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Key Takeaways
Losses from fraud have increased by nearly fivefold to $15.9 billion since 2020, according to Federal Trade Commission (FTC) data. While this total dollar amount may attract attention, the share of fraud cases where actual financial loss resulted increased from about 27% to 38%.
The number of fraud attempts can be filtered, but increased approvals point to a breakdown.
Bankers told lawmakers that “fraud is growing faster than defenses.” As such, the challenge is managing the fraud that does occur.
Fraud Tactics Are Becoming More Effective
The FTC data shows a clear change in outcomes. Criminals are refining their tactics, instead of just increasing volume.
From an issuer’s perspective, this increases the likelihood that fraud will be viewed as a “decision” for issuers. Many types of scams use social engineering to obtain approvals from customers for transactions that may have appeared legitimate when authorized by the customer, but ultimately resulted in fraud, blurring the line between fraud and liability.
The clearing process occurs before a determination is made about whether the transaction was fraudulent. That’s why traditional controls, such as those used to detect credit card fraud, are less effective with electronic funds transfer-based schemes.
Fraudsters Can’t Resist Credit Cards
Cards continue to play an important role although fraud is shifting toward bank transfers and crypto, including about $5.7 billion in investment and crypto scams.
Cardholders use cards to fund these types of transactions by buying crypto and funding their digital wallet prior to sending the funds. That means that many of these losses begin as authorized card activity, leaving issuers exposed to downstream losses even when cards are not the final payment rail.
The fraud process begins with a card transaction, but fraud may then occur by means of the crypto/wallet, and potentially will be returned to the issuer when the consumer files a dispute or account takeover complaint. In many cases, once the fraud has been detected, there is no longer any way to recover the lost money.
Liability Pressure Is Quietly Building
As losses grow, consumer expectations are changing. Consumers expect protection — and reimbursement — even when they approve a transaction, despite saying they trust banks most to protect against fraud.
More lawmakers than ever before are focused on what is being done regarding customer reimbursement for fraudulent activity. Banks continue to express their inability to bear the cost of absorbing the risk of fraud.
But much of this fraud begins with an individual using a social media platform or messaging application and then intersecting with a bank when they make a payment based on the scam. This dynamic creates conflict.
Should fraud continue to exceed defense capabilities, consumer expectations will begin to shift from preventing fraud to who bears the liability. Pressure to provide quicker refunds will also grow. The question remains regarding who will absorb the losses — and how much longer will existing systems be able to fund such costs?
Fraud Is Now a Customer Experience Challenge
The way fraud operates is changing thanks to social engineering. In the words of regulatory agencies, scammers now “follow consumers where they are” to build confidence via email, text messages, or social media before they ask for money from the consumer.
An increase in friction will reduce fraud but decrease customer satisfaction. And reducing friction will improve approval rates but create increased loss.
Each decision involves trade-offs between risk, cost, and customer experience.
What Comes Next for Issuers
If loss trends continue, the models currently being used to assess fraud risk will be put at a disadvantage.
Credit card issuers could potentially face increased expenses when issuing credit cards and decreased profit margins. In response, issuers may choose to either increase the price for issuing credit cards, limit access to credit, or lower the amount of risk they are willing to take on by adjusting their fraud risk assessment models.
