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Andy Renshaw, Feedzai: It crosses borders but doesnt have a passport. Its meant to protect people but can make them angry. Its competitive by nature but doesnt want you to fail. What is it?

If the PSD2 regulations and Strong Customer Authentication (SCA) feel like a riddle to you, youre not alone. SCA places strict two-factor authentication requirements upon financial institutions (FIs) at a time when FIs are facing stiff competition for customers. On top of that, the variety of payment types, along with the sheer number of transactions, continue to increase.

According to UK Finance, the number of debit card transactions surpassed cash transactions since 2017, while mobile banking surged over the past year, particularly for contactless payments. The number of contactless payment transactions per customer is growing; this increase in transactions also raises the potential for customer friction.

The number of transactions isnt the only thing thats shown an exponential increase; the speed at which FIs must process them is too. Customers expect to send, receive, and access money with the swipe of a screen. Driven by customer expectations, instant payments are gaining traction across the globe with no sign of slowing down.

Considering the sheer number of transactions combined with the need to authenticate payments in real-time, the demands placed on FIs can create a real dilemma. In this competitive environment, how can organisations reduce fraud and satisfy regulations without increasing customer friction?

For countries that fall under PSD2s regulation, the answer lies in the one known way to avoid customer friction while meeting the regulatory requirement: keep fraud rates at or below SCA exemption thresholds.

How machine learning keeps fraud rates below the exemption threshold to bypass SCA requirements

Demonstrating significantly low fraud rates allows financial institutions to bypass the SCA requirement. The logic behind this is simple: if the FIs systems can prevent fraud at such high rates, they've demonstrated their systems are secure without authentication.

SCA exemption thresholds are:

Exemption Threshold Value

Remote electronic card-based payment

Remote electronic credit transfers

EUR 500

below 0.01% fraud rate

below 0.01% fraud rate

EUR 250

below 0.06% fraud rate

below 0.01% fraud rate

EUR 100

below 0.13% fraud rate

below 0.015% fraud rate

Looking at these numbers, you might think that achieving SCA exemption thresholds is impossible. After all, bank transfer scams rose 40% in the first six months of 2019. But state-of-the-art technology rises to the challenge of increased fraud. Artificial intelligence, and more specifically machine learning, makes achieving SCA exemption thresholds possible.

How machine learning achieves SCA exemption threshold values

Every transaction has hundreds of data points, called entities. Entities include time, date, location, device, card, cardless, sender, receiver, merchant, customer age the possibilities are almost endless. When data is cleaned and connected, meaning it doesnt live in siloed systems, the power of machine learning to provide actionable insights on that data is historically unprecedented.

Robust machine learning technology uses both rules and models and learns from both historical and real-time profiles of virtually every data point or entity in a transaction. The more data we feed the machine, the better it gets at learning fraud patterns. Over time, the machine learns to accurately score transactions in less than a second without the need for customer authentication.

Machine learning creates streamlined and flexible workflows

Of course, sometimes, authentication is inevitable. For example, if a customer who generally initiates a transaction in Brighton, suddenly initiates a transaction from Mumbai without a travel note on the account, authentication should be required. But if machine learning platforms have flexible data science environments that embed authentication steps seamlessly into the transaction workflow, the experience can be as customer-centric as possible.

Streamlined workflows must extend to the fraud analysts job

Flexible workflows arent just important to instant payments theyre important to all payments. And they cant just be a back-end experience in the data science environment. Fraud analysts need flexibility in their workflows too. They're under pressure to make decisions quickly and accurately, which means they need a full view of the customer not just the transaction.

Information provided at a transactional level doesnt allow analysts to connect all the dots. In this scenario, analysts are left opening up several case managers in an attempt to piece together a complete and accurate fraud picture. Its time-consuming and ultimately costly, not to mention the wear and tear on employee satisfaction. But some machine learning risk platforms can show both authentication and fraud decisions at the customer level, ensuring analysts have a 360-degree view of the customer.

