Credit card fraud has always been a serious issue, and it’s only gotten more serious in recent years. This type of fraud is now the most common form of identity theft, with more than 114,000 cases recorded in 2023 alone. 

The good news: credit card issuers are aware of this problem and are doing what they can to stop it, and there are a few things you can do as well.

Want to know more about credit card fraud detection? Read on to learn how credit card fraud usually manifests and what a solid fraud detection strategy should look like.

Common Fraud Trends in 2024

Each year brings some new transaction fraud trends, and 2024 hasn’t been an exception. Here are some trends you should be aware of in order to protect your business and customers:

• A2A/P2P fraud: Direct payment methods like account-to-account (A2A) and person-to-person (P2P) are now a common target for fraudsters, who can steal information and use it to illegally acquire goods.
• Brushing scams: A brushing scam involves the victim receiving unordered (cheap) merchandise and getting fake reviews posted in their name. This often means fraudsters have your personal information and may use it for future scams.
• Pig butchering: This scheme revolves around duping victims into investing major sums of money. The scammers usually cultivate a sense of trust by doling up interest payments, only to pull the rug once the victim invests their own money.

How Does Credit Card Fraud Detection Work?

The key purpose of every fraud detection strategy is to pinpoint suspicious activities promptly and accurately. For this purpose, most businesses use outlier models. These statistical tools can identify transactions that deviate from the expected pattern of behavior. For instance, erratic spending and unusual transaction amounts are both clear red flags for outlier models.

The key to using outlier models is to calibrate them to detect genuine fraud while teaching them to ignore valid purchases. This is where anomaly detection systems come in. These systems use sophisticated algorithms to go through vast amounts of transaction data. This, in turn, allows you to spot anomalies, such as a flurry of transactions, more consistently.

Another good method for identifying fraud is using predictive modeling. A predictive model gauges a transaction’s risk by looking at factors like purchase amount, time of purchase, location, and so on. Much like anomaly detection systems, predictive models can adapt to evolving fraud patterns, helping them stay effective over time.

The bottom line: effective credit card fraud detection is all about balance. You need to do your best to minimize the risk of fraud while keeping the processing experience hassle-free for your users. Modern technologies like the ones mentioned above can help you find this balance.