Fighting Fraud with AI

Most anti-fraud software solutions function by applying rules designed to detect fraudulent transactions as they occur, so that they can be filtered out and rejected or held for the merchant to manually review. In the past, this was done by applying human analytics to data from fraudulent transactions, identifying the shared characteristics and indicators of fraudulent transactions, and creating a ruleset for the anti-fraud software to reference.

These solutions can be great at stopping the majority of low-effort fraud, but knowledgeable fraudsters have many ways of getting around these defenses.

Fraud is a problem that keeps growing in scope and complexity, and the consequences for merchants who don’t take fraud seriously can be dire. In addition to the loss of revenue from fraud-related chargebacks, merchants can be penalized by acquirers and credit card networks if they carry excessive rates of fraud, which in extreme cases may cause them to lose their merchant accounts.

To adequately deal with this threat, merchants need anti-fraud solutions that are capable of catching sophisticated, high-tech fraud.

Fighting fraud always requires a multilayered approach, and the right software solution for one merchant may not be the right fit for another. That said, merchants of all stripes are finding that the most effective fraud filtering solutions available today are utilizing artificial intelligence as well as machine learning technology.

How Can AI Help Fight Fraud?

One of the biggest challenges with fraud filters that use preset rules is figuring out just how restrictive to make them. Filter too much, and you’ll flag a lot of false positives, which can alienate legitimate customers. On the other hand, if your filters let too many questionable transactions through, it defeats the point of having them at all.

AI-based fraud filters can make real-time determinations based on vast amounts of variables and historical transaction data. Consider the example of a merchant who experiences high rates of fraud from a particular country. Using a manually-configured fraud filter, the merchant might block all orders from that country, stopping fraud as well as any legitimate orders that might originate from there.

An anti-fraud solution that employs AI would be able to factor in the customer’s behavior as well as countless other data points to make a more accurate determination of the specific risk of fraud in that instance.

Three things set most AI-based anti-fraud solutions apart from ordinary filtering and risk scoring solutions: behavioral analytics, specialized fraud analytics, and the application of machine learning technology to large sets of historical transaction data.

What’s the Difference between AI and Machine Learning?

AI and machine learning are frequently discussed together in a fraud fighting context. While AI refers to computers mimicking human thought processes, machine learning is a subset of AI, or a method of applying it, in which a machine or system “learns” on its own by crunching massive amounts of data instead of being fed specific instructions by a human programmer.

In terms of fighting fraud, machine learning means analyzing transaction data to determine how to detect fraud. Machine learning can be “supervised,” which means that fraudulent and non-fraudulent transactions are explicitly tagged as such, or “unsupervised,” where the data is not tagged and the system is making its own determinations as to whether or not transactions in the historical data are fraudulent.

From this data, the machine learning system generates behavioral analytics, which defines “normal” customer behavior such that deviations from it can be interpreted as signs of fraud, and specialized fraud analytics, which identifies the actions and underlying conditions that are most likely to be associated with fraud.

Put together, these analytics provide a model that the AI can use to assess whether any given transaction is likely to be fraudulent.

Is AI Really More Effective at Fighting Fraud?

According to some experts, AI makes a big difference when it comes to stopping fraud. However, every merchant must weigh their fraud and chargeback defenses on their own merits and calculate whether or not they are providing a positive ROI.

A solution that generates too many false positives may need to be reconfigured, one that allows too much fraud to go through may likewise need adjustments—or it may be the wrong tool for the job.

Conscientious merchants should always make manual review a companion process to any automated fraud filtering system. By taking a close look at blocked orders, you can allow false positives to go through, reach out directly to customers whose orders appear questionable, and learn more about how actual fraud presents itself.

The goal for every merchant should be a quick and seamless transaction process that succeeds in catching out fraudsters as much as possible.

As fraud grows more technologically advanced and subtle in its methods, it becomes increasingly likely that AI and machine learning will be indispensable elements of the best fraud prevention solutions.

Conclusion

True fraud is a thorny problem for ecommerce merchants, and because true fraud chargebacks cannot be fought after the fact, they require a proactive approach.

You can’t have meaningful chargeback prevention without fraud prevention, and software solutions make the most sense for many merchants. Choosing and implementing the right solution isn’t always easy, so keep in mind that a good chargeback management firm will always offer fraud prevention as part of their services.

That includes analyzing your transaction data to get a clear picture of where your fraud is coming from, and helping you choose and set up a fraud solution that’s the right fit for your business.

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