Artificial Intelligence

AI/BOTS: Machine Learning Tips Online Fraud Scales

Technological advances are pushing the boundaries for what’s possible in a variety of industries.

Within the retail industry specifically, artificial intelligence (AI) is moving the ball for a lot of merchants looking to not only streamline their business operations but provide a more personalized experience for consumers.

Stemming from AI, machine learning is helping technology move at a swifter rate, and as more people bring their shopping needs into the online world, it’s likely that machine learning will play a larger role moving forward. Machine learning applies AI to take up-to-date data to help improve upon an experience without any human interaction. In a sense, machine learning is the autopilot for the digital arena.

From enhancing the supply chain process to learning more about consumers’ shopping behavior, machine learning is a technology that’s at the forefront of retail advancements and innovation.

As such, it would be logical to utilize machine learning to help tackle one of the largest threats to the eCommerce shopping world – online retail sales fraud.

In 2016, online financial fraud hit 15.4 million Americans, a 16 percent increase to $16 billion in stolen money. With a $1-billion climb from the year prior, it’s apparent that this type of fraud is not going away any time soon, especially with the increasing popularity of online purchases.

Several companies have popped up over the last few years with the specific purpose of combatting online fraud.

From Fraugster to Riskified, Signifyd, and Feedzai, these machine learning companies are fighting fraud head-on in the eCommerce arena. As we reported back in April, machine learning comes in handy when identifying consumers’ digital identities, which includes payment methods and geographic locations. Basically, machine learning is tuning into consumers’ usual retail behavioral patterns and has the capability to alert merchants when any unusual activity takes place.

As machine learning is implemented in retailers’ operations, it appears its working as far as helping to reduce the amount of fraudulent activities.

In PYMNTS and Signifyd’s Q1 2017 Fraud Index, it was found that eCommerce fraud has decreased an astounding 35 percent since 2016. The increased use of machine learning was found to be one of the main contributing factors in helping to reduce fraud across most industries, except for department stores and jewelry stores.

“One of the main reasons behind this decline is the use of machine learning in fraud prevention solutions that are raising the bar against a global network of cybercriminals. These machine learning solutions are doing a better job than previous solutions, which relied on static rules, of distinguishing real orders from fraudulent ones,” the index report stated.

Rather than having a system that’s reliant on human power, the installment of machine learning services is probably helping to ease pressure on 24/7 security monitoring for retailers in all sectors. With machine learning, retailers will have a system that continually improves upon itself to become more in tune with the overall businesses’ daily activities, including supply chain operations, manufacturing and consumer behavioral preferences, to name a few.

Businesses have been in the news as of late in terms of using machine learning for their benefits.

While GE uses machine learning to better predict maintenance needed for its large industrial machines, Walmart is helping to differentiate itself through its ordering online and picking up at designated Pick-Up Towers. Through the use of artificial intelligence, Walmart is able to route consumers’ online orders through its brick-and-mortar stores where items are then selected by employees and brought to the tower, allowing consumers to skip the checkout lines.

As grocery delivery services like Peapod and Instacart continue to move into the grocery arena, this offering by Walmart may help the 55-year-old retailer stay afloat.

By utilizing machine learning, retailers are essentially freeing up their security staff to improve and innovate on their fraud detecting tools and the rest of their employees to provide a more customized experience for consumers.

 

 

 

 

 

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Our data and analytics team has developed a number of creative methodologies and frameworks that measure and benchmark the innovation that’s reshaping the payments and commerce ecosystem. In the December 2019 Mobile Card App Adoption Study, PYMNTS surveyed 2,000 U.S. consumers for a reveal of the four most compelling features apps must have to engage users and drive greater adoption.

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