arrow left facebook twitter linkedin medium menu play circle
December 15, 2021 - Claire Zhou

How to Prevent Policy Abuse Fraud and Friendly Fraud

Customers dispute charges for a variety of reasons: their credit card data was stolen, an item never arrived, or they didn’t remember making a purchase, for example. In some cases, these disputes are perfectly valid. But in many cases, a dispute is the result of friendly fraud or policy abuse fraud, and it’s costing businesses millions of dollars per year.

What Is Friendly Fraud and Policy Abuse Fraud?

Despite the nice name, friendly fraud isn’t as friendly as it sounds. It’s first-party fraud that occurs when customers try to get money back on goods or services they purchased for which they may not be entitled to a refund. This usually happens when a customer contacts their bank instead of the merchant to dispute the charge, resulting in a chargeback to the merchant.

Policy abuse fraud falls along similar lines. Customers take advantage of refund or return policies, even when items aren’t defective or they’ve already used the item. 

Friendly fraud can happen intentionally or unintentionally. However, policy abuse fraud most often happens intentionally.

The Business Impact of Friendly Fraud

Both friendly fraud and policy abuse fraud can wreak havoc on a business’s bottom line. The average chargeback ratio across all industries is about 0.60%, with every $1 of fraud costing merchants roughly $3.36 in expenses. With merchants only winning about 21% of chargeback disputes, friendly fraud and policy abuse fraud remain costly problems for retailers. 

In total, retailers lost $7.7 billion in 2020 due to online return fraud. To put it another way, about 18% of all online sales were returned, and roughly 7.5% were found to be fraudulent. 

While retailers want (and need) to be flexible in accepting customer returns to remain competitive, they shouldn’t have to do so to the detriment of their company. Eroded margins raise costs for good customers, and can cause extra friction as merchants try to reduce potential acts of fraud. However, too much friction in online ordering and returns can deter good customers from purchasing, leading to lost sales and less profit to make up for bad actors.

20211117_How to Stop Chargeback Fraud with AI-Powered Detection600x400

How to Prevent Policy Abuse and Friendly Fraud

Retailers can’t afford not to take friendly fraud and policy and promotion abuse fraud seriously. In addition to strengthening your return policy and training your customer service team, it’s also essential to review your fraud prevention strategy. 

As a comprehensive AI-powered fraud and risk platform, DataVisor helps retailers fight fraud at scale. Our platform provides detailed data analysis on every transaction in real-time, including the links between entities, past transactions, and user behaviors to find repeat offenders. Intuitive visualization models allow you to see the “why” behind DataVisor’s findings and address potential acts of fraud on the spot. 

Our fraud detection solution has resulted in a 20% fraud detection uplift, 94% detection accuracy, and an average $15 million in annual savings due to fraud. From stopping fake or duplicate accounts at registration to tracking customer return histories, DataVisor integrates with your existing systems and starts creating value from Day 1. 

Learn how to stop policy abuse and friendly fraud at the source. Experience proactive AI-powered fraud prevention today.

Request Demo

Photo of Claire Zhou
about Claire Zhou
Claire is a Senior Product Marketing Manager at DataVisor with over 5 years of marketing experience in security and fin-tech. She is passionate about empowering enterprise customers with AI-based solutions. Her expertise spans data analytics, cybersecurity, and fraud prevention. Claire has an MBA from UCLA.
Photo of Claire Zhou
about Claire Zhou
Claire is a Senior Product Marketing Manager at DataVisor with over 5 years of marketing experience in security and fin-tech. She is passionate about empowering enterprise customers with AI-based solutions. Her expertise spans data analytics, cybersecurity, and fraud prevention. Claire has an MBA from UCLA.