Identification And Prevention Of Payment Fraud

Payment fraud detection is the procedure of discovering efforts to make fraudulent transactions and stopping them from occurring. The following examples of payment fraud, among others, are provided:

  • Card-Not-Present (CNP)
  • Account Enrollment Fraud
  • Chargeback Fraud
  • Account Takeover (ATO)

In response to COVID-19, more companies have turned to eCommerce over the past few years to stay competitive and reachable to customers.

Total digital channel payments may reach $5.9 trillion by 2023, according to Outseer and Aite-Novarica Group analysis, after rising to $4.2 trillion in 2020. And if customer interest in card-not-present-enabled online and mobile transactions, well-liked payment methods like Scan-and-Go, “invisible” payments, and Buy Now, Pay Later continues to grow, that number may increase.

Approximately 60% of customers will pick a company that accepts these or other payment methods over a rival that does not.

Payment fraud has risen in the last 18 months with the growth of digital channel sales. Unfortunately, scammers have fully exploited this change. The CNP scam alone might result in $17.2 billion in lost income each year over the next two years. The financial harm quickly mounts when you factor in the potential loss of up to $25.3 billion by chargeback fraud.

These attacks may quickly reduce profits for retailers, credit card issuers, and other payment system processors.

Without impeding legitimate consumer payments, payment fraud detection seeks to spot fraudulent activity before transactions are completed.

What Kinds Of Attacks Can Be Prevented By Payment Fraud Detection?

Let’s look at a few instances of payment fraud assaults and how payment fraud detection might prevent them from gaining a better understanding of payment fraud detection.

Card-Not-Present (CNP) Fraud

When thieves utilize credit card information that has been stolen without actually having the card, this is known as card-not-present fraud. CNP fraud is one of the most widespread types of payment fraud since thieves use many ways to obtain card information.

Cybercriminals frequently use phishing tactics to deceive victims into providing their credit card information on a fake website that looks exactly like the real thing. Once this information has been taken, fraudsters use it to make fraudulent purchases.

Another frequent source of the stolen card information is data breaches. Attackers can steal a cardholder’s payment information from unprotected sites wherever their card was used, even when the cardholder takes precautions to protect their information. To test card data on vulnerable websites, fraudsters buy these compromised credentials in volume via dark web marketplaces.

The victim’s CVV is compromised in both instances, proving that CVV checks are insufficient.

Payment fraud prevention and detection techniques using the PSD2 SCA Secure protocol have shown to be amazingly successful.

For instance, today’s most effective payment fraud detection tools secretly authenticate consumers before any transactions by analyzing over 100 risk factors. This makes it possible for retailers, issuers, and banks to offer genuine consumers a quick and polite checkout experience while discouraging frauds at the door.

Chargeback Fraud

Chargeback fraud occurs when a customer starts a chargeback via their account for a product they previously received. Customers may occasionally forget they purchased something or that autopay was turned on. This type of incident is described as “friendly fraud” when it occurs accidentally.

Regardless of the motive, these situations lead to a chargeback that provides the client a refund and the company a chargeback fee. When fraudsters find a weak company, they can continually target it, resulting in a spike in chargebacks.

Based on the merchant’s contract with their payment provider, chargeback fees might range from $20 to $100. Chargebacks can result in losses for firms on each transaction due to acquisition costs, marketing expenses, and transaction fees.

According to a recent survey, 66% of merchants claim that return abuse has worsened over the past 12 months, and 44% say it has happened to them. It can be challenging to stop this payment fraud before it occurs.

Thanks to artificial intelligence and machine learning developments, chargeback fraud can be avoided before the transaction is completed using tried-and-true fraud detection techniques. By evaluating the behavior of the consumer’s transaction history, retailers can filter out dangerous transactions that match the behavior of chargeback fraud.

Account Takeover

In account takeover (ATO) assaults, thieves use stolen credit card numbers or compromised login information to access legitimate consumer accounts and make fraudulent payments. This crime costs society quite as much as $16.9 billion a year. Data breaches-related ATOs rose by 850% during Q2 2020 and Q2 2021.

Because transactions originate from a genuine cardholder or customer account, ATOs can be challenging to identify. But it’s conceivable. Credential stuffing attacks that are intended to compromise accounts can be identified and stopped by bot detection technologies. Furthermore, modern fraud prevention tools use big data and machine learning to discern between legitimate account activity and fraud, regardless of who is registered into the account.

Account Enrollment Fraud

Fraudsters are increasingly combining stolen and made-up data to generate false identities that may be used to acquire a credit card, financial services, BNPL, or other payment accounts and submit loan applications.

Payment fraud detection tools that emphasize preventing enrollment fraud can spot fake identities and stop them from opening accounts from which they can cheat you or other businesses.

Also read: Things to Compare and Apply for new credit cards


Data science, statistical analysis, and machine learning are used in modern payment fraud detection to monitor transactions continuously and evaluate the relative risk involved with each one. Today’s best methods bridge the gap between physical and digital identities to prevent fraud without degrading the customer experience. It integrates with an array of identity verification partners. This technology prevents fraudsters even before the account is created. A fraudulent transaction or a chargeback ever takes place through continuously updated machine learning.