7 Steps Online Retailers Can Take to Reduce CNP Fraud

7 Steps Online Retailers Can Take to Reduce CNP Fraud

Posted on May 20th 2021, 8:02:00 pm

Category: Podcast

reduce cnp fraudStay-at-home orders and the closure of brick-and-mortar stores has led to a rise in cardless payments. In fact, there was a in activity between March and April 2020 when the coronavirus pandemic began to worsen.

While cardless make it easy for consumers to make purchases online, it also paves the way for fraudsters to commit CNP fraud – where criminals make fraudulent transactions without processing the physical card. This type of fraud has increased so much that predicts retailers will lose around $130 billion in CNP fraud between 2018 and 2023.

Fraudsters are savvier than ever. They’re taking over existing customer accounts and inventing new customer identities using stolen data. But, worst of all, fraud is happening at scale across multiple industries. There’s been a sharp to launch huge fraud attempts and carry out mass fraudulent transactions that can ruin a business.

Here are the steps retailers can take to reduce their risk of CNP fraud.


1. Know Your Customer

Traditional Know Your Customer (KYC) practices let you know exactly who you’re dealing with. The process allows companies to collect information about the identity and address of a customer to confirm they’re a real person and not misusing the service in any way.

Apply strict rules for to confirm if the customer does actually exist, and match them with a real-world identity. You can do this by searching their email address, calling their phone number, confirming their identity on social media, and carrying out other checks to confirm who the customer is.

Obviously, this can be a time-consuming manual process, so it’s best to reserve it for customers that bring up cause for concern.


2. Verify Customer Information

The key is to verify, verify, and verify some more. Encourage new and existing customers to verify their accounts or transactions with their card number and CVVs, and flag any history of fraud associated with that data or information.

Online consumers will be used to providing this information, and it’s easy to incorporate it into the sales process without causing too much friction.

You can manually apply a rule-based scoring system to transactions by examining payment data like , transaction amount, currency, IP address, payment method, order history, and billing address and determine how many of these are satisfactory for each customer.


3. Transaction Monitoring

Customers don’t venture far from their usual purchasing habits. In fact, when a customer that regularly buys low-cost items suddenly makes a big purchase, it’s a cause for concern and an instant red flag for fraud.

by tracking transaction patterns and understanding how customers typically interact and pay for your products. Look at these transactions regularly, too, and consider:

  • Whether the customer is new
  • If the purchase is unusual compared to their usual activity
  • If the transaction amount is higher than usual
  • If there are multiple orders
  • If different shipping addresses have been used


4. Geolocation Tactics

Geolocation is a powerful way to determine if a customer is in the place they say they are. One of the quickest ways to identify a fraudulent CNP transaction is if the customer’s shipping address doesn’t match their IP address.

Geolocating customers compares their location and delivery location to their actual location at the time of purchase. If the addresses don’t match up, you can dig deeper to investigate if there’s possible fraud at play.


5. Combine Rules-Based Scoring and AI

We mentioned manual rules-based scoring earlier, but there’s an easier way to determine whether or not a transaction is a fraud risk. An AI-powered scoring system uses machine learning technology to assess the risk of each customer and transaction and learns from its past findings.

Like the manual rules-based system, it analyzes payment data but has the added benefit of doing so in a matter of seconds.

Monitoring consumer information like this helps retailers identify potential fraud threats quickly without having to run time-consuming manual verification activities.

Known as hybrid scoring, it’s a popular way to identify fraudulent transactions because the rules-based element provides a solid foundation for assessing risk, and the AI element learns to identify what constitutes a fraudulent transaction to detect sophisticated fraud.


6. Group Analysis

Group analysis uses technology to determine the context of an order against other similar orders. Using an anomaly detection tool, you can identify rogue transactions in a group of legitimate ones by comparing the different elements of each order.

For example, if most orders have a similar but one comes with a much higher price tag, it might be cause for concern. The software can dig deeper into the highlighted transaction and analyze other payment data to see if it’s a legitimate order or not.

Add group analysis to your fraud screening process so you can identify the characteristics of multiple orders in one go.


7. Behavior Analysis

Behavior analysis is similar to group analysis, except it digs into customer data rather than order data. Sophisticated fraud-prevention algorithms are able to identify customers by their unique shopping behavior. This might include how they search for products and what products they show the most interest in.

Diverting from their common browsing habits will indicate a potential fraud threat and flag the transaction. For example, if a customer has only ever bought women’s clothes in a size ten from your store but suddenly they’re purchasing high-ticket men’s items, it could be a potentially fraudulent transaction.

Behavioral analytics technology that’s capable of doing this is relatively new in the ecommerce world, but it’s something that will likely be adopted by stores in the future to tackle fraud.


Take Action to Reduce CNP Fraud

The rise in CNP fraud is cause for concern for retailers, but there are measures you can put in place to significantly reduce the risk. Cardless payments create a slicker , allowing consumers to make purchases quickly and easily.

Leverage the tips in this article, and you’ll be able to identify potential fraudulent activity in no time at all, reducing the number of chargebacks and other issues that fraudsters are trying their hands at.


About the Author – Rafael Lourenco is Executive Vice President and Partner at ClearSale, a card-not-present fraud prevention operation that helps retailers increase sales and eliminate chargebacks before they happen. The company’s proprietary technology and in-house staff of seasoned analysts provide an end-to-end outsourced fraud detection solution for online retailers to achieve industry-high approval rates while virtually eliminating false positives. Follow on , , Twitter , or visit .


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