Credit card transaction fraud continues to climb to new heights

Online shopping is on the rise as more of us stay at home and let our credit cards do the walking. Keeping pace with that trend is an unfortunate increase in credit card fraud.

It’s no surprise, really. According to Forbes, online fraud has been a growing problem for the past few years. And now, as consumers and businesses adapt to the worldwide pandemic and make more credit card transactions in the card-not-present (CNP) space, the resulting uptick in online shopping and ecommerce has opened up an even bigger playground for fraudsters to try out new tricks.

A 2018 study by the Federal Reserve showed the amount of card-present fraud in the U.S. declined from $3.68 billion in 2015 to $2.91 billion in 2016. Unfortunately, during the same period the loss from CNP fraud jumped from $3.4 billion to $4.57 billion. Another study by Javelin Strategy & Research revealed that CNP fraud is 81 percent more likely to occur than card present fraud.

Every fraudulent transaction leaves its mark. But while all may seem dire, ecommerce ventures and businesses that take online payments can take heart when it comes to credit card fraud detection and protecting data.

Specifically, a data mining process called anomaly detection is helping stem the tide of credit card fraud, using behavioral analysis to identify and predict unusual fraud pattern behaviors in data. With the use of anomaly detection algorithms, emerging technologies are helping to create a more impenetrable firewall against credit card fraud in both the card present and CNP environments.

Related: Guarding Your Digital Doorstep: Data Security Tools and Fraud Prevention

When it comes to credit card fraud, everyone pays the price. Consumers and the businesses that serve them all suffer from fraudulent activity. And the costs can be staggering. Global financial losses related to payment cards are estimated to reach $34.66 billion in 2022. Everyone along the payment lifecycle is impacted by a fraudulent transaction—from the consumer who makes purchases in person or online using a credit or debit card to the merchant who finalizes that purchase.

So, how does credit card fraud occur? Let us count the ways.

First, there are the more common types of fraud like chargebacks, accidental or intentional friendly fraud, credential stuffing and account takeovers. Physical credit cards also pose a risk, as they can be lost or stolen and duplicate credit cards can easily be made with the use of a skimmer.

Identity theft is another gateway. Personal information, such as name, address and birthday, can be pilfered and used for fraudulent purposes, such as stealing someone’s identity and opening unauthorized accounts. This increases the impact of credit card fraud on victims, making them vulnerable to losing what they own or earn as well as taking an emotional toll.

Identify theft also has the potential to greatly increase a retailer’s liability. The cost for each dollar lost to fraud increases astronomically when expenses for chargeback fees, restocking merchandise, investigations, legal prosecution and security are added on. From 2016 to 2019 alone, the cost of each dollar lost to fraud rose from $2.40 to $3.13.

Whatever the manner of theft, every year, businesses across the board—the mom-and-pop stores, restaurants and ecommerce, not to mention financial institutions—are hit hard, and losses from credit card fraud keep growing.

Technology introduced in 2015 called EMV (Europay, Mastercard and Visa) refers to the small computer chips in credit cards that store data and interact with point of sales systems. Entering a credit card into a point-of-sale reader makes it much harder to create a counterfeit plastic card from credentials stolen by swiping a magnetic strip.

While EMV credit card protection is an effective deterrent for point-of-sale fraud, fraudsters have in turn shifted their focus to CNP fraud. Because CNP sales are often less secure, a fraudster needs only credit card data to make purchases. This data can be found through hacking, phishing, or social engineering.

To guard the proverbial henhouse against such substantial losses, businesses can now turn to robust fraud detection tools that use anomaly detection techniques. In the CNP space, these solutions can be very effective in reducing risk by detecting fraudulent activity online.

Anomaly detection—also known as outlier detection method—in credit card fraud is all about analyzing patterns and data sets. It uses machine learning—an application of artificial intelligence (AI) that helps machines learn and improve from an experience without being specifically programmed.

Analysts use anomaly detection in data mining to identify data that don’t conform to an expected pattern or model. This data shows how the anomalies might have occurred. Fraud analysts apply these techniques to credit card transactions, because they can recognize changes in normal behavior and identify potentially fraudulent activity.

