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.

In 2020, an estimated £1.26 billion was lost to fraud in the UK. In the last 13 months, a staggering 449,687 incidents of fraud were reported to the National Fraud and Cyber Crime Reporting centre. It's clear that the domestic financial services industry has a huge problem -- and current strategies to mitigate it are not working. The financial industry in the UK has, like all industries, seen a huge acceleration in digital transformation over the past two years. The pandemic forcibly increased the pace of the transition to all manner of digital interactions, including online banking.

Today, Featurespace introduces Automated Deep Behavioral Networks for the card and payments industry, providing a deeper layer of defense to protect consumers from scams, account takeover, card and payments fraud, which cost an estimated $42 billion in 2020. "The significance of this development goes beyond the scope of addressing enterprise financial crime. "The significance of this development goes beyond the scope of addressing enterprise financial crime. It's truly the next generation of machine learning," said Dave Excell, founder of Featurespace. A breakthrough in deep learning technology, this invention required an entirely new way to architect and engineer machine learning platforms. Automated Deep Behavioral Networks is a new architecture based on Recurrent Neural Networks that is only available through the latest version of the ARIC Risk Hub. Deep learning technology has various applications, such as in natural language processing for the prediction of the next word in a sentence, however its use in preventing fraud in card and payments fraud detection has not been optimized to protect companies and consumers from card and payments fraud. With this invention, that challenge is solved. Transactions are intermittent, making contextual understanding of time critical to predicting behavior. Previously, building effective machine learning models for fraud prevention required data scientists to have deep domain expertise to identify and select appropriate data features – a laborious, yet vital step. Featurespace Research developed Automated Deep Behavioral Networks to automate feature discovery and introduce memory cells with native understanding of the significance of time in transaction flows, improving upon the market-leading performance of the company's Adaptive Behavioral Analytics. Detecting fraud before the victim's money leaves the account is the best line of defense against scams, account takeover, card and payment fraud attacks. Excell continued, "As real-time payments, digital transformation and consumer demand require the instantaneous movement of money, our role is to ensure the industry has the best tools for protecting their organizations and consumers from financial crime.

U.K.-based Featurespace, which offers anti-fraud systems for financial institutions (FIs), on Thursday (Feb. In a press release, the company said the new product, intended for the card and payments industry, provides a "deeper layer of defense to protect consumers from scams, account takeover, card and payments fraud, which cost an estimated $42 billion in 2020." The new product is "truly the next generation of machine learning," said Dave Excell, founder of Featurespace. The company said that it involves "a breakthrough in deep learning technology" that is capable of pinpointing potential fraud before the victim's money is removed from their account. That serves as "the best line of defense against scams, account takeover, card and payment fraud attacks," the release stated.

Payments solutions provider Vesta today announced that it raised $125 million in capital, bringing its total raised to over $145 million. The company says it will use the financing to grow and accelerate the deployment of its fraud protection and ecommerce payment products. Payment fraud is pervasive -- in 2018, $24.26 billion was lost due to credit card fraud worldwide, reports Shift Processing. That same year, the rate of card fraud increased by nearly 20% as the U.S. took the lead in reported losses. Vesta says its AI-powered decisioning platform helps clients to assess the risk of this fraud and ultimately to prevent fraud from occurring, with connectors that tie into existing software from vendors including Magento, Shopify, WooCommerce, BigCommerce, and SAP Commerce Cloud.

AI and machine learning will continue to enable asset management improvements that also deliver exponential gains in IT security by providing greater endpoint resiliency in 2020. Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software, observes that "Keeping machines up to date is an IT management job, but it's a security outcome. Knowing what devices should be on my network is an IT management problem, but it has a security outcome. And knowing what's going on and what processes are running and what's consuming network bandwidth is an IT management problem, but it's a security outcome. I don't see these as distinct activities so much as seeing them as multiple facets of the same problem space, accelerating in 2020 as more enterprises choose greater resiliency to secure endpoints."

Artificial intelligence is set to improve cybersecurity in 2020, and you're about to find out how. As a result, businesses must consciously remain updated on legal requirements like CCPA and GDPR. Also, it's essential to stay on top of the latest industry trends now more than ever. Some major cybersecurity trends in 2019 include increased data privacy regulation, phishing attacks, IoT ransomware, among others. But, the most significant trend this year may be the increased investment in artificial intelligence.

With identity theft and account takeover on the rise, it's increasingly difficult for businesses to trust that someone is who they claim to be online. Join this exclusive interactive 1-2-1 interview where Robert Prigge, President, Jumio will share the latest identity verification and authentication trends and how you can leverage the power of biometrics, AI and the latest technologies to quickly verify the digital identities of new customers and existing users. Some questions to be tackled; What types of cybersecurity threats are on the rise? Is account takeover becoming a real threat in the financial services space? How will eKYC evolve over the next few years?

AI and machine learning will continue to enable asset management improvements that also deliver exponential gains in IT security by providing greater endpoint resiliency in 2020. Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software, observes that "Keeping machines up to date is an IT management job, but it's a security outcome. Knowing what devices should be on my network is an IT management problem, but it has a security outcome. And knowing what's going on and what processes are running and what's consuming network bandwidth is an IT management problem, but it's a security outcome. I don't see these as distinct activities so much as seeing them as multiple facets of the same problem space, accelerating in 2020 as more enterprises choose greater resiliency to secure endpoints."

Protecting user credentials from compromise is a nearly impossible task. Billions of credentials uncovered in data breaches are circulating online, and every month millions more are exposed, either through intrusions or unprotected servers. In addition, phishing attacks continue to dupe users into coughing up their credentials voluntarily. You'll always need layers of security controls to secure credentials. But when credential controls are bypassed--either by an external threat actor or an insider--user and entity behavioral analytics (UEBA) can help.