Machine learning prevents instant payments from becoming instant losses

Instant payments can provide immediate customer satisfaction, but also instant fraud losses. Scoring transactions in real-time means institutions can increase the security around the payments going through their system before its too late.

Real-time transaction scoring requires a colossal amount of processing power because it cant use batch processing, an efficient method when dealing with high volumes of data. Thats because the lag time between when a customer transacts and when a batch is processed makes this method incongruent with instant payments. Therefore, scoring transactions in real-time requires supercomputers with super processing powers. The costs associated with this make hosting systems on the cloud more practical than hosting at the FIs premises, often referred to as on prem. Of course, FIs need to consider other factors, including cybersecurity concerns before determining where they should host their machine learning platform.

Providing exceptional customer experiences by keeping fraud at or below PSD2s SCA threshold can seem like a magic trick, but its not. Its the combined intelligence of humans and machines to provide the most effective method we have today to curb and prevent fraud losses. Its how we solve the friction-security puzzle and deliver customer satisfaction while satisfying SCA.

About Andy Renshaw

Andy Renshaw, Vice President of Banking Solutions at Feedzai, has over 20 years of experience in banking and the financial services industry, leading large programs and teams in fraud management and AML. Prior to joining Feedzai, Andy held roles in global financial institutions such as Lloyds Banking Group, Citibank, and Capital One, where he helped fight against the ever-evolving financial crime landscape as a technical expert, fraud prevention expert, and a lead product owner for fraud transformation.

About Feedzai

Feedzai is the market leader in fighting fraud with AI. Were coding the future of commerce with todays most advanced risk management platform powered by big data and machine learning. Founded and developed by data scientists and aerospace engineers, Feedzai has one mission: to make banking and commerce safe. The worlds largest banks, processors, and retailers use Feedzais fraud prevention and anti-money laundering products to manage risk while improving customer experience.

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PSD2: How machine learning reduces friction and satisfies SCA - The Paypers

When searching through theoretical lists of possible new materials for particular applications, such as batteries or other energy-related devices, there are often millions of potential materials that could be considered, and multiple criteria that need to be met and optimized at once. Now, researchers at MIT have found a way to dramatically streamline the discovery process, using a machine learning system.

As a demonstration, the team arrived at a set of the eight most promising materials, out of nearly 3 million candidates, for an energy storage system called a flow battery. This culling process would have taken 50 years by conventional analytical methods, they say, but they accomplished it in five weeks.

The findings are reported in the journal ACS Central Science, in a paper by MIT professor of chemical engineering Heather Kulik, Jon Paul Janet PhD 19, Sahasrajit Ramesh, and graduate student Chenru Duan.

The study looked at a set of materials called transition metal complexes. These can exist in a vast number of different forms, and Kulik says they are really fascinating, functional materials that are unlike a lot of other material phases. The only way to understand why they work the way they do is to study them using quantum mechanics.

To predict the properties of any one of millions of these materials would require either time-consuming and resource-intensive spectroscopy and other lab work, or time-consuming, highly complex physics-based computer modeling for each possible candidate material or combination of materials. Each such study could consume hours to days of work.

Instead, Kulik and her team took a small number of different possible materials and used them to teach an advanced machine-learning neural network about the relationship between the materials chemical compositions and their physical properties. That knowledge was then applied to generate suggestions for the next generation of possible materials to be used for the next round of training of the neural network. Through four successive iterations of this process, the neural network improved significantly each time, until reaching a point where it was clear that further iterations would not yield any further improvements.

This iterative optimization system greatly streamlined the process of arriving at potential solutions that satisfied the two conflicting criteria being sought. This kind of process of finding the best solutions in situations, where improving one factor tends to worsen the other, is known as a Pareto front, representing a graph of the points such that any further improvement of one factor would make the other worse. In other words, the graph represents the best possible compromise points, depending on the relative importance assigned to each factor.

