If you missed our webinar on August 3rd, “Finding Revenue in Friendly Fraud Losses”, this blog post provides a convenient recap.
- Monica Eaton-Cardone, COO and Co-Founder, Chargebacks911
- Jennifer Johannsen, Director of Risk and Compliance, ClickBank
- Don Bush, VP of Marketing, Kount
- Melayna Gabiou, Sr. Marketing Manager, Kount
The webinar covered 4 topics:
- How to reduce chargebacks related to criminal and friendly fraud
- How to improve bank and customer behavior through disputing your chargebacks
- Case study of how much ROI you can gain from fighting chargebacks
- Benefits of the Kount and Chargebacks911 partnership
Chargebacks Are Usually Labeled as Fraud
Don Bush, VP of Marketing of Kount began by identifying the three most common types of fraud:
- Criminal Fraud. This is defined as a criminal stealing from a consumer and is up 22% in 2016, which is faster than e-commerce growth overall.
- Consumer Fraud. This happens when a merchant steals from a consumer. For example, if businesses don’t deliver the goods or services promised to online shoppers.
- Friendly Fraud. This is when a consumer steals from a merchant and grew almost twice as fast as criminal fraud – up 41% in 2016.
This presentation is focused on criminal and friendly fraud.
Polling Question #1
Sources of Criminal Fraud Chargebacks
Don then discussed the 3 factors that are fueling the increase in Criminal Fraud:
- Fraud tools and training are easily and cheaply available to criminals on the Dark Web.
- About 1.2 billion data records were compromised in 2016.
- There are more opportunities than ever for fraudsters due to the growth of mobile commerce, the EMV mandate, the proliferation of payment types and sources, the new fraud prevention challenges presented by mobile wallets, the demand generated by hot new products, the growth of digital goods, and extraordinary liquidity of eGift cards.
Sources of Friendly Fraud Chargebacks
Next, Monica Eaton-Cardone, COO and Co-Founder of Chargebacks911 began the discussion about friendly fraud by tracing the origins of the name back to the term “friendly fire.” She defined friendly fraud as a chargeback that is illegitimately filed, and is an enemy of everyone. It creates cost for both the issuer/acquirer and the merchant.
Sources of friendly fraud include buyer’s remorse, family and friends (e.g., a child makes purchases in Candy Crush app without parent’s permission), expired refund period, and out-and-out shoplifting. There are a number of factors facilitating the increase in friendly fraud:
- It’s easy. Today’s online banking makes filing a chargeback as easy as tapping a button in an app.
- Reduced scrutiny. Card issuers want to minimize customer service expenses and retain customers, making them less likely to investigate or challenge chargebacks.
- Free stuff. 50% of the time, consumers will claim a second friendly fraud chargeback within 60 days of the first successful friendly fraud chargeback.
Polling Question #2
ClickBank Case Study
Finally, Jennifer Johannsen, Director of Risk and Compliance at ClickBank recounted her recent success in fighting friendly fraud. ClickBank is a top 100 Internet retailer serving 6 million entrepreneurs in 190 countries with over $4 billion in sales to 200 million customers since 2005. With a generous 60-day refund policy, ClickBank was experiencing a high number of friendly fraud chargebacks. However, once ClickBank implemented the Chargebacks911 integration within their Kount Complete fraud prevention system, they experienced dramatic success and significant ROI:
- Reduced losses related to friendly fraud
- Initial results showed over $400,000 recouped in the first 90 days
- Representment success rate above 70%
- No increase in operational cost, headcount, etc.
- Changed bank behavior (less likely to approve chargebacks)
We’ve discussed only a few key parts of the webinar in this article. Discover all the ways in which online businesses can minimize the significant losses and costs associated with criminal and friendly fraud. Click here to watch a recording of the webinar: “Finding Revenue in Friendly Fraud Losses.”