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Deep Labs Featured in Global Finance Magazine on How Artificial intelligence is Moving from the Consumer to the Business Sector

Artificial intelligence is creeping into almost all aspects of our lives. Everything from mobile banking apps and chatbots to voice-activated home assistants and self-service checkouts at supermarkets now have some form of AI embedded in them. Given the technology’s pervasiveness in the consumer space, it was only a matter of time before it started to permeate our business lives as well.

Most companies plan to invest in AI in the coming months, according to research by Accenture. However, more than 50% are still in pilot mode or the early phases of adoption while some have not even reached the starting blocks—an indication of the implementation challenges.

Scott Edington, CEO of  Deep Labs, an AI start-up, says AI has a journey ahead. “You don’t just go from zero to 100 overnight,” he says. “You start introducing some of these technologies in a supervised mode.”

Even for companies that are wedded to legacy technologies like ERP and treasury management systems, Edington says it’s not a case of having to “rip and replace” these systems; AI plugs into existing technologies that companies already use.

Read the full article here.
account takeover behavior analytics deep learning device recognition fraud detection fraud prevention Identity machine intelligence risk analysis risk engine signals intelligence supervised machine learning unsupervised machine learning

Deep Labs Featured in CBR on the European Banking Authority’s View on Biometrics for PSD2

When it comes to body parts the European Banking Authority (EBA) is agnostic: heart, veins, retina – even your fingers wandering idiosyncratically over a keyboard.

The regulator, needless to say, was not talking about harvesting organs when it got open minded about human anatomy in an opinion published this summer, but biological and behavioural biometrics: specifically, the types it will accept as “inherence”.

What is “inherent” to someone making a payment (and few would deny that veins and a heart are pretty inherent indeed) is about to get very important. Here’s why.

European Banking Authority: OK With Your Heart Rate

Under the looming “Strong Customer Authentication” requirements of Europe’s Second Payment Services Directive (PSD2), payments and ecommerce providers need to introduce two-factor authentication (2FA) for payments of over €30 (£27).

Future transactions above this SCA threshold have to satisfy two of the three elements the EBA deems satisfactory authentication methods: something you are, e.g. biometrics; something you know, e.g. a password or PIN; and/or something you have.

While the strict new rules were set to come into force on September 14, UK providers have won an 18-month extension from the Financial Conduct Authority (FCA).

One of the things they need to think about as they belatedly prepare their systems for SCA is what the “2” in “2FA” is going to be.

It could be your palm geometry…

In a payments biometrics opinion in June, the EBA took a broad view of what constitutes adequate biometric inherence.

“The EBA is of the view that inherence, which includes biological and behavioural biometrics, relates to physical properties of body parts, physiological characteristics and behavioural processes created by the body, and any combination of these”

“Inherence”, it noted, “is the category of elements that is the most innovative and fastest moving, with new approaches continuously entering the market.”

It approved: retina and iris scanning, fingerprint scanning, vein recognition, face and hand geometry (identifying the shape of the user’s face/hand), voice recognition, keystroke dynamics (identifying a user by the way they type and swipe), the angle at which a user typically holds their device, and their heart rate.

Are these really viable options? We asked Michael Lynch, Chief Strategy and Product Officer, Deep Labs. He said: “There are two important dynamics for the use of the inherence technologies. First is the acceptance by the consumer to use such technologies, and the second is the efficacy of the technology.

Read the full article here.
account takeover behavior analytics deep learning device recognition fraud detection fraud prevention Identity machine intelligence risk analysis risk engine signals intelligence supervised machine learning unsupervised machine learning

Deep Labs Featured in Banking Dive about how a Silicon Valley Startup is Fighting False Declines

Deep Labs Featured in Banking Dive about how a Silicon Valley Startup is Fighting False Declines

Three top banks employ Deep Labs’ platform to differentiate between transactions that are anomalies and those that are fraud.

Financial institutions are fighting the wrong war when it comes to credit card fraud, said Scott Edington, CEO of San Francisco-based Deep Labs.

Fraudulent transactions are expected to total $32 billion worldwide in 2019, according to Nilson. But banks should be paying more attention to false positive declines, Edington said.

False declines — payment-card transactions that are incorrectly flagged and canceled — led to losses of $331 billion globally in 2018, said Edington, citing a study by research and advisory firm Aite Group.

Read the full article here.
account takeover behavior analytics deep learning device recognition fraud detection fraud prevention Identity machine intelligence risk analysis risk engine signals intelligence supervised machine learning unsupervised machine learning

From Mobile Payments Today: As PSD2 deadline draws near, calls for delay grow louder

From Mobile Payments Today: As PSD2 deadline draws near, calls for delay grow louder

According to Mobile Payments Today, as the mid-September deadline draws closer for a major transition of customer authentication rules for ecommerce transactions in Europe, a growing number of voices are seeking a pause to prevent what they fear could be a logistical nightmare for merchants, banks, payment processors and consumers.

