The chargeback rate is a crucial metric for any business that processes card payments. Put simply, it is a measure of the percentage of transactions that result in chargebacks.
Note that a chargeback is a reversal of funds that occurs when a customer disputes a transaction with their card provider or bank.
To calculate the chargeback rate, the total number of chargebacks is divided by the total number of transactions and multiplied by 100.
For instance, if a business processes 10,000 transactions in a month and receives 100 chargebacks, its chargeback rate is 1%.
The factors that influence the chargeback rate for any given business tend to fall into one or more of the following three categories.
Friendly fraud occurs when a customer files a chargeback claim despite having received the goods or services.
This may happen if a customer:
Friendly fraud is a relatively common cause of chargebacks. In one survey, 23% of consumers said they had engaged in the practice. Friendly fraud is also responsible for around 70% of credit card chargebacks.
Merchant error can also motivate a customer to initiate a chargeback. The primary reasons include:
If a business doesn’t clearly communicate return policies, for example, customers may initiate a chargeback if unable to receive a refund.
When affiliate marketers use deceptive tactics to drive traffic or sales, the resulting transactions often lead to chargebacks.
Affiliate fraud occurs when the marketer makes fraudulent purchases with stolen credit card information to claim the affiliate commission. When victims notice the fraudulent transactions, a wave of chargebacks is initiated against the merchant in question.
More broadly speaking, chargeback rates also increase when criminals use stolen credentials to make fraudulent purchases online. These details are often stolen in phishing scams or as part of triangulation fraud, where the criminal poses as a legitimate seller in an online marketplace.
Card networks impose fines on merchants with elevated chargeback rates and payment processors may also increase transaction fees to mitigate risk.
Merchants that exceed Visa’s chargeback rate thresholds, for example, may be subject to fines and other penalties under the Visa Dispute Monitoring Program (VDMP).
The program has three tiers, with each based on both the number of chargebacks and chargeback ratio over a calendar month:
When a business fails to properly address its chargeback rate, it will reach the third tier. At this point, fines are incurred immediately and continue until the business’s account is cancelled by either the card network or payment processor.
A fine of $50 per dispute is applicable for businesses in the “Standard” and “Excessive” tiers. There is also a hefty $25,000 review fee at the end of the 12-month enforcement period.
Chargeback rates vary between industries for various reasons.
Here’s how finance stacks up against some other industries with some key drivers listed for each:
Chargebacks are an important part of consumer protection and offer security to victims of fraud or other abusive practices.
However, as touched on earlier, malicious actors (and many consumers who have not been victims of fraud) are responsible for a significant percentage of chargebacks.
Here are a few ways merchants can protect themselves in either case.
Chargeback representment is the process of a merchant disputing a chargeback with the bank. To do this, they must have reasonable evidence to support their claim and then submit a request for the bank to review
If the chargeback is reversed, the total amount of the transaction is refunded to the merchant. If, however, the claim is rejected, the merchant must absorb the chargeback itself as well as any associated fees.
After a chargeback, the temptation for many businesses is to focus solely on recouping lost revenue. While this is important, data analytics help uncover how and why chargebacks occur.
Some of the data that should be combined for analysis include:
Based on the above data points, businesses can identify chargeback patterns. For example, a high chargeback rate on low-value items may be representative of fraudsters testing stolen cards to see if they’re active before attempting larger purchases.
Improved customer satisfaction and transparency can also reduce instances of non-fraudulent (or borderline-fraudulent) chargebacks.
These include measures that:
Transparency can also be enhanced with the establishment of a robust evidence-collection procedure in the event of a dispute.
AI-powered detection employs machine learning algorithms to analyse transaction patterns and identify suspicious activity before it has a chance to escalate into chargebacks.
Algorithms evaluate multiple factors such as transaction history, user behaviour and device data to detect anomalies indicative of fraud.
This real-time detection enables businesses to block fraudulent transactions. But it also enables them to proactively address customer issues and reduce non-fraudulent chargebacks.
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