The Quick and Scalable Ways AI Can Help Prevent “Biz-Killing” ECommerce Fraud

By Jason Little
10 minute read

Both e-sellers and consumers have vested interests in keeping fraud at bay, and AI offers the most viable solution we currently have for both detection and prevention.

 

Of all the things that keep e-commerce sellers awake at night (including the prospects of lagging sales and diminishing customers) it’s that scary word fraud that can strike fear into the most experienced of online retailers, and for good reason.

Fraudulent transactions are predicted to cost e-sellers an excess of $71 billion over the next few years. E-commerce fraud has already grown by nearly 60% since 2016, according to Experian, and that’s just the beginning. With scammers getting increasingly sophisticated, it becomes essential to have a fraud-prevention system in place.

Artificial intelligence or AI is the ideal solution. Here’s why.

Instant Transactions Mean Rampant Fraud

Consumers today demand immediacy. They want to be first in line and desire near-instant transactions, which ramps-up chances of fraud considerably. Fast checkouts increase scammer risks and can be difficult to detect until long after the transactions occur.

Here is where Artificial Intelligence can reign supreme by rooting out scams as they happen. Using machine-learning technology, AI can detect and prevent fraud by analyzing large data sets across many sites to find patterns associated with fraud that typical algorithmic solutions miss. Big data makes it the superior solution for e-commerce sellers looking to stop fraud in its tracks.

The Real Reason AI is the Best Weapon Against Fraud

Fraud prevention teams need to know what fraud looks like compared to legitimate purchases. Even algorithm programmers are limited by the analysis of fraud that has already transpired.

Unfortunately, there’s no cookie-cutter mold for fraudulent scams. AI algorithms, however, are constantly analyzing, learning, and adapting their models for fraud to detect it in all its forms. Their continuously adjusting models of fraud are significantly more effective, catching fraudulent charges before they can result in any losses.

The Amazing Power of AI for Detecting & Preventing Fraud

For years, e-commerce fraud prevention teams have struggled to identify and prevent fraud, especially as tactics become more advanced with each passing year.

Machine-learning technology provides new insight and assistance to fraud prevention teams by identifying fraud as it happens and acting quickly to stifle any activity before any damage can be done. Machine Learning technology has a reduced learning cycle because it does not require rebuilding the model in batches and instead dynamically adjusts it with each additional data point.

Furthermore, AI scales in tandem as the company grows, allowing e-sellers to rest easy as the system continuously analyzes all consumer data to identify risks and hacks as they occur - in real-time.

What Types of Fraud Can AI Detect?

Of all the cases of fraud currently being reported, a few tactics remain popular with modern-day scammers.

  • Return to Origin (RTO): These are instances where scammers abuse the e-store’s refund policy. In some cases, the fraudster orders a product, then returns a fake one.
    • How AI Can Help: AI can detect the subtle behavioral patterns these transactions have as they occur, thus predicting when RTO scams are about to happen or identifying scammers who are known for this type of trickery.
  • Abuse of Promo Codes: This is where a scammer creates multiple user IDs to apply a promo code before ordering.
    • How AI Can Help: AI can determine when scams like this occur by determining how many accounts originate from a same or similar IP. The system is so fast and accurate that scammers can be blacklisted automatically, for example, when trying to abuse any of your e-store’s policies.
  • Payment Fraud: CNP (Card Not Present) transactions offer a variety of opportunities for ambitious hackers, and fraudsters are always looking for new exploits. Stolen credit cards and chargeback fraud (where the consumers later report a transaction as fraudulent with the bank) are just two examples of CNP fraud that can plague online retailers.
    • How AI Can Help: AI prevents CNP fraud by verifying accounts automatically, for example, so that cards are marked valid before they’re used.
  • Account Takeover: This scammer technique is incredibly difficult to identify, according to 38% of fraud teams. Instead of attacking consumer transactions directly, fraudsters figured out they could take possession of entire accounts. With merchants retaining more account and payment information to facilitate easier checkouts, scammers have immense amounts of data at their fingertips. By controlling entire accounts, fraudsters can make purchases to their hearts’ desires without being discovered (until, that is, the consumers notice the money missing from their accounts).
    • How AI Can Help: AI can determine when an account is behaving oddly. This is done through pattern recognition which creates a model around a specific user's behavior and compares each new session against the original. Behavior in each session acts as a data point either for, or against this "fingerprint". This allows the system to flag the e-store owner or the consumer, or both, to ensure all accounts and transactions are valid before they’re processed.

