Why CNP Fraud is a Growing Risk

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A May 2020 report stated 55% of credit card purchases took place via card not present (CNP) transactions. Unfortunately, this increasing trend has also resulted in more CNP fraud.

As a retailer, you must include the option of CNP payments. So, how do you protect yourself? Continue reading to increase your knowledge about this problem and find solutions.

What Is CNP Fraud?

CNP fraud describes illegal purchases using stolen credit card information. This takes place via telephone or internet purchases without a card physically present. CNP fraud is expected to cost retailers about $130 billion between 2018 and 2021.

CNP Fraud Strategies

The first step in fraud prevention is understanding how criminals work. If you’ve never experienced CNP fraud, don’t assume you’re safe. Letting your guard down lets them in.

Thieves make purchases with stolen credit cards and sell the information on the Dark web. They test stolen cards by making small purchased to see if their “plan” works. Criminals take advantage of “address verification services” to divert deliveries to steal products.

Hackers gain access through online skimming. When users log onto unsecured WiFi networks, thieves steal passwords and credit card information. They can also get phone numbers and order history to mimic the customer.

Chargebacks represent a significant problem. Companies pay bank fees and penalties and lose revenue, merchandise, and their reputation. Businesses suffered a 68% monthly increase in chargebacks between February and April 2020.

Chargebacks occur when a customer or criminal requests a refund for a product. Intentional fraud involves requesting a refund for an item the thief didn’t buy.

Fraud Prevention Strategies

Once you understand how cybercriminals attack, you can develop prevention strategies. Artificial intelligence (AI) with machine learning offers accurate and quick fraud detection. Instead of following set rules, it uses analytics to “learn”.

Supervised machine learning algorithms evaluate normal transaction behavior. Through self-learning, they detect and flag abnormal activity and store this new data.

Unsupervised machine learning looks for possible suspicious behavior. Instead of working with a given target, it searches for unusual data.

Multi-factor authentication allows businesses faster and more accurate identification of customers. This combines customer and device data with behavioral analysis to reduce losses. Watching IP addresses, domain names, and devices can help detect transnational criminal organizations.

These tools allow you to approve or deny transactions in real-time. This allows you to accurately detect fraud and avoid financial loss.

How to Get CNP Fraud Help

The best defense is to put an intelligent fraud management solution into place. Look for a company that offers the following security tools:

  • eCommerce fraud protection
  • Address verification
  • Card verification value (CVV) verification
  • Geolocation

Payment Protection scores all transactions. This decreases fraudulent chargebacks and increases approval rates. 

Payment guarantees build upon fraud detection systems. They offer 100% reimbursement for chargeback fraud reducing your risk to zero. 

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Account takeover protection prevents unauthorized purchases using customer accounts. These solutions watch login attempts and account information changes. Biometric patterns, device fingerprints, keylogger events, and models allow flagging of unusual behavior.

Do You Need Increased Transaction Security Solutions?

As CNP fraud increases, businesses must fortify their security measures. Vesta is a global fintech pioneer for payment and account fraud protection. Our technologies allow merchants to optimize revenue and eliminate fraud concerns.

We're focused on our customers and offer a 100% chargeback guarantee. Vesta also recently sponsored an event addressing CNP fraud chargebacks. This event featured Chargelytics speaker Karisse Hendrick.

Contact us today and let us help decrease your fraud risk.

 Contact Vesta

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