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Mitigating friendly fraud in a recessionary environment

Author: Serpil Hall


The current global recessionary environment will intensify friendly fraud challenges in 2023. Merchants and financial institutions will need to step up their fraud prevention game to protect their organizations. While all types of fraud, from RAT to Bot attacks and scams, tend to increase during a recession, there’s a more complicated type of fraud that’s harder to prevent – friendly fraud.


Friendly fraud increases in a recession because increased unemployment, higher costs of living (inflation), and stress can drive regular people to commit fraud out of desperation. We call these consumers the “opportunistic fraudster”. They’re not a career criminal, but financial hardship is a big motivator.

The fraud triangle is made up of three components – Motivation, Rationalization, and Opportunity. While opportunities are often there, regular people typically won’t entertain the idea of committing fraud because it’s not in their nature. However, when jobs are lost, and costs are steadily increasing the motivation (financial pressure) is created. And when people are stressed, desperate for money, and feeling hopeless it’s a lot easier to rationalize friendly fraud. “It’s a huge company, they won’t miss it.” “I lost my job, I deserve a break.” “My kids need new shoes, and I can’t afford them.” These are all ways opportunistic fraudsters may justify behavior they normally wouldn’t consider.

What is friendly fraud?

While some fraud vendors say friendly fraud is simply chargeback fraud, others say it’s accidental fraud – for example when another person in the household makes a purchase but the cardholder isn’t aware and thinks they’ve been a victim of fraud. Both are too simplistic because there are many variations of friendly fraud. In short, it refers to fraud committed by a legitimate or known customer, rather than a criminal or imposter. No matter what you call it, the current recessionary environment will spur an increase in all types of friendly fraud across many industries. Here are some examples to watch out for:

BNPL (Buy Now Pay Later)

BNPL fraud has been increasing as fraudsters take advantage of the new, less rigorous credit option, but it will also increase on the friendly fraud side. Consumers are more cash-strapped and looking for ways to cut corners – and costs. The increased volume of BNPL also makes it harder for organizations to detect fraudulent accounts. Opportunistic fraudsters will also take advantage of this opportunity by creating accounts to purchase items they have no intention of paying for, claiming non-delivery of items, or simply not making additional payments once they’ve received the items and experience a job loss or financial hardship.

Non-received merchandise

Also referred to as retail chargeback, or payment dispute, this friendly fraud occurs when a customer falsely reports they were charged by the credit card company, but the item wasn’t delivered, or simply denies the transaction was legitimate.

Never returned items

Like non-received merchandise, the customer tells their credit card issuer they returned the item to the merchant, but a refund wasn’t processed. Of course, they never actually returned the item, so they get to keep it without paying for it.

Counterfeit return items

Arguably the most passive form of friendly fraud, this is when a customer claims the item purchased doesn’t match the online description and now they don’t want it. Although they may have to return it, in some cases the merchant will tell them to keep it – especially with low value items.

Credit card compromise

A customer may even go so far as to say they don’t remember making the purchase so their credit card must have been compromised. This is a drastic step as it requires cancelling the card and getting a new one, but it’s often used to commit friendly fraud on multiple items, or high value purchases. It’s estimated that up to 40% of credit card fraud claims are found to be unsubstantiated. This is not a small problem.


Although their credit will be impacted, some desperate consumers will simply disappear without paying their credit card or other bills because they can’t afford to – especially if they’ve lost their job.

Loan fraud

During a recession people will be more apt to falsify information to qualify for loans. A combination of high interest rates and the increased need for money creates the motivation for consumers to “cheat” on their application for a mortgage, auto, or personal loan.

Insurance fraud

Again, as people become financially stressed they may turn to unethical sources of money. Whether falsifying information on their insurance application to get a better rate, or filing a false claim for damages, friendly fraud often increases for insurance companies during a recession.

How to mitigate friendly fraud

There’s no “one-size-fits-all” type of fraud prevention for friendly fraud challenges. Because they're legitimate customers, it's harder to detect and arguably more delicate (no one wants to alienate customers, especially in a recession!). Businesses need to use a combination of best practices, fraud prevention tools and platforms, chargeback management services, and payment solutions to reduce friendly fraud risk in the best way possible.

To fight friendly fraud, businesses need to know their customers really well. This means building a complete digital footprint, or identity graph, using all digital interactions (both transactional and non-transactional), both pre and post login. A layered fraud solution that provides a true 360-degree view of the customer is the best possible fraud prevention tool.

A first-party identity graph captures and contextualizes data from all digital properties (think mobile app, website, POS, etc.) to build a digital identity of both known and anonymous visitors. Persisting this identity across channel, device, domain, and over time enables organizations to understand their customers’ behavior from start to finish – not just when they’re logged in.

As part of a fraud data platform, this ID graph not only identifies customers in terms of legitimate vs. fraudster, it compares the visitor to themselves to detect anomalies and flag inconsistencies in behavior. Transactional fraud monitoring can’t do that – it only looks at individual transactions without any context. Layering behavioral biometrics into the ID graph means you (or more specifically, your fraud prevention solution) can compare behavior over time at an individual level. So, for example a customer who routinely pays off their store credit balance in full every month may suddenly be delaying payments, claiming non-received items, and then applying for a BNPL account. Viewed as individual interactions, none of these would raise a huge red flag. But taken in context, it may paint a picture of someone experiencing financial stress and looking for an opportunity to get ahead. Similarly, an insurance provider who only tracks data after their customer is logged in won’t know that an existing customer is running quotes on their sister sites and changing key details to get a lower rate.

A platform fraud solution can capture all of this data, and resolve digital identity to deliver a comprehensive customer profile that facilitates more accurate, extensive, and real-time fraud prevention.

To combat friendly fraud in retail, there are several additional best practices merchants can apply:

  • Collect and store detailed order shipment and receipt data.
  • Have an effective mechanism to keep shipping/postage records on file
  • Document every touchpoint of the order process and the customer journey
  • Have tight measures to ensure there is proof that customers signed for packages
  • Ensure they receive clear billing descriptions
  • Apply AVS (Address Verification Services) checks to ensure the credit card billing address matches the address the card’s issuing bank has on file for the card
  • Require CVV (Card Verification Value) checks to confirm the customer has the card in their possession prior to making an online purchase

This detailed data can then also inform the fraud data platform to reconcile purchases, claims, activities, and interactions against the customer identity graph. In fact, the fraud data platform should work in tandem with any and all fraud detection, payment, chargeback management, and risk management solutions at their disposal. The more data available, the more complete the identity graph, and the quicker friendly fraud can be prevented.

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