Thursday , October 17, 2019

Manual Reviews And False Positives, Costly Scourges of Online Commerce, Are on the Rise

U.S. e-commerce merchants are struggling with more fraud, but they’re also contending with two related problems that are on the rise and costing them plenty of time and money—manual reviews and false rejections, also known as false positives.

That’s according to two studies, one released on Wednesday and the other published last month.

Manual reviews, the process where merchants have staffers check out orders before accepting them, can be an effective check on fraud but also take a toll on timeliness and fraud-mitigation budgets, according to the 8th annual “True Cost of Fraud” study released Wednesday by LexisNexis Risk Solutions. Digital Transactions News previewed the study in April.

The study, which surveyed more than 1,000 risk and fraud managers early this year, finds that “excessive manual-review orders” is the top challenge at large e-commerce businesses that use so-called automated flagging to identify such orders. Some 45% of executives at these retailers cited manual reviews, as did 38% of managers at large m-commerce merchants.

Yet a study released in April by online gateway and fraud-management provider CyberSource Corp. discovered that the use of manual review is increasing among e-commerce merchants. Some 29% of orders on average were set aside for manual review last year, up from 27% in 2013. Even so, the acceptance rate ended up at 82%, indicating a good many reviews probably weren’t necessary. The process is expensive, with 46% of fraud managers in the CyberSource survey citing it as their biggest cost.

Despite the cost, “more manual review is being done,” Aaron Press, director of e-commerce and payment at LexisNexis, tells Digital Transactions News, agreeing with the CyberSource findings. Several factors explain the increase, he says. Two big reasons are the rash of data breaches and the transition to EMV chip cards at the physical point of sale, which e-commerce merchants know is pushing criminals into online fraud.

“The fact is data breaches are so common, there’s so much payment data out there and it’s driving a certain amount of paranoia,” says Press. He adds that the general increase in e-commerce transactions is also helping to increase the volume of manual reviews. Still, he warns that merchants could end up spending the entire margin they earn on a transaction in conducting a review. “It would have been better not to review [these orders] in the first place,” he says.

But merchants are also struggling to avoid turning down good customers whose orders, for any number of reasons, might look fishy. Such rejections, known as false positives, are also plaguing e-commerce and costing merchants hard dollars in lost sales as well as untold losses in goodwill. “They’re leaving a lot of money on the table,” says Press.

Indeed, the rise of false positives in just one year is stunning, according to the LexisNexis data. Thirty-five percent of orders rejected by large e-commerce merchants early this year turned out to be good, up from 25% at the same time in 2015. The corresponding figures for large m-commerce merchants are 33% and 30%.

The CyberSource study looked at the issue in a slightly different way but also found an upward trend. Online merchants on average rejected 2.8% of orders in 2015, up from 2.3% in 2013. Most estimated that as much as 10% of these orders were actually valid.

Press says part of the problem is that the profile of a dodgy order changes quickly, throwing off merchants’ detection systems. Also, new consumer trends can confound traditional vetting methods. An example, says Press, is the “buy online, pick up in store” trend. In such cases, the order by definition lacks a shipping address that many fraud models can tie to a verifiable identity, allowing the model to OK the transaction. “Not having a shipping address verifiable to an identity creates the need to change the fraud model,” says Press.

Another way consumers can throw off vetting models is by changing their behavior when they switch from a desktop computer to a smart phone. Consumer behavior on the former device is often quite different from that on the latter, Press says. Yet, “merchants don’t differentiate,” he adds.

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