Chargeback BlacklistIs This Really Your Best Bet Against Chargebacks?

Can a Chargeback BlackList Prevent Post Transaction Fraud?

Chargeback blacklists are considered effective tools for fighting post-transactional fraud. With the dramatic growth in eCommerce—and the accompanying surge in chargebacks—card-not-present (CNP) merchants are looking for an easy way to cut off digital fraudsters for good. 

The straightforwardness  can be enticing. By banning anyone who commits fraud or chargeback abuse, you avoid being victimized again in the future. But are blacklists really the answer? Let’s find out. 

What is a Chargeback Blacklist?

Chargeback Blacklist

[noun]/chahrj • bak • blak • list/

A chargeback blacklist is a database containing details on persons, institutions, or locations that present a high chargeback risk to you as a merchant. Future transaction attempts submitted by blacklisted customers are blocked based on inclusion on the list.

A chargeback blacklist isn’t a literal “list.” It could more accurately be described as a kind of database of accounts that are known to have been associated with fraud.

Blacklisting relies on data from previous fraudulent transactions. The aim is to identify risky accounts and block them from making future purchases.This should help prevent chargeback abuse without jeopardizing legitimate sales.

Retailers may create their own lists by manually checking orders. This is a work-intensive process, however, and could negatively impact the customer experience. Instead, merchants often invest in either external lists or fraud screening software that automatically monitors each transaction for blacklisted account information.

How do Chargeback Blacklists Work?

Some systems are designed to blacklist users based on a single factor. For example, you can automatically ban anyone who has filed an invalid chargeback (a practice known as friendly fraud). However, you can achieve more accurate results — thereby avoiding false declines — by using sophisticated filters that access multiple data points.

Blacklist decisioning can be based on a number of criteria. Some of the most commonly-used identifiers for fraud blacklists include:

• Customer location data
• Delivery location
• Device fingerprint
• Email address
• IP address

Specific blacklisted data points can vary, of course. However, they all represent triggers historically linked to fraud. Filters can be as granular as the ID of the device used for the transaction or as wide as a grouping of entire countries or regions.

Are Chargeback Blacklists Effective?

Blacklists work in the sense that they will stop some transactions from being processed. Whether they stop the right transactions is a different story.

Merchants who promote blacklists compare them to tools like spam filters. Once you mark an email as spam, you probably won’t get another email from that same source. The comparison is not totally accurate, though, for two key reasons.

First, spam filters work on very basic logic. One person (the user) makes a “yes or no” decision, primarily based on a single factor: do I recognize this email? Second, any issues that may arise are localized and easily undone. Neither of these circumstances applies to a chargeback blacklist. Accidentally blacklisting a legitimate customer means lost sales revenue and possible reputational damage among buyers.

Counting on blacklists as a primary fraud prevention device is problematic. For starters, fraudsters are already ahead of the game. They understand how blacklists work and have developed multiple tactics to thwart them. They may create new email accounts using multiple cards. They can also ship goods to post office boxes or use proxy servers. These are just a few examples.

We don’t recommend that you ban users based on a single data point. Of course, this means the decision to blacklist a buyer can be a highly complex one.

Chargeback Blacklists vs. Whitelists

In contrast to chargeback blacklists, some merchants choose to deploy whitelists instead.

Whitelists operate on the opposite principle of the blacklist. Instead of identifying and blocking potentially fraudulent users, a whitelist is a list of trusted users who are pre-approved for transactions. These users have a history of legitimate transactions and are less likely to be flagged for fraud.

Refer to this table for a quick comparison:

Aspect	Blacklists	Whitelists
Definition	List of users flagged for suspicious activity or fraud	List of users pre-approved for transactions
Criteria	Single factor (e.g., chargeback) or multiple data points for intelligent blacklisting	Historical transaction legitimacy and trustworthiness
Risk	High risk of false positives with single-factor blacklisting; moderate risk with intelligent blacklisting	Risk of fraud only if whitelisted users' behavior changes or accounts are compromised
Customer Experience	Can lead to false declines, impacting legitimate customers negatively	Enhances experience for loyal customers by reducing false declines
Management Complexity	Requires ongoing updates and sophisticated algorithms for intelligent blacklisting	Requires monitoring to ensure whitelisted users remain trustworthy

Whitelists help reduce the likelihood of false declines for frequent, loyal customers. However, relying solely on whitelists can be risky if not managed properly, too, as fraudulent activity can sometimes originate from seemingly trusted sources.

How to Implement a Chargeback Blacklist

Implementing and using chargeback blacklists and whitelists can significantly enhance your fraud prevention strategy. It's important to note that, while these tools are powerful, they should be part of a broader chargeback prevention plan. But, we’ll get into that further down. 

For now, let’s look at what you’ll need to do to implement a blacklist as part of your strategy:

How to Implement a Chargeback Whitelist

We’ve explained how to set up a blacklist. Now, here’s how to get started with putting together a chargeback whitelist:

False Declines & Machine Learning

Remember: blacklists and whitelists both rely heavily on machine learning algorithms to function at their full capacity.

As we established earlier, some of the factors used for comparisons are generic enough that they can lead to false declines with a chargeback blacklist. Furthermore, failing to conduct due diligence with whitelists can result in fraudulent charges resulting from account takeover or other scams that go undetected.

You need to establish a multilayered fraud and chargeback prevention strategy that moves beyond computer programming. This is the only way to really combat this issue and protect your bottom line.

No Single Tool Can Prevent Fraud

Keep in mind that while chargeback blacklists are designed to offer protection against criminal fraud, as few as 10% of chargebacks may actually be criminal fraud cases. The vast majority of chargebacks stem from merchant error and friendly fraud.

Automated chargeback management isn’t nearly as effective as a personal approach. Universally banning potential fraudsters means you also ban potential customers. So, rather than relying on a list—or any other single tool—consider a multipoint strategy that addresses every stage of the chargeback cycle.

You need a chargeback management plan tailored to your specific business to offer better protection and long-term results. Ready to take a more customized approach to chargeback prevention? Contact Chargebacks911® today to see how much ROI you can expect with our services.