Consulting Cyber FinTech Retail Solutions

How AI Fights Fraud During the Holidays

Christmas is just over a week away, which means the holiday shopping season is in full swing.

Copyright by www.datanami.com

 

SwissCognitiveConsumers are spending billions of dollars per day on gifts in anticipation of the big day. But the fraudsters are also out in force to steal a piece of the action. Luckily, and are getting better at identifying these grinches before they ruin things for the rest of us.

The math is pretty simple: The bigger the holiday buying season, the bigger the pay day for fraudsters. Deloitte predicts that online sales during this holiday season will increase between 14% and 18% compared to the previous year, accounting for $149 billion in sales. Overall holiday spending is expected to top $1.1 trillion, it said.

According to data from the Forter Fraud Attack Index, the overall dollar volume of fraud rose 12% between the second quarter of 2018 and the second quarter of 2019, with activity soaring once holiday shopping began. “Since the holiday season represents about 20% of annual retail sales, expect an increase in attempted fraud,” Forter writes in its holiday white paper.

In the UK, 94% of retailers say they’ve witnessed a rise in fourth-quarter fraud since 2016, according to GBG , with 31% saying that the increase is “significant.” The uptick in activity during the “gold quarter” provides cover for fraudsters to blend in with the holiday traffic.

That provides plenty of room for fraudsters to do their dastardly deeds, according to the folks at Terbium Labs, a provider of data security solutions. “Criminals rely on consumers to stay distracted during the holiday season, and less likely to notice unauthorized transactions on their accounts during the bustle of holiday parties and gift-giving,” Terbium writes in its November 2019 report, “How Fraud Stole Christmas.”

Fraudsters have become extremely diverse in their techniques over the years, thanks to an inherent curiosity in developing innovative approaches to separate hard-working people from their money. According to Terbium, card-skimming and stolen cards are the types of fraud that people fear most, followed by phishing scams and (everybody’s favorite) card-not-present fraud.

Card-not-present transactions soar during the holiday season, as many people prefer to stay at home and shop online as opposed to going to the store. While shoppers may not think there’s a big difference between shopping online and in-person, it makes a big difference to the merchant and the firms processing the transaction. Card-not-present transactions are inherently riskier, and therefore banks will compensate consumers who fall victim to fraud, but the merchants are held liable.

Regardless of the way fraudsters worked, about two-thirds of people say they would hold their bank at least partly responsible for fraudulent activity, regardless of how the compromise occurred, Terbium found. That puts the onus on retailers (which lost 3% of revenues to fraud in 2018, per LexisNexis) to ensure that fraud doesn’t happen on their watch.

Detecting the fraudulent activity quickly is the key to stopping it, and that’s where comes in. Machine learning gives the good guys (banks and merchants) a big technological advantage against the fraudsters in several ways. […]

 

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