The issue involves the transition to Secure Customer Authentication, a move by European regulators under what is called the second Payment Services Directive (PSD2) to lower the risk of fraud as more consumers make purchases through ecommerce channels.

Essentially future purchases will require all transactions to be authenticated using two of three authentication methods:

  1. Something a customer knows, like a PIN code or password.
  2. Something a customer has, like a smartphone or token.
  3. Something that uniquely identifies the customer, like a fingerprint or facial recognition.

“The PSD2 directive has faced strong opposition in the market as the timeline to implement a solution with the complexity of the new Strong Customer Authentication rules for ecommerce transactions has always been seen as a challenge,” Scott Edington, CEO of Deep Labs told Mobile Payments Today via email. “The two-step verification process, with many requirements within that new process, requires a high degree of technical and security knowledge and time to build and put into production that new process.”

The San Francisco-based digital security firm has used artificial intelligence to develop solutions to meet the new PSD2 requirements. In March, for example, it launched “Deep Identity” to track transactional risk analysis, which leverages data signals and context aware machine-learning to help confirm risk levels.

Read the full article here

account takeover behavior analytics deep learning device recognition fraud detection fraud prevention Identity machine intelligence risk analysis risk engine signals intelligence supervised machine learning unsupervised machine learning

Deep Labs featured in FinExtra: Artificial Intelligence – A multidimensional technology that can mitigate risk and increase revenue

rtificial Intelligence - A multidimensional technology that can mitigate risk and increase revenue

In this contributing post to FinExtra, Deep Labs Chief Strategy Officer Michael Lynch discusses that within organizations, fraud professionals and those responsible for revenue growth have been working against each other for decades. You can stop fraud almost completely of course, by making it nearly impossible for your customers to transact by placing limits on the transaction or transfer amount, excessive manual reviews, and requesting additional authentication information that leads to abandonment. 

Or you can maximize revenue by taking on more risk by minimizing the consumer security steps and security defenses. 

Understanding and recognizing your consumer from a 360-degree view, both with respect to risk and fraud mitigation, to their propensity, motivation to buy, and predicting their next product need is now possible using machine learning and artificial intelligence.

Read the full article here

account takeover behavior analytics deep learning device recognition fraud detection fraud prevention Identity machine intelligence risk analysis risk engine signals intelligence supervised machine learning unsupervised machine learning

Deep Labs featured in PaymentsSource: As digital payments take hold, AI can fill tech voids

As digital payments take hold, AI can fill tech voids

In this article, Deep Labs Chief Strategy Officer Michael Lynch discusses how artificial intelligence is disrupting markets and providing a competitive advantage for early adopters for those investing in the technology now.

Many banks, payment companies and fintechs are attempting to gain a competitive edge in the marketplace by embracing artificial intelligence, via innovative partnerships, strategic investments, and attempting to develop their own cutting-edge technology.

Examples include automation of manual processes and repetitive tasks, predicting how to better serve customers with advice or predicting their next product need, fraud and risk management, chatbots and intelligent virtual assistants, and personalized user experiences.

Read the full article here

account takeover behavior analytics deep learning device recognition fraud detection fraud prevention Identity machine intelligence risk analysis risk engine signals intelligence supervised machine learning unsupervised machine learning

Deep Labs Selected for Plug and Play’s Spring 2019 FinTech Accelerator

Deep Labs Selected for Plug and Play’s Spring 2019 FinTech Accelerator

SUNNYVALE, CA., March 14, 2019 — Plug and Play has selected 135 startups to participate in their Spring 2019 accelerator batches. Entrepreneurs from each startup will take part in one of the following programs: Brand & Retail, Cybersecurity, Energy & Sustainability, Fintech, Food & Beverage, New Materials & Packaging, and Supply Chain. Deep Labs has been selected to participate in the Fintech accelerator Batch 9 program.

“It is fantastic to see such a talented group of entrepreneurs accepted into our programs. We aim to give our startups the best chance for success and these next three months will give them the tools and connections they need to thrive,” said Saeed Amidi, Founder and CEO of Plug and Play. “I am excited to see what they can accomplish when integrated into our ecosystem.”

Read full article here.

account takeover behavior analytics Cybersecurity deep learning device recognition Fintech fraud detection fraud prevention Identity machine intelligence Plug and Play risk analysis risk engine signals intelligence startup supervised machine learning unsupervised machine learning
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