the potential threats to business operations. These are, unfortunately, technically valid email addresses so will not bounce, but can open a company up to issues with email service providers or worse. While some of these can be flagged within a system, working with a broad network will make identifying these concerns much easier.

Advancing Fraud Prevention

With a solid foundation of comprehensive, accurate email data, organizations can more quickly identify when anomalous information enters their system. They can then flag these for further review, keeping their customers’ journey frictionless.

Working with an elaborate network of email signals utilizing an established fraud prevention API is even better. There are machine learning models already built to identify malicious activity at a more rapid pace across billions of data points for things such as:

  • Onboarding – To identify fake account creations while keeping customer data clean and preventing fraud downstream.
  • Transactions – Alleviating transactional fraud, lowering friction for good customers, and reducing ‘friendly fraud’ chargebacks.
  • Account Management – Detect account take-overs and reveal identity theft to maintain a clean client base, ultimately increasing customer loyalty.

Being able to further tie additional identifiers to the email address opens other avenues for detecting fraud. Appending a physical address can help stop reshipping fraud by flagging conflicting information between the two data points. Even resolving multiple email addresses to the same individual can mitigate concerns. 

The average consumer has about 2 email addresses (99Firms), but many active digital users have more that they utilize across a variety of apps and accounts. In a Forbes article, individuals were even encouraged to have at least 4 email addresses. If a customer is unjustifiably flagged as fraudulent because they happen to use the wrong email, it can cause friction that will push them away. 

Complete, accurate email address data is absolutely key.

Insights for Fraud Identification

To do fraud prevention right in the digital world, an organization needs to be able to look across many data points in real-time to identify potential issues. While this can be hard to do alone – working with an established email-centric fraud prevention specialist is ideal – addressing some of these within the dataset will get a company started down the right path. 

  • Behavioral Insights – Identification of first activity date, email velocity, and popularity.
  • Platform Tumbling Check – Fraudsters sometimes use sequentially named email addresses or multiple variations of the same email.
  • Domain Analytics – Profiling of email domain risk along with ongoing activity.
  • IP Verification – Analyze IP addresses to determine trust and flag suspicious behavior.
  • Email Validation – Stop risky or dead addresses from compromising datasets.
  • Name and Postal Address Correlation – Check that the name and postal match information previously seen and verified.
  • Email and Address Anomaly – Check for abnormally high amounts of postal addresses associated with a given email address or emails to a postal address.
  • Artificial Intelligence Bases Risk Score – Determine a numerical risk value driven by machine learning models.

Identifying ways to utilize email data in fraud prevention strategies is key. With the email address becoming more important in the continuing shift to digital, it cannot be overlooked. The best way to get started is to look at partnering with an established fraud prevention company that specializes in email data. Their network of signals and database will bolster your efforts and greatly reduce the time to implementation. Adding a fraud prevention API to an intake form that captures email is a good start and a small tech lift for a large benefit.

The email address has become the central data element for most account creations across platforms, apps, and digital transactions. It has become crucial to discover malicious activity based on this identifier.

Visit to learn how you can implement email-based fraud prevention strategies.

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Author: Diarmuid Thoma

AtData’s Vice President of Fraud & Data Strategy. Over 20 years of experience designing and working on fraud prevention platforms with large companies including Facebook, Symantec, and Hewlett Packard. Founding member of fraud platform Trustev which was acquired by TransUnion.