Protect Your Business With Transaction Fraud Detection

We’ve already seen similar fraud trends from previous years spill into 2024. What’s clear is that fraudsters closely align their tactics with consumer trends—most notably online shopping. With so many consumers making digital purchases, transaction fraud is becoming a larger threat to businesses each year.

For perspective, a study by LexisNexis found that every $1 of fraud costs U.S. and Canadian businesses $3 or more.

Suffice it to say, if your business isn’t prioritizing fraud, you should start now.

The key to making fraud prevention a priority for your business is using a payment provider with advanced fraud monitoring. Many solutions exist, but you want to partner with one that specializes in transaction fraud.

A strong fraud detection and prevention solution will include the following:

• Custom and traditional rulesets

• Adaptive machine learning

• Automated decisioning

• Expert human oversight

Let’s dive into these in more detail.

It may seem a little like magic, but there’s a lot that goes into effective fraud detection and prevention solutions.

And it starts with rules.

How fraud rulesets are defined

There’s no one-size-fits-all ruleset for detecting fraud. Rules vary based on business type, vertical, or merchant category code (MCC), as well as how a business operates.

Here’s a comparison between different business types:

Let’s say you run a lumber company that accepts payments by invoice and it’s common for you to have multiple invoices and multiple payments for one customer. For instance, you may send 10 invoices to the same customer, and accept 10 separate payments for each invoice within a short timeframe.

This makes sense for your lumber business, so these transactions wouldn’t be flagged as fraudulent as your rulesets would account for this behavior.

However, if your business is a grocery store, 10 separate purchases from the same customer in a short time is incredibly unusual behavior for your buyers. In this case, the transactions would be flagged as fraudulent. Again, this would be based on predefined rules tailored to your industry.

How rulesets work

Think of rulesets as a decision tree. Meaning, that if “this” is “true” then perform “x” action. If “this” is “false,” continue moving the transaction through the workflow until it’s completed. Depending on your industry, there may be several rulesets in place to guide decisioning.

Next up is machine learning (ML). An ML model is a form of artificial intelligence (AI) that analyzes algorithms and data to perform tasks based on unique situations and without the need for explicit instruction.

How machine learning works to detect fraud

When it comes to payment fraud, machine learning models are trained on behaviors that resulted in chargebacks for previous transactions. The next time the model sees similar behavior, it can boost the scoring algorithm to proactively block bad transactions.

A good example is if there is a spike in authorization activity, which is indicative of a bad actor card testing in preparation for making fraudulent purchases. If rules are in place, the ML model would notice this behavior and block transactions related to this anomaly.

While machine learning and AI are pretty much a necessity for fraud prevention in this digital age, they alone just aren’t enough.

ML models still require human supervision and guidance as models operate based on the input given to them. That’s why having a dedicated team of fraud experts behind the algorithms is critical, as they can adjust your rulesets and algorithm to your risk appetite and unique transactional behavior.

Too many failed transfers can block revenue; too few blocks can lead to lost revenue and reputational loss.

Finding the right balance requires experienced analysts as they can better identify false positives and adjust your rulesets accordingly so you can maximize your revenue potential.

Most card brands view chargeback ratios at 0.65% of your total processing volume as an early warning sign of chargeback risk. Anything above 1.5% is considered excessive and could land you or your merchants on the MATCH list, which would cause your business to be labeled as high risk, or worse, your merchant account could be revoked.

Blocking suspicious behaviors and bad actors is key to protecting your business as it significantly reduces chargebacks, which subsequently, increases your revenue.

While human oversight is still required, fraud detection software also helps increase revenue by reducing manual review and automating tasks. For example, as ML models are trained on rulesets specific to your business or industry, you can control most bad behaviors automatically and focus human oversight on other parts of your business.

Now that you understand how machine learning and fraud detection work to benefit your business, let’s dive into some of the different aspects of fraud. This will help you fine-tune your payment strategies and set you up for success.

Fraud is growing and mirroring our digital economy. Understanding the risks associated with different payment methods and which are most abused can go a long way in combating and preventing fraud.

Checks

Despite its decline in usage, the paper check still reigns supreme when it comes to payment fraud. This is due to the lack of security measures and fraud detection available for traditional payment methods.

Credit and debit card fraud

Card fraud is wildly popular among fraudsters. This is because bad actors can purchase card lists and test how many cards on the list are ‘active’ by first making a low-dollar transaction. If this transaction is successful, they’ll attempt to make pricier purchases before the real cardholder becomes aware of the transactions.

Digital prepaid and gift cards

The growing popularity of gift cards makes them prime targets for fraud. Gift card numbers and PINs are commonly stolen via phishing scams that trick consumers into providing their information.

Setting fraud rules around prepaid cards is especially beneficial for restaurants, food and beverage delivery, entertainment, and retail stores.

Peer-to-Peer (P2P) payments

This type of fraud is on the rise and is very difficult to track. It’s vital to have tight KYC controls around P2P payments and to watch buyer behavior on what these transactions could be for, as they are popular for use in money laundering scams.

Wire transfer fraud

Wire fraud is another fan favorite of fraudsters. Unfortunately, this typically falls outside of fraud detection solutions as it runs on different systems. Deepfake scams using AI to impersonate C-level professionals are on the rise. These tactics are used to trick unsuspecting employees on accounts payable or finance teams into providing sensitive company and/or employee payment data.

Safest payment options

All payment methods are at risk for fraud. That said, ACH typically sees slightly lower return rates due to NACHA regulations, such as bank validations for purchases and the timeframe to dispute transactions. ACH payments are particularly popular for high-ticket transactions as ACH has lower fees than credit cards. If your business is in a high-ticket industry and sees a sudden flurry of card activity, this is a strong signal that you’re being targeted by fraudsters.

While credit and debit cards experience a higher rate of fraud than ACH, they offer consumers more protection. Card companies usually give consumers a conditional credit when they file a dispute based on fraud.

Read our blog Payment Fraud: Everything You Need to Know to learn more about the different types of payment fraud.

Now let’s look at some of the industries most targeted by cyber criminals.

E-commerce

When thinking of fraud, the average consumer immediately thinks of e-commerce, as most customers have experienced a “Hey! I didn’t buy that!” moment at least once, if not on multiple occasions.

The e-commerce industry is also one of the biggest victims of ‘friendly fraud,’ which is a type of fraud committed by consumers. Friendly fraud can be when a customer attempts to get goods and services for free by filing a chargeback or filing a chargeback instead of a return.

But it’s often the result of a bad descriptor on a credit card statement. That’s why it’s so important to have a clear statement descriptor of what your consumers are buying. You should also make it easy for customers to contact your business to confirm a purchase as this can prevent a costly dispute.

Government

Another popular form of fraud is when a bad actor poses as a government entity or a third party contacting an individual on behalf of the government through phishing scams. These tactics involve threatening individuals with legal action or wage garnishments because of overdue bills that may or may not exist.

Mining

While mining might be the last industry that comes to mind when you think of fraud, it actually has the highest median loss, even though it experiences the lowest rate of occurrence.

Real estate

Fraud in real estate is similar to mining in that it doesn’t occur as often as in other industries, but it has the second highest median loss.

Finix provides all customers with a full-service fraud solution that uses machine learning and sophisticated rulesets to detect fraud and prevent bad transactions from processing. We also offer custom fraud consoles for PayFacs and businesses that require more complex configurations. On top of that, we have a team of in-house fraud analysts who continually monitor your transactions for fraud and help you mitigate issues.