Training typical neural networks requires very large data sets, ranging from thousands to millions of examples, but Kulik and her team were able to use this iterative process, based on the Pareto front model, to streamline the process and provide reliable results using only the few hundred samples.

In the case of screening for the flow battery materials, the desired characteristics were in conflict, as is often the case: The optimum material would have high solubility and a high energy density (the ability to store energy for a given weight). But increasing solubility tends to decrease the energy density, and vice versa.

Not only was the neural network able to rapidly come up with promising candidates, it also was able to assign levels of confidence to its different predictions through each iteration, which helped to allow the refinement of the sample selection at each step. We developed a better than best-in-class uncertainty quantification technique for really knowing when these models were going to fail, Kulik says.

The challenge they chose for the proof-of-concept trial was materials for use in redox flow batteries, a type of battery that holds promise for large, grid-scale batteries that could play a significant role in enabling clean, renewable energy. Transition metal complexes are the preferred category of materials for such batteries, Kulik says, but there are too many possibilities to evaluate by conventional means. They started out with a list of 3 million such complexes before ultimately whittling that down to the eight good candidates, along with a set of design rules that should enable experimentalists to explore the potential of these candidates and their variations.

Through that process, the neural net both gets increasingly smarter about the [design] space, but also increasingly pessimistic that anything beyond what weve already characterized can further improve on what we already know, she says.

Apart from the specific transition metal complexes suggested for further investigation using this system, she says, the method itself could have much broader applications. We do view it as the framework that can be applied to any materials design challenge where you're really trying to address multiple objectives at once. You know, all of the most interesting materials design challenges are ones where you have one thing you're trying to improve, but improving that worsens another. And for us, the redox flow battery redox couple was just a good demonstration of where we think we can go with this machine learning and accelerated materials discovery.

For example, optimizing catalysts for various chemical and industrial processes is another kind of such complex materials search, Kulik says. Presently used catalysts often involve rare and expensive elements, so finding similarly effective compounds based on abundant and inexpensive materials could be a significant advantage.

This paper represents, I believe, the first application of multidimensional directed improvement in the chemical sciences, she says. But the long-term significance of the work is in the methodology itself, because of things that might not be possible at all otherwise. You start to realize that even with parallel computations, these are cases where we wouldn't have come up with a design principle in any other way. And these leads that are coming out of our work, these are not necessarily at all ideas that were already known from the literature or that an expert would have been able to point you to.

This is a beautiful combination of concepts in statistics, applied math, and physical science that is going to be extremely useful in engineering applications, says George Schatz, a professor of chemistry and of chemical and biological engineering at Northwestern University, who was not associated with this work. He says this research addresses how to do machine learning when there are multiple objectives. Kuliks approach uses leading edge methods to train an artificial neural network that is used to predict which combination of transition metal ions and organic ligands will be best for redox flow battery electrolytes.

Schatz says this method can be used in many different contexts, so it has the potential to transform machine learning, which is a major activity around the world.

The work was supported by the Office of Naval Research, the Defense Advanced Research Projects Agency (DARPA), the U.S. Department of Energy, the Burroughs Wellcome Fund, and the AAAS Mar ion Milligan Mason Award.

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Neural networks facilitate optimization in the search for new materials - MIT News

AI (artificial Intelligence) concept.

During the past decade, deep learning has seen groundbreaking developments in the field of AI (Artificial Intelligence). But what is this technology? And why is it so important?

Well, lets first get a definition of deep learning.Heres how Kalyan Kumar, who is the Corporate Vice President & Chief Technology Officer of IT Services at HCL Technologies, describes it:Have you ever wondered how our brain can recognize the face of a friend whom you had met years ago or can recognize the voice of your mother among so many other voices in a crowded marketplace or how our brain can learn, plan and execute complex day-to-day activities? The human brain has around 100 billion cells called neurons. These build massively parallel and distributed networks, through which we learn and carry out complex activities. Inspired from these biological neural networks, scientists started building artificial neural networks so that computers could eventually learn and exhibit intelligence like humans.

