Credit Card Fraud: The Types and Techniques to Detect Them

Credit card fraud is a growing problem that affects individuals and businesses worldwide. The most recent research from McAfee about cybercrime and fraud indicates that 0.8% of the global GDP is lost to fraud every year.

Fraudsters are constantly finding new ways to steal credit card information and use it to make unauthorized purchases. For most cases, once the legitimate cardholder realizes that their card has been used by a fraudster, the damage is done.

Because of that, the impact of credit card fraud can be devastating, causing financial hardship and damage to credit scores. That’s why fraud detection and prevention are essential for protecting both consumers and businesses.

The different types of credit card fraud

There are several types of credit card fraud, each with its own unique characteristics and risks to the participants of e-commerce activities.

Counterfeit fraud

This is one of the most common types of credit card fraud at the moment. It involves creating a fake card that has the looks of a legitimate one. Fraudsters may obtain credit card information through skimming devices or phishing scams and use that information to create the counterfeit cards. These fake cards can be used to make purchases or withdraw cash without trouble.

The technology used for producing counterfeit cards is growing more and more sophisticated, which poses a real challenge to banks and merchants.

Lost or stolen card fraud

This occurs when a thief steals a credit card or the card is lost, found by fraudsters and then used for unauthorized transactions. In some cases, the thief may sell the stolen card information to other fraudsters on the dark web.

For most cases, the cardholder would have realized that their card has been stolen and notified the bank to invalidate it. However, there are also instances where fraudsters have access to the lost card before the cardholder can take action.

Account takeover fraud

Account takeover fraud happens when a fraudster successfully gains access to a victim’s credit card account and makes unauthorized purchases. This type of fraud often involves social engineering tactics such as phishing scams or malware attacks to infect the victim’s device.

Friendly fraud

Friendly fraud occurs when a cardholder disputes a legitimate charge, claiming that it was unauthorized or that the goods or services were not received. While not technically fraud, friendly fraud can be costly for businesses as they may be forced to issue refunds or chargebacks.

This tactic is often used by customers who want to receive the goods bought, but also to claim the money back. It is extremely hard for merchants to tell if a chargeback was done based on the true circumstance, or was it initiated as a means of financial gains for the customer aka friendly fraudster.

It’s important for consumers and businesses to be aware of these different types of credit card fraud and take steps to prevent them. This includes being vigilant about protecting credit card information and reporting any suspicious activity to the card issuer as soon as possible.

Techniques to detect credit card fraud

To be more proactive in detecting credit card fraud, many companies choose machine learning and artificial intelligence as a means of support. These technologies can analyze vast amounts of data and identify patterns that may indicate fraudulent activity.

Join us in learning more about the different techniques available in fraud detection solutions and how they work.

Anomaly detection

This detection technique is especially used in fraud detection. It involves identifying unusual transactions or those outside the typical pattern of a cardholder’s behavior (based on a huge amount of historical data points).

For example, if a cardholder usually uses the card to make small purchases at local stores but suddenly makes a large purchase at an online retailer in another country, this would be seen as unusual and be flagged as an anomaly.

Rule engine (rule-based) detection

Detection based on rules involves pre-setting specific rules or thresholds for transaction behavior, depending on the adopter’s needs. If a transaction meets certain criteria, such as a purchase amount exceeding a set limit or a card used in a different location from the cardholder’s usual location, it can be flagged for further review instead of an instant pass.

Predictive Modeling

Predictive modeling involves using historical data to build a model that can predict future fraudulent activity. This model can be used to identify transactions that are likely to be fraudulent and flag them for review.

At HiTRUST, our risk based authentication solution adopts machine learning algorithms to analyze millions of transactions in real-time and enhance the system’s capabilities to detect suspicious activities over time. Once being flagged, the transactions can be further reviewed to either accept or reject, based on the analyzed data and actual circumstance.

Challenges and Limitations

While machine learning and AI are powerful tools for detecting credit card fraud, there are also challenges and limitations. One challenge is the issue of false positives, where legitimate transactions are flagged as fraudulent. This can cause frustration for consumers and can be costly for businesses if they are forced to issue refunds or chargebacks.

The remedy for this continuous improvement and training of the AI and machine learning tools, based on historical data, to ensure the lowest possible false positive rate.

Data privacy concerns are also a limitation, as fraud prevention systems require access to sensitive personal information. It is HiTRUST’s top priority when developing systems and products to serve our client as we adhere to strict industry security protocols and process the retrieved information responsibly and protect them from unauthorized access.

Fraudsters are constantly evolving their tactics, which makes it more challenging for fraud detection systems to keep up. This means that companies must continually update and improve their fraud prevention systems to stay ahead of new threats.

To address these challenges, companies are investing in advanced technologies such as biometrics to improve fraud prevention systems. Biometrics such as fingerprint or facial recognition can be used to verify a cardholder’s identity, while blockchain can provide secure and transparent record-keeping for transactions.

If you are interested in learning more about the application of biometrics in fraud prevention, contact us today to be consulted with our newest solution HiFIDO.

Conclusion

In conclusion, credit card fraud is a significant problem that affects both individuals and businesses everywhere. The impact of fraud can be severe, causing financial losses and damage to credit scores. That’s why fraud detection and prevention are crucial for protecting consumers and businesses.

Looking to the future, we can expect to see even more advanced technologies being used for fraud detection and prevention. Biometrics is just an example of emerging technologies that could help to improve security and mitigate fraud risks.

Ultimately, the fight against credit card fraud requires a collaborative effort from all stakeholders, including consumers, businesses, and financial institutions. By working together and leveraging the latest technologies, we can better protect ourselves against the growing threat of credit card fraud.