There’s one more fraud-related instance that can still cost e-commerce sellers big, but technically it’s not a scam at all.

  • False Positives: These are transactions attempted by legitimate customers that are tagged as suspicious by fraud prevention systems. While technically not an attack by scammers, these erroneous errors can leave money on the table. Not only that, but false positives frustrate and potentially alienate customers so they never return.

The thing about false positive transactions is that they are difficult to detect, unless the prospective customer notifies you of the error when attempting to check out.

What’s worse is that many retailers ignore these cases altogether. It’s said that 30% of merchants don’t measure false positives and 42% aren’t familiar with the term “false positive rates” at all. For this reason, these fraudulent cases are rarely reported and yet can yield untold financial consequences.

In fact, MasterCard found that affluent consumers place more than half of all falsely declined orders. That could spell big losses for many e-commerce sellers.

  • How AI Can Help: Machine-learning technology cam understand when an actual consumer is attempting to make a purchase based on many factors that a human or algorithm would have difficulty managing or coping with. The system can analyze thousands of data points simultaneously across all consumers and dynamically calculate odds of an order truly being a false positive using factors outside the immediate financial transaction. Since the system works no matter how large the database, it works from day one and keeps consumers transacting and loyal without fail.

Not All AI E-commerce Fraud-Prevention Tools Are Created Equal

When deciding on an AI tool for e-commerce, whether it’s for fraud, visual search, or anything else, consider the system’s ease of integration with your e-store, the speed with which it processes data, and how recommended it is by other e-store owners.

Here are some fraud prevention tools that make use of AI to keep e-sellers safe.

ThirdWatch is an AI fraud-prevention system for e-sellers that claims to reduce fraud by 30-40%. The system makes use of mini models and global signals, along with a host of AI algorithms, to accurately profile fraudulent behavior. The system can also scale horizontally across millions of users without changing the software.

Stripe Radar is another major player that offers e-commerce fraud protection. Representing an entire suite of products by Stripe, Radar’s algorithms are backed by years of data science and infrastructure work. The system analyzes every transaction for risk of fraud and takes appropriate action, all without triggering false positives. The good news is that Radar is built into Stripe, which gives you the ability to accept fast, varied, and secure payments from anywhere your customers happen to be.

MasterCard Decision Intelligence uses deep-learning technology to improve the accuracy of real-time MasterCard transactions, reducing the number of false declines. The great news is that merchants only need to accept MasterCard to employ this offering.

Riskified protects against costly chargebacks and other instances of e-commerce fraud. With a 100% chargeback guarantee and instant decisions, there’s no wonder this AI platform is used by so many brands, like Finish Line, Skullcandy, Aldo, and many others.

Signifyd by Magento is becoming more popular with e-sellers because of its 100% guarantee against fraud. Using machine-learning, the tool works with Shopify, Magento, BigCommerce, and other leading merchant platforms, and claims that over 5,000 sites use its service, and growing.

Conclusion

Both e-sellers and consumers have vested interests in keeping fraud at bay, and AI offers the most viable solution we currently have for both detection and prevention.

Fraud will never truly be eradicated, but artificial intelligence can help keep ecommerce store owners secure with quick and accurate data processing at any scale. You now have several ideas to get you started. Show scammers you mean business by implementing one or more of these AI fraud prevention tools starting today.

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