Think of it this way:You first will start with a huge amount of unstructured data, say videos.Then you will use a sophisticated model that will process this information and try to determine underlying patterns, which are often not detectable by people.

During training, you define the number of neurons and layers your neural network will be comprised of and expose it to labeled training data, said Brian Cha, who is a Product Manager and Deep Learning evangelist at FLIR Systems.With this data, the neural network learns on its own what is good or bad. For example, if you want the neural network to grade fruits, you would show it images of fruits labeled Grade A, Grade B, Grade C, and so on. The neural network uses this training data to extract and assign weights to features that are unique to fruits labelled good, such as ideal size, shape, color, consistency of color and so on. You dont need to manually define these characteristics or even program what is too big or too small, the neural network trains itself using the training data. The process of evaluating new images using a neural network to make decisions on is called inference. When you present the trained neural network with a new image, it will provide an inference, such as Grade A with 95% confidence.

What about the algorithms?According to Bob Friday, who is the CTO of Mist Systems, a Juniper Networks company, There are two kinds of popular neural network models for different use cases: the Convolutional Neural Network (CNN) model is used in image related applications, such as autonomous driving, robots and image search. Meanwhile, the Recurrent Neural Network (RNN) model is used in most of the Natural Language Processing-based (NLP) text or voice applications, such as chatbots, virtual home and office assistants and simultaneous interpreters and in networking for anomaly detection.

Of course, deep learning requires lots of sophisticated tools.But the good news is that there are many available and some are even free like TensorFlow, PyTorch and Keras.

There are also cloud-based server computer services, said Ali Osman rs, who is the Director of AI Strategy and Strategic Partnerships for ADAS at NXP Semiconductors.These are referred to as Machine Learning as a Service (MLaaS) solutions. The main providers include Amazon AWS, Microsoft Azure, and Google Cloud.

Because of the enormous data loads and complex algorithms, there is usually a need for sophisticated hardware infrastructure.Keep in mind that it can sometimes take days to train a model

The unpredictable process of training neural networks requires rapid on-demand scaling of virtual machine pools, said Brent Schroeder, who is the Chief Technology Officer at SUSE. Container based deep learning workloads managed by Kubernetes can easily be deployed to different infrastructure depending upon the specific needs. An initial model can be developed on a small local cluster, or even an individual workstation with a Jupyter Notebook. But then as training needs to scale, the workload can be deployed to large, scalable cloud resources for the duration of the training. This makes Kubernetes clusters a flexible, cost-effective option for training different types of deep learning workloads.

Deep learning has been shown to be quite efficient and accurate with models.Probably the biggest advantage of deep learning over most other machine learning approaches is that the user does not need to worry about trimming down the number of features used, said Noah Giansiracusa, who is an Assistant Professor of Mathematical Sciences at Bentley University.With deep learning, since the neurons are being trained to perform conceptual taskssuch as finding edges in a photo, or facial features within a facethe neural network is in essence figuring out on its own which features in the data itself should be used.

Yet there are some notable drawbacks to deep learning.One is cost.Deep learning networks may require hundreds of thousands or millions of hand-labeled examples, said Evan Tann, who is the CTO and co-founder of Thankful.It is extremely expensive to train in fast timeframes, as serious players will need commercial-grade GPUs from Nvidia that easily exceed $10k each.

Deep learning is also essentially a black box.This means it can be nearly impossible to understand how the model really works!

This can be particularly problematic in applications that require such documentation like FDA approval of drugs and medical devices, said Dr. Ingo Mierswa, who is the Founder of RapidMiner.

And yes, there are some ongoing complexities with deep learning models, which can create bad outcomes.Say a neural network is used to identify cats from images, said Yuheng Chen, who is the COO of rct studio.It works perfectly, but when we want it to identify cats and dogs at the same time, its performance collapses.

