Kount Launches New Friendly Fraud Prevention Solution Featuring VMPI

Friendly fraud, made up of chargebacks and lost goods due to intentional and unintentional exploitation by real customers, can account for 40-80% of all fraud losses.

For a business with a strong criminal fraud prevention program, the remaining chargebacks can be from legitimate customers in the form of friendly fraud. While in some instances, friendly fraud is intentional, it isn’t always – some aspects of friendly fraud are due to exploitation. Whether friendly or not, these chargebacks often come through with a reason code that lacks specifics about why the chargeback occurred. Without expert tools, it can be difficult for businesses to understand that certain types of friendly fraud are not friendly at all and are caused by malicious intent. With added information, businesses can protect themselves from several types of friendly fraud that are not friendly at all.

Intentional Friendly Fraud

With this type of fraud, a consumer makes a purchase, calls the bank, recognizes the purchase, but still requests a credit from the issuing bank and claims they did not make the purchase.

In this hypothetical example, a consumer is able to fill her closet with a collection of expensive designer shoes without actually paying for them. This consumer intentionally called her bank to request a refund by claiming that the shoes, which she is currently admiring, were never received. By claiming they were never delivered, the consumer simply takes the intentional step to request a refund from her bank, initiating a chargeback. Without any visibility into the purchase, her bank simply refunds her account and the businesses absorbs the chargeback and loss of the shoes.

This can be repeated multiple times with multiple venders with no impact to the consumer.

Policy Abuse Fraud

A similar type of fraud is policy abuse. For example, retailers who prioritize customer service often have flexible return policies. By easily accepting returns, businesses try to build trust with new customers and create a loyal customer base. Others view flexible return policies as a way to remain competitive. Some businesses allow users to return items without a time limit or a limit for the number of times a customer can return or request a refund. While companies want to be customer-centric, if their policies are abused, it can be detrimental to sales, gross margins, and profitability. While it doesn’t happen with the majority of customers, there are some who take advantage of these customer-centric policies, and this behavior can result in policy-abuse fraud.

A significant example is “wear once and return” behavior. Or, perhaps a certain consumer buys more than they are planning to keep and always sends products back. By analyzing fraud data, businesses can begin to recognize frequent returns and take action when they identify shoppers who abuse the policy.

In-flight Chargebacks

When a business issues a refund based upon one of the scenarios above, it may take several days for the consumer to see the refund, but they expect it to reach their accounts instantaneously. When the refund doesn’t occur quickly enough, the customer often calls the bank to say the charge wasn’t refunded. Because the issuer doesn’t know when or if it was refunded, they dispute the chargeback. In the interim, the customer might post a negative review about the brand on a third-party website. For the business, this means that they may pay the refund twice.

Resolving Intentional Friendly Fraud

When added together over time, these types of fraud can have a significant financial
impact. Kount’s Friendly Fraud Prevention Solution featuring VMPI
 fights all the causes of fraud losses, including friendly fraud. It protects against chargebacks, increases revenue, and improves customer experience. Datamart, Kount’s advanced data analytics, analyzes fraud and then isolates the legitimate disputes from friendly fraud. Kount provides the only complete solution to accurately identify, separate, and solve for these friendly fraud types:

  • Intentional friendly fraud: By analyzing friendly fraud chargebacks, businesses can pinpoint areas for improvement. Kount’s solution can help determine specific patterns or abusers. Businesses can then take action against intentional friendly fraud or put specific policies into place such as adding tracking or a signature, or other options to keep track of a shipment once it leaves the warehouse.
  • Policy abuse fraud: Businesses can gain deeper information and insight by relying on Kount’s self-service analytics feature, Datamart, which gives businesses control over their data so they can analyze fraud data and take action when necessary. Datamart is a key feature in helping businesses isolate types of friendly fraud chargebacks to put the appropriate solutions into place.
  • In-flight chargebacks: Kount’s Friendly Fraud Prevention Solution with VMPI integration helps businesses circumvent chargebacks by providing additional information about the status of a refund. If a refund is in process, it may not appear on a customer’s statement immediately. With more information, the status of a refund can be confirmed, thereby avoiding the potential for double refunds.   

Kount’s solution uses supervised and unsupervised machine learning to distill transaction risk in a single safety rating, Omniscore, which detects fraud and minimizes false positives. Gain a single platform for fraud prevention that accurately detects and protects against all types of fraud.

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