Fraud Strategies

What I’ve Learned: The High Cost of Friendly Fraud (or, How Normal People Will Scam You to Save a Buck)

August 31, 2018

The path that led me to the fraud industry was, like so many others, an indirect one.

After a few years of doing underwriting and merchant support for a subprime leasing company,

I was looking for a change of pace. As a result, I wound up on the customer support team for a well-known eCommerce site.

Before long, there was an opening in the fraud department. With nothing to lose, and an affinity toward “othered” groups within an organization, I hopped on it.

I’m glad I did.

Because being a fraud analyst is the most fun I’ve had in my adult life.

The fact is that even though fraudsters will always be the same, fraud itself is always changing.

As someone who has always been good at finding loopholes in a system, I was still surprised by a few things that are commonplace in the fraud world.

Of course the seasoned fraudster is hard to detect

They are much more precise and methodical. Fraudsters have to move fast and will only endure so much friction before moving on.

In contrast, I had no clue how far normal, everyday people would go to “beat” the system in order to save a buck or two.

When I say, “normal,” I mean the everyday consumer who will go to extreme lengths to exploit a system, without necessarily committing “fraud” per se

The industry has dubbed this “friendly fraud,” though merchants know it’s anything but friendly.

It’s remarkable how long these friendly fraudsters will feign ignorance of any wrongdoing. One friendly fraud case I worked involved a customer who’d opened several credit cards to purchase $20k+ of high value merchandise from our website. Items such as designer bags and shoes, electronics, and luxury perfumes.

Some behaviors, taken alone, aren’t inherently suspicious

In this case, however, all of the transactions were made within a couple of days, each right after the other.

Our team decided to contact the customer to ensure everything checked out. After speaking with them, we determined that these purchases seemed legitimate.

Even so, we kept an eye on their account, just in case anything weird(er) occured. A few weeks passed, the customer has received all of their items, and we got a heads-up that one of their purchases had been returned. Claimed they had received the wrong item.

Things happen. Rarely do they happen twice…or three times

At the fourth instance, we started to more closely monitor the account. The customer then went on to return Every. Single. Order.

They returned each time, claiming to have received the wrong item. At this point, the scam was plain as day; we shut the account off and blocked refunds.

But this friendly fraudster neither gave up nor disappeared. They fought tooth-and-nail for nearly six weeks, filing multiple service chargebacks after our refusal to refund.

These “normal” everyday people can represent much more work than a routine fraudster

Even though fraudsters are more dangerous, cases like this draw much needed time and resources from your team, affecting the bottom line.

Fighting fraud on all fronts requires a layered system that monitors for this type of behavior. Fraud modeling via machine learning can identify patterns of chargeback behavior and deliver meaningful insights through predictive scoring.

The result is a clearer understanding of customer behavior and transactional history, which, in turn, can help determine whether a chargeback was genuine or a deeper sign of repeated abuse.

Doing so will save a lot of time and energy in the long run, not to mention money. The cost of introducing additional security measures is much lower than the losses caused by friendly fraud.


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