But then again, there continues to be rapid progress, as companies continue to invest substantial amounts into deep learning.For the most part, things are still very much in the nascent stages.

The power of deep learning is what allows seamless speech recognition, image recognition, and automation and personalization across every possible industry today, so it's safe to say that you are already experiencing the benefits of deep learning, said Sajid Sadi, who is the VP of Research at Samsung and the Head of Think Tank Team.

Tom (@ttaulli) is the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems.

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Deep Learning: What You Need To Know - Forbes

Business Overview

Microsoft AI is a platform used to develop AI solutions in conversational AI, machine learning, data sciences, robotics, IoT, and more.

Microsoft AI prides itself on driving innovation through; protecting wildlife, better brewing, feeding the world and preserving history.

Its Cognitive Services is described as a comprehensive family of AI services and cognitive APIs to help you build intelligent apps.

Executives

Tom Bernard Krake is the Azure Cloud Executive at Microsoft, responsible for leveraging and evaluating the Azure platform. Tom is joined by a team of experienced executives to optimise the Azure platform and oversee the many cognitive services that it provides.

Notable customers

Uber uses Cognitive Services to boost its security through facial recognition to ensure that the driver using the app matches the user that is on file.

KPMG helps financial institutions save millions in compliance costs through the use of Microsofts Cognitive Services. They do this through transcribing and logging thousands of hours of calls, reducing compliance costs by as much as 80 per cent.

Jet.com uses Cognitive Services to provide answers to its customers by infusing its customer chatbot with the intelligence to communicate using natural language.

The services:

Decision - Make smarter decisions faster through anomaly detectors, content moderators and personalizers.

Language - Extract meaning from unstructured text through the immersive reader, language understanding, Q&A maker, text analytics and translator text.

Speech - Integrate speech processing into apps and services through Speech-to-text, Text to speech, Speech translation and Speaker recognition.

Vision - Identify and analyse content within images, videos and digital ink through computer vision, custom vision, face, form recogniser, ink recogniser and video indexer.

Web Search -Find what you are looking for through the world-wide-web through autosuggest, custom search, entity search, image search, news search, spell check, video search, visual search and web search.

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What are the top AI platforms? - Gigabit Magazine - Technology News, Magazine and Website

Originally published in TechCrunch, March 16, 2020

In a briefing on Monday, research leaders across tech, academia and the government joined the White House to announce an open data set full of scientific literature on the novel coronavirus. The COVID-19 Open Research Dataset, known as CORD-19, will also add relevant new research moving forward, compiling it into one centralized hub. The new data set is machine readable, making it easily parsed for machine learning purposes a key advantage according to researchers involved in the ambitious project.

In a press conference, U.S. CTO Michael Kratsios called the new data set the most extensive collection of machine readable coronavirus literature to date. Kratsios characterized the project as a call to action for the AI community, which can employ machine learning techniques to surface unique insights in the body of data. To come up with guidance for researchers combing through the data, the National Academies of Sciences, Engineering, and Medicine collaborated with the World Health Organization to come up with high priority questions about the coronavirus related to genetics, incubation, treatment, symptoms and prevention.

The partnership, announced today by the White House Office of Science and Technology Policy, brings together the Chan Zuckerberg Initiative, Microsoft Research, the Allen Institute for Artificial Intelligence, the National Institutes of Healths National Library of Medicine, Georgetown Universitys Center for Security and Emerging Technology, Cold Spring Harbor Laboratory and the Kaggle AI platform, owned by Google.

The database brings together nearly 30,000 scientific articles about the virus known as SARS-CoV-2. as well as related viruses in the broader coronavirus group. Around half of those articles make the full text available. Critically, the database will include pre-publication research from resources like medRxiv and bioRxiv, open access archives for pre-print health sciences and biology research.

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With Launch of COVID-19 Data Hub, The White House Issues A 'Call To Action' For AI Researchers - Machine Learning Times - machine learning & data...