Businesses of all kinds suffer from the pervasiveness of fraud, with a recent study finding that 40 percent of retailers, restaurants and insurance companies had their online expansions impeded by cybercrime. Many of these companies are turning to new and innovative techniques, such as machine learning, to counter this threat. Others, like Dropbox, are leveraging password encryption matching, and extensive employee education on best practices.
The February edition of the Digital Fraud Tracker explores recent developments (including
Developments From Around The World Of Digital Fraud
Fraud is on the rise in a variety of forms. Card-not-present (CNP) fraud, for example, rose from $4.5 billion to $7.2 billion since 2016, while account opening fraud rose from $703 million to $1.3 billion since 2016. These losses are devastating enough, but are made even worse by the fact that only 25 percent of fraud losses are ever recovered.
Some types of fraud target individuals rather than businesses. Online dating fraud, for example, is a pandemic in the Netherlands, where incidences more than doubled over the past two years. The average victim lost €14,400 ($15,927 USD) as a result of dating fraud, with country-wide losses totaling €3.7 million in 2019. The most common victims were women, individuals over 45 and the divorced or widowed, though Dutch authorities noted that this type of fraud often goes unreported.
Nonprofits and charities can also fall victim to fraud.
For more on these and other digital fraud news items, download this month’s Tracker.
Dropbox Leverages A Three-Pronged Approach To Fight Digital Fraud
Cloud service providers host untold terabytes of valuable data, making them a particularly juicy target for fraud. Dropbox is one platform working to keep its data safe from cybercrime, utilizing a three-part approach to fight illicit activity.
In this month’s feature story, Rajan Kapoor, the platform’s director of security, explained how Dropbox employs automated tools, user and employee education, and threat response teams to keep cybercriminals out of the cloud.
The damage caused by fraud in the cloud service space can total in the millions of dollars, as demonstrated by the devastating Capital One data breach last year. Keeping these services safe is a tall order, though unsupervised machine learning may be the key to closing this security gap.
This month’s Deep Dive explores the various uses of unsupervised machine learning in the cloud, and how its diverse techniques and implementations can provide defense in depth against an equally diverse array of fraudsters.
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