Use Machine Learning to Optimize Your Chargeback Management


As the digital age continues to evolve, eCommerce has transformed the way business is conducted. Businesses can now reach far wider than before and are able to process transactions seamlessly. However, as technology evolves, so do the business risks. One particular threat to eCommerce is the effects of chargebacks. Now more than ever, businesses need to seize new opportunities or risk falling behind. Step forward, machine learning technology for chargebacks. In an effort to fight ever-increasing chargeback figures, machine learning provides an innovative solution to efficient chargeback management.

How do chargebacks occur?

Chargebacks exist as a mechanism to uphold consumer protection in cases of fraudulent transactions. They occur when a cardholder disputes a merchant charge as unauthorized. This results in a refund initiated by the cardholders’ bank from the merchants’ bank account. 

Chargebacks occur for several reasons. 

A typical example is a merchant error chargeback. These occur when a transaction is deemed incorrect, duplicated, or unauthorized by a cardholder. Merchant error chargebacks also occur when the cardholder believes the items they received were not in accordance with the purchase agreement, either due to misrepresentation or another fault of the merchant. 

However, another form of chargeback that has emerged is friendly fraud chargebacks. Where a customer deems an authorized and genuine purchase as fraudulent, resulting in a host of financial and non-financial issues for the merchant. The resultant impact of this is that friendly fraud cases are often treated the same as true fraud chargebacks. Ultimately, without investigating each claim, it can be challenging to decipher the exact cause of the chargebacks.


What is machine learning in chargebacks?

Managing and mitigating chargebacks requires convincing evidence to form an effective rebuttal. Generating a successful chargeback rebuttal letter requires a comprehensive evidence-gathering process. There are multiple forms of evidence that may be presented. This includes the shipping address of the customer alongside their IP address, phone number, and other points of evidence. These can be used to establish the identity of the purchaser and refute fraudulent or chargeback claims. 

Justt’s machine learning solution provides chargeback mitigation by detecting the relevant data points needed to present as evidence. Additionally, machine learning technology can understand how to best present evidence to ensure optimal chargeback success rates. This can range from the document format for the evidence to the length of the rebuttal letter and other factors that influence the likelihood that an analyst at the issuing bank accepts the rebuttal. 

The success of any machine learning solution is based in part on the amount of data it has available for training purposes. This data needs to be taken from real-life situations and not just simulations in order for it to be useful. Previous success cases can be analyzed to identify what is required to mount a successful case. 

Conducive to this, Justt’s proprietary technology has processed millions of transactions, and this data is used to create an optimized chargeback mitigation service. It is important to note that Justt also uses human know-how to create a personalized solution for every client. This ensures that the machine learning technology is set for the most effective chargeback management per client.

How can machine learning in chargeback management increase your business’s bottom line?

The process of successfully disputing a chargeback case requires identifying evidence that can be burdensome to businesses. The amount of time it can take to gather the evidence is costly both in terms of employee salaries and time used. Additionally, chargeback cases can result in losing further income to chargeback fees, damage to reputation with payment processors, and loss of goods or services. 

Justt operates as a success-based fee solution. Meaning that you only pay when revenue is recovered. Therefore, you reduce losses to chargebacks while saving money, time, and human resources on having to counter chargeback claims by yourself. 

Bottom line

To thrive in the eCommerce space, efficiency and innovation are required to stay competitive amid what are usually thin margins. Machine learning technology in the fight against friendly fraud is the epitome of this. Chargebacks can now be dealt with effectively and will allow you, as a business owner, to concentrate on what actually matters.

The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of The World Financial Review.