Payment Fraud: What Is It And How To Protect Your Business

Payment fraud is increasing as fraudsters find new tactics to target their victims. Businesses need to enhance their fraud strategies to keep up with these new payment fraud trends.With the right technology, businesses can detect and prevent fraud faster and reduce its negative impact, leading to cost reduction, better customer experience, and higher revenue.

What is payment fraud?

Payment fraud occurs when a person who is not the legitimate owner of the payment instrument initiates a payment to commit fraud.

Types of fraud

The main challenge for businesses is to keep up with the different techniques used to commit payment fraud and identify them on time. Understanding what types exist and how they can affect your business is important before looking at how to build an effective fraud management strategy.

Credit card fraud

Credit card fraud is when fraudsters use stolen card details to commit fraud by charging purchases to an account or removing money from it.

Some examples on how to detect and prevent credit card fraud:

• Perform AVS (Address Verification Service) or CID (Card Identification) checks on transactions to verify the payment location and the card’s presence.
• Apply behavioral analytics technology that flags suspicious behavior, such as someone purchasing an item multiple times, multiple purchases with the same email, or orders delivered to the same address using different payment details.

Card testing fraud

Card testing fraud is when stolen cards are tested to see if they’re active. If they are, they can be sold on the dark web for a much higher price than untested ones.

Fraudsters can see if a card is active by entering the card details when signing up for a subscription-based service with a free trial. The subscription business then performs a zero-amount authorization to see if the card is active.

Some examples on how to detect and prevent card testing fraud:

• Apply behavioral analytics technology to identify fraudulent checkout attempts.
• Use transaction data to understand your shoppers’ behavior and use velocity risk checks and business rules to optimize for full funnel conversion.
• Check order time frames. Card testers involving bots/scripts are on the rise; you can identify them by spotting many transactions within a short time frame.

Account takeover fraud

Account takeover fraud is when fraudsters get access to shoppers’ accounts and change the account details. Fraudsters can either use websites where shoppers have an account with saved payment details or create websites that look legitimate to steal the credentials of unsuspecting shoppers.

Some examples on how to detect and prevent account takeover fraud:

• Use timeline visualization to understand the normal behavior of genuine shoppers and how they differ after account takeovers.
• Ask for verification once account details are changed, for example, when a shipping address is changed.

Friendly fraud

Friendly fraud, also known as first-party fraud, is when a shopper purchases goods on an ecommerce website and initiates a chargeback without a legitimate reason.

Some examples on how to detect and prevent friendly fraud:

• Ensure your risk system can recognize patterns that identify serial-friendly fraudsters, such as shoppers who have initiated multiple service-related disputes across different cards and identities.
• Use blocked lists to make sure those bad shoppers don't return.
• Leverage a solution that can recognize fraudsters who shop across multiple global businesses so you can fine-tune your risk assessment

Policy abuse: Refund fraud 

Refund fraud is when a professional fraudster makes money by requesting business refunds. It’s becoming increasingly common and can be very difficult to detect.  This is also commonly known as policy abuse - when shoppers get well acquainted with your business’ policies in order to take advantage of things such as returns, refunds or promotions

Retailers also see a trend where bad actors return different products than they ordered, such as counterfeit merchandise or even bottles of water.

Some examples on how to detect and prevent refund fraud:

Make sure your risk system has unified commerce capabilities so you can fully understand a shopper’s lifecycle and view past orders to identify refund fraud.

Use a combination of unique attributes and leverage custom risk rules to mitigate such scenarios and identify unique shoppers misusing those details.

Gift card fraud

Gift card fraud is a common way to commit transactional fraud because the cards are hard to trace and aren’t as heavily regulated as debit or credit cards. An example of gift card fraud is when a fraudster uses stolen payment details to buy a product online and then returns it for a refund on a gift card.

Some examples on how to detect and prevent gift card fraud:

• Use contextual data to help build a much stronger defense against gift card fraud.
• Use a combination of custom risk checks and block lists based on this data to help spot these transactions.
• Identify misuse of gift cards by using custom risk rules and specified indicators to mitigate such events.

How does payment fraud impact businesses?

Payment fraud has a negative impact on businesses. Here are a few of the consequences:

• Money lost
• Increased chargeback fees
• Reputational damage
• Legal and regulatory challenges
• Payment fraud challenges

Due to legacy technology not being able to balance security with customer experience, many businesses end up compromising revenue and customer experience by being too stringent. Payments are blocked as soon as something stands out from normal customers' behavior. Differentiating between fraudsters and customers can be difficult and lead to genuine transactions being blocked. This will directly affect revenue and leave customers unhappy with the buying experience.

Fraud prevention is the process of preventing fraudulent activities from impacting the business, customer, or financial institution. To do this effectively, businesses need to maintain full control and reduce operational workload. This is done by combining risk rules with machine learning and manual reviews.

Supervised machine learning

Supervised machine learning involves a combination of risk knowledge and machine learning. Businesses can create risk profiles to help automate part of the risk assessment, saving time and reducing risk management efforts. The bigger the scale of the platform the machine learning model is learning from, the more your business will benefit. These models can learn from multiple channels, payment instruments and regions to build strong shopper understanding and ensure that automated decisioning does the heavy lifting.

Customizable risk rules

Different industries and business models face different types of risks. Through customizable risk rules, businesses can create risk profiles tailored to their unique needs and use them to complement the payment evaluation process of machine learning models. This can help optimize underperforming risk profiles or rules, and monitor the impact of changes. .

Manual review

Certain types of transactions are at a higher risk of being targeted by fraudsters, these include high-value transactions or transactions in high-risk markets. For an extra layer of fraud protection, businesses can choose to manually review these types of transactions before they’re completed to avoid negative bottom line impact. Source