While account takeovers appear to be the most common sort of loyalty fraud recorded, this is simply the tip of the iceberg; program rule violations, unauthorized redemptions, privilege escalations, improper integrations, and data breaches are also on the rise.

To make matters even more complicated, the growing complexity of loyalty programs makes it much more difficult for businesses to defend themselves against fraudsters.

A typical loyalty platform implementation project entails dozens of interconnections with other systems, partners, point transfers, reversals, and conversions, among other things. The more complicated the implementation, the more likely there are to be flaws that can be exploited.

Currently under assault

Though it’s hard to believe, there is a youngster out there doing something right now to try to exploit exposed weaknesses in your loyalty program. Statistically, your program will become a target for fraudsters regardless of the reporting solution you use, the fraud rules you have in place, or the security strategy you have in place.

How can you retaliate? The only thing that could give you an advantage is an automated security system that doesn’t require explicit configuration; a system that adapts to the data processed by your loyalty program — a system that can sift through massive amounts of data to find a few subtle patterns and correlations among billions of data points and parameters that are constantly changing. Over time, this algorithm should improve at spotting anomalies.

Machine learning has struck once more!

Despite the fervor with which this topic is discussed, machine learning is not a magic wand that can eliminate all risks. Traditional fraud prevention and detection approaches will never completely replace it. Strong endpoint security, policies, and processes, as well as well-designed reporting and fraud standards, are and will always be required. However, machine learning will transform your firm from a reactive to a proactive fraud protection approach that discovers abnormalities before they do harm to the program as a whole and to individual members.

Here’s a simple illustration. If more than five sale transactions were logged in a day, a gas station chain created a fraud rule that would block a member’s account. The ban was intended to keep cashiers from swiping their own loyalty cards while a paying customer wasn’t enrolled in the program, as well as from accumulating points in violation of loyalty program guidelines. Car wash services, on the other hand, were processed by distinct Point-of-Sale software and considered as a different type of transaction, as cashiers discovered. Soon, cashiers started concentrating on car wash customers, as those transactions were not covered by the preset limits and allowed for quick and simple point gains.

Errors made by humans

Another case in point. A security team set up an alarm that goes out when new member enrollments hit a certain threshold. The marketing team devised a fresh sign-up promotion that was successful in attracting a large number of new members. Those two teams rarely communicate with one another, so they didn’t think to talk about the promotion and its implications for the system. As a result, when the security team began receiving an unusually large number of alerts, they suspected the platform was being used to commit mass registration fraud and decided to shut it down completely. It took them about an hour or two to double-check that all of the new member accounts were genuine.

These two examples are real-life loyalty systems with millions of active members. What unites them is that, whereas previous fraud prevention procedures were founded on sound assumptions, there are always scenarios in which those assumptions will fall short of meeting all of the program’s requirements.

Advantages of loyalty programs

Machine learning modeling has the advantage of requiring only one basic assumption: that the vast majority of employees and members have no intention of harming the program; members follow the rules and enjoy the program as it was intended. Machine learning algorithms may “learn” common behaviors and identify patterns and links between millions of data points, whether they are transactions, points, values, or activity patterns, using the data they collect. Of course, these can alter over time, and machine learning will adapt to these changes.

What’s also impressive about this methodology (dubbed “unsupervised machine learning” by some AI nerds) is that it doesn’t require any explicit criteria of what is and isn’t typical behavior. It will adjust to the volume of data it gets as input and reports any irregularities as soon as it “decides” they are significant enough to warrant a warning. This allows fraudsters to be one step ahead of them by proactively preventing fraudulent behaviors that have not been witnessed in the past.

The missing component

Although machine learning is not a universal answer to all loyalty program concerns and challenges, it can be the huge missing piece of the puzzle in terms of loyalty program set up security. It offers a truly proactive loyalty fraud protection strategy that is ready to tackle the difficulties of the ever-changing landscape of current information systems when combined with standard fraud countermeasures.

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