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Western E-Tailers Set to Lose Nearly $19bn to Fraud

E-commerce sites in the US and Western Europe are estimated to lose a whopping $18.6bn this year through fraud, according to a new Forrester report.

The market analyst compiled its figures from LexisNexis estimates that in 2017 the cost of fraud was just over 2% of revenue for e-tailers, and that the regions are expected to generate $859bn in revenues this year.

In response to the growing losses, it claimed that the fraud management solutions market would grow from $5bn last year to reach $10.4bn by 2023; a CAGR of 12.9%.

Although traditional enterprise solutions are expensive — typically ranging from $750,000 to $1.2m, with implementation adding another 40-50% in costs — they can automate and improve the accuracy of risk scoring, reducing false positives, the report claimed.

This can in turn reduce the investment needed in fraud personnel to review transactions.

However, customer friction remains a key differentiator for effective modern fraud prevention platforms, argued Forrester.

The report claimed that technological advances like AI will help to drive improvements in the accuracy and effectiveness of solutions going forward.

“It’s time consuming for fraud and risk management professionals to continually update fraud models, and it’s increasingly difficult to identify fraud across multiple channels including mobile,” it said. To combat these threats, fraud management solution vendors are incorporating artificial intelligence tools, such as supervised and unsupervised machine learning, into their products.”

It also pointed to Blockchain as “the next evolution in fraud management.”

“Blockchain is a distributed and secure database, making it a trusted repository for device ID and known fraudster blacklists. Blockchain already secures payments and can be extended to enterprise fraud management,” the report claimed.

The importance of fraud prevention was highlighted recently by PayPal’s $120m acquisition of Simility, a pioneer in friction-free anti-fraud technology featuring machine learning.

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