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Magecart web skimming attacks target merchants connected to major payment networks including American Express, Mastercard, and Discover.

A series of coordinated smash-and-grab thefts in the San Francisco Bay Area dominated our news feeds at the start of the 2021 holiday season. Dozens of people stormed San Francisco's Louis Vuitton store and a Nordstrom in nearby Walnut Creek, emerging with handfuls of luxury items valued at more than $100,000. These attacks, according to law enforcement, were organized on social media and committed by people who didn't know each other. There is now a digital version of this organized retail theft -- and it is silent, nameless, and faceless -- and it uses a new type of process called BNPL. BNPL (buy now, pay later) is a type of installment loan that lets you make purchases online and pay them off in weekly, bi-weekly, or monthly installments.

App builders commonly use security challenges, a form of step-up authentication, to add security to their apps. However, the ethical implications of this type of architecture has not been studied previously. In this paper, we present a large-scale measurement study of running an existing anti-fraud security challenge, Boxer, in real apps running on mobile devices. We find that although Boxer does work well overall, it is unable to scan effectively on devices that run its machine learning models at less than one frame per second (FPS), blocking users who use inexpensive devices. With the insights from our study, we design Daredevil, anew anti-fraud system for scanning payment cards that work swell across the broad range of performance characteristics and hardware configurations found on modern mobile devices. Daredevil reduces the number of devices that run at less than one FPS by an order of magnitude compared to Boxer, providing a more equitable system for fighting fraud. In total, we collect data from 5,085,444 real devices spread across 496 real apps running production software and interacting with real users.

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In what can be described as a kind of arms race, tech companies have been amassing large volumes of user data to hone the artificial intelligence algorithms that power their applications and platforms. Thus far, they've mostly been able to evade accountability when their practices have pushed them into legally and ethically gray areas. But that might change on May 25, when the European Union's General Data Protection Rules (GDPR) come into effect. The GDPR will impose unprecedented restrictions on the collection and handling of user data in the EU region and slap heavy penalties on companies that fail to comply. That might sound like bad news for companies that use AI algorithms, which have benefitted from lax data-collection regulations (and lengthy, boring, and ambiguous terms of service documents).

Customers of ING Direct may soon be able to ask their "digital assistant" to check their bank balance, or tell a "chatbot" on Twitter to freeze a credit card that has gone missing. As banks grapple with the mobile revolution, the online bank is preparing for what some in the technology world think could be an even bigger change: the rise of artificial intelligence. By the end of this year, it expects to be capable of unleashing chatbots on Twitter, Facebook, and Google's Assistant, which is the tech giant's answer to Apple's digital assistant, Siri. While many banks are active on social media, and other Australian banks are also working with chatbots, ING says its new wave of bots are different because they will be able to talk to customers in in everyday, jargon-free language. Recently appointed chief information officer, Ani Paul, said customers would be able to ask the chatbot questions such as "How much money do I have in my account," or "Am I broke yet?"