As credit card fraud loss skyrockets, this technology is helping to protect businesses that offer card present and CNP transactions—not to mention banks and other financial institutions—by detecting fraudulent activity before those transactions are even completed.

Adding anomaly detection services to existing security measures is a great way to reduce the risk faced by online and mobile payments banking services for retail and small business clients. 

There are a variety of old-school credit card fraud detection techniques and technologies that still play a valuable role in credit card fraud prevention, such as CVV verification (that three- or four-number code on the back of your credit card), address verification, geolocation, velocity limits and fraud scoring. Businesses can also fortify their security posture in fraud detection with these simple techniques:

• Using centralized and consistent software distribution and patching practices
  
• Using a layer security approach
• Using applications and system logs to spot anomalies

With millions of credit card transactions taking place every day, it is impossible for humans to fully understand what is going on and to identify patterns, much less respond in a timely manner. Anomaly detection algorithms, however, have the ability to look at a wide variety of information when evaluating that data. The more data points, the more accurate the decision-making can be.

Much like humans learn from their experiences, machine learning uses outlier detection algorithms to evaluate and learn from past transactions and to apply that information to future analyses—making the machines smarter—and more adept at fighting fraud over time. The differences between the human and machines, of course, is the speed with which that data is processed and the sheer volume of information that can be managed.

Machine learning techniques used in anomaly detection include supervised and unsupervised methods—and each works in a different way. Common supervised learning methods include logistic regression, neural network, support vector machine and random forest of trees. Common unsupervised learning methods include self-organizing maps, k-means, Based Spatial Clustering of Application and Noise (DBSCAN), kernel density estimates, one-class support vector machines and principal components analysis. Both types of machine learning and their corresponding fraud detection methods have a place in making a fraud case.

As fraud detection analysts work to stem the tide of credit card fraud, these evolving technologies in artificial intelligence are getting a lot of attention for their ability to detect anomalies and point the way to suspected fraudulent activity.

In addition to requiring customers to provide more information to verify their identity, businesses can monitor the transactions made with their payment services. This can include using technology to accurately watch self-service stations, point of sale systems, and the security of mobile apps and websites.

Data security software for businesses can also be a great way to use technology to prevent attempts at theft such as card tumbling. Ideally, this software creates an accurate picture of your company’s average customer, and uses your customer’s purchasing info to spot fake customers and fake credit card numbers. Through a combination of machine-learning and profiling, security software identifies unusual behavior and notifies you right away—preventing future fake transactions and keeping your customer data safe.

Competent data security software is also both flexible and secure. It protects your consumers data while helping them move smoothly through the buying process without jumping through too many hoops.

Learning how to protect customer data is crucial for the success of any business, no matter the size. So do your research on fraud prevention software—it's vitally important in the process of finding the protection that’s right for you and your business.

Some of the burden to stop fraud rests on credit card users. Common sense practices help keep personal data–and credit cards–-safer. Various consumer advocacy groups and the Federal Trade Commission are working hard to educate the public on the best ways to guard their identity and prevent credit card fraud.

But companies also play a role in protecting consumers—and their business. With every transaction, businesses have a responsibility to make sure their credit card fraud detection practices are effective. Businesses must have the right partner and the most effective fraud detection software to keep data secure during each transaction. To attain that optimal level of data security, it’s important to understand the role of behavioral analysis, data analytics and machine learning in the ongoing battle to mitigate credit card fraud.

And while credit card fraud is on the rise, fraud analysts and the businesses and consumers that depend on them can take heart knowing that new technologies are making it harder for criminals to succeed. The artificial intelligence and the machine learning technology tools of today continue to evolve to meet the challenge of protecting data and keeping up with the changing landscape.

Understanding the risks and investing in anomaly detection tools with machine learning capabilities is helping payment processors, financial institutions and other business lessen their losses and protect themselves and the consumers they serve.

Putting security at the forefront of every business decision is the best way to ensure that you can continue to compete, grow and scale your business with every credit card transaction.