Credit Card Fraud Detection: What is It, How It Works and Its Importance

Credit card fraud is on the rise and, according to the Nilson Report, it’s projected to reach a staggering $38.5bn by 2027. So how do you detect credit card fraud? And why is it so common? Find all the answers below.

What Is Credit Card Fraud Detection?

Credit card fraud detection is a set of methods and techniques designed to block fraudulent purchases, both online and in-store. This is done by ensuring that you are dealing with the right cardholder and that the purchase is legitimate.

When it comes to identifying the cardholder, credit card fraud detection relies on authentication techniques such as MFA (multi-factor authentication), 3DS, biometrics, and OTP (one-time passwords).

However, it is also possible to detect credit card fraud by looking at anomalies in the transaction. For instance, an IP address could point to a suspicious geolocation. Similarly, a device with a never-seen configuration of software and hardware could raise red flags. 

Depending on the kind of detection tools your company uses, you may answer questions about the cardholder identity and intention in real-time or retroactively. In that sense, credit card fraud detection can be either a payment fraud prevention measure or a way to investigate previous transactions.

How Does Credit Card Fraud Detection Work?

Credit card fraud detection uses advanced technologies, algorithms, and data analysis to identify and prevent fraudulent transactions. Financial institutions employ real-time machine learning models trained to recognize patterns and anomalies indicating potential fraud, such as sudden large purchases abroad or multiple transactions in a short period. The system assesses risk based on transaction history, location, and user behavior. Suspicious transactions may be halted, and the cardholder is often contacted for verification.

In addition to machine learning, credit card fraud detection also relies on rule-based systems, which use predefined rules to identify potential fraud. These rules can include simple conditions like exceeding a spending limit or more complex ones involving correlations between different data points.

Financial institutions use real-time monitoring and behavioral analytics to continuously track cardholder activities. Advanced techniques like biometric verification, tokenization, and multi-factor authentication enhance security. Integrating these technologies allows credit card companies to effectively detect and prevent fraud, protecting both cardholders and financial institutions.

How Do Fraudsters Get Credit Card Numbers?

It’s easier and cheaper than you might think to acquire credit card numbers online. There are thousands of dedicated marketplaces both on the clearnet and darknet. In fact, a report by The Guardian claims you may find prices as low as $17 per card. Here is how they become available:

• Theft: criminals steal or gain access to physical cards and use them.
  
• Skimming and cloning: making unauthorized copies of credit card details with special equipment known as a skimmer that can be installed on top of a legitimate card reader. The card numbers are then reused for a cloned card.
  
• Account takeover: when a fraudster gains unauthorized access to someone else’s account. With a credit card linked to it. The problem is even worse if the account acts as an ewallet (BNPL, crypto or neobank account, for instance)
  
• Phishing and social engineering: taking advantage of people in order to extract key information. Credit card details may be stolen by sending emails or SMS, or by deploying entire fake online shops.
  

Infiltrating legitimate online stores: criminals inject scripts on existing online store websites, effectively a form of online skimming, which can be done with sophisticated tools such as MageCart.

How to Detect Credit Card Fraud

Given the multitude of ways fraudsters obtain credit card details, businesses must employ robust tools and strategies to detect compromised information as found below:

Card Security Features

Credit card networks have developed a number of security features designed to prevent fraudulent purchases. These include:

• Address Verification Service (AVS): A service designed to confirm the cardholder’s identity by looking at their registered address. The address is confirmed against the bank’s records.
• 3-D Secure (3DS): A security layer that prompts users to enter a code to complete a purchase. Different card operators offer the service under different names, such as Visa Secure (Visa), SecureCode (Mastercard), or SafeKey (American Express).
• CVV: A CVV, or Card Verification Value, is a three-digit number located on the card. It is designed to verify that the card is indeed in possession of the customer at the time of purchase.

It’s worth noting that these card security features add a certain level of friction. This is why Amazon, for instance, doesn’t ask for a CVV at the checkout stage, as the company has determined that it slows down the process, impacts the customer experience negatively, and has other defenses in place to make sure it is in fact you logging in. 

Risk Scoring

Risk scoring / fraud scoring is a standard risk management method, which uses rules to gauge risk. They help people make educated guesses about a certain user action. For instance, you can use a risk score to determine whether a payment should be allowed on your site or not.

