Friendly Fire: Dealing with Deliberate Chargebacks from Consumers
Card-not-present fraud is an issue every single ecommerce sees, often in the form of the chargeback. This process can be very tedious and challenging for banks and card issuers because it requires a lot of effort to ensure customers get their money back. To best utilize CNP fraud prevention software, merchants should consider the causes of chargebacks by customers. A surprising number of fraudulent claims are not actually caused by criminals, but consumers instead. Addressing both of these concerns can help mitigate the effects of chargebacks.
A not-so-friendly customer tactic
There are a lot of different forms of fraud that can appear, quite a few of which are criminal activities. In order to be effective to prevent CNP fraud, you must consider the issue of friendly fraud. In this situation, customers request a chargeback in lieu of normal transactional resolutions, as noted by Inc. The two most common reasons for friendly fraud chargebacks are the ease with which an ecommerce customer can request a chargeback and the deliberate commission of fraud through legitimate means.
This is no small matter. CBS News found that 86 percent of all ecommerce chargebacks were deliberate. Heartland Payments Systems noted chargebacks cost merchants $40 billion in 2015, triple the number reported by Visa in a 2012 survey cited by Inc. This was likely do to the rapid rise of online and mobile commerce, as well as multichannel retail. Friendly fraud can flabbergast merchants because there may not be any problems with the transaction and the customer gets the product, yet they still get hit with a chargeback.
The customer isn’t always right
Part of the reason friendly fraud is so prevalent is the conventional wisdom that the customer is always right. Merchants and banks don’t question the chargeback because it would require a time-intensive and tedious investigation. When you do make a dispute, you have to prove without reasonable doubt that there is no fraudulent activity committed by your business, and it often costs you money in the form of fees. Customers, most of whom never see this process, don’t think much of it. Consequently, they don’t consider the ramifications of performing this act.
Fraud prevention software that is based on machine learning can be a powerful tool to address this issue. Using computational power, sophisticated machine learning models can effectively identify patterns of chargeback behavior and deliver meaningful insights through transparent white box scoring. Gaining a better understanding of customer behavior using transactional history, whether its online or offline, can help businesses determine if the chargeback was genuine or sign of repeated abuse. Using potentially unlimited variables and features, models can help further identify the characteristics of chargeback so they can be avoided in the future. A friendlier return policy can be a great help in this situation, as it will enable customers to perform returns and still get their money back. Chargebacks not only results in loss of potential revenue but also high administrative costs and possibly result in losing the merchant account or ability to accept a certain type of credit card. Taking a proactive approach can greatly reduce chargebacks from friendly sources while gaining a better understanding of fraudulent behavior.
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