For credit card fraud detection, risk scoring tends to rely on heuristic rules, also known as heuristics. They are shortcuts designed to deliver quick decisions using if-then logic. For example:

• If the IP address points to a different location from the shipping address, then the risk score should go up by 1 point.

When the risk score reaches a certain threshold, an automated system can decide to block or allow the transaction.

A more advanced form of risk rule is called a velocity rule, which looks at data points within a certain time frame to score human behavior. For instance:

• If the user fails to enter the right password five times within one minute, then the account should be temporarily blocked.

By combining multiple risk rules, you can create decision trees that allow for more accuracy in the scoring system.

Machine learning solutions can help improve the accuracy of risk scoring, either by providing an additional suggested risk score based on all the data fed into it but without any explanation (blackbox machine learning) or by suggesting new rules to implement with clear logic and human-readable explanation, based on previous transactions (whitebox machine learning). 

Whether you prefer a whitebox or blackbox system depends on your ability to monitor credit card detection. Companies with fewer resources may prefer relying on an out-of-the-box solution. Those with a dedicated risk management team tend to favor whitebox systems, as they allow for more customization and flexibility. 

You can read more about risk rules and best practices in our post on card not present fraud prevention.

Digital Footprinting

How can you verify someone’s online identity before a transaction? You might ask for ID documents or use video verification. But is it necessary for a low-value transaction, and is it worth the additional friction in the customer experience?

This is the core challenge for companies needing to detect fraudulent credit card payments: verifying customers without adding friction. Too many hurdles can drive customers away, leading them to choose your competitors instead.

This is why digital footprinting, aka. collecting and analyzing customers’ online trails, including social and digital profiles, IP addresses and device information is a powerful and efficient way to confirm an identity. It acts as an invisible security layer, enhancing information from data you already have at hand, like an email address. Consider these methods:

• Device fingerprinting: Determine if the user has previously connected to your site with the same device or if they are attempting to spoof their connection details.
• IP analysis: Check if the connection originates from a VPN, a suspicious proxy, or a Tor node.
• BIN lookup: Verify if the payment card type is appropriate. For instance, is it plausible for a customer in APAC to use a prepaid card?
• Social and digital profiling: Investigate if the phone number or email address is linked to a social media account. Does the user profile align with the transaction details?

The goal is to build a user profile without asking the customer for additional information. This enriched data feeds into your risk scoring system, helping to identify potential credit card fraud.

Additionally, digital footprinting logs more information about users, which can be useful in disputing chargebacks and presenting evidence of friendly fraud.

How SEON Does Credit Card Fraud Detection

SEON’s fraud detection software offers several features to enhance credit card fraud detection. These solutions provide comprehensive insights into the payment, cardholder and their alternative data, such as social media presence and device information:

• Card BIN lookup: This feature verifies the validity of the card, identifies the issuing bank, and determines the country of origin.
• Digital footprinting: You can check for an online presence by entering the shopper’s email address or phone number. A lack of social media presence may warrant further investigation.
• IP analysis: Gain detailed information on how the customer connects to your checkout. This includes flagging harmful IPs, VPNs, Tor usage, or suspicious DNS activity.
• Device intelligence: Spot anomalies and detect whether the customer uses a suspicious hardware/software setup or connects from an unknown device.
• Custom and industry-specific risk rules: SEON provides pre-loaded risk rule templates tailored for online stores, BNPL and other industries. These templates enable automated risk management and help immediately reduce credit card fraud rates. You can also customize these rules to suit your specific needs.
• Integration flexibility: SEON can be utilized in various ways to address your credit card fraud challenges. You can add an extra layer of data intelligence, connect via API for full automation, or use a plug-in for platforms like Shopify.

The goal is to equip you with powerful tools to control credit card fraud rates and minimize chargebacks while maintaining a balance between security and a frictionless payment experience.

FAQ

Sources

• Juniper Research: Online Payment Fraud
• The Guardian: Stolen credit card details available for £1 each online
• Nilson Report: Card Fraud Losses Dip to $28.58 Billion
• Magecart: A Deep Dive Into Magecart