Analytics, artificial intelligence (AI) and big data – these conversations are no longer complete without the term “deep learning”, a powerful phrase that is increasingly becoming part of the business vocabulary as it recognises life-changing advantages.

Brendan Marr, best-selling author and keynote speaker on business, technology and big data, says it is with good reason, as deep learning “it is an approach to AI, which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionising many industries. Deep learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. ”

A deep learning website, simply registered as deeplearning.net, comes with the tagline ‘moving beyond shallow machine learning since 2006!’. On its home page it states that “Deep learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.”

This statement is in unison with the voice of veteran tech journalist, Mike Copeland, who in a multi-part series, published by deep learning expert, NVIDIA, explains the fundamentals of deep learning. He says: “Deep learning has enabled many practical applications of Machine Learning and by extension the overall field of AI. Deep learning breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. Driverless cars, better preventive healthcare, even better movie recommendations, are all here today or on the horizon. AI is the present and the future. With Deep learning’s help, AI may even get to that science fiction state we’ve so long imagined.”

Deep learning frameworks are only as powerful, in performance and scalability, as the quality of the smart offloading technologies backing them up. “Mellanox is enabling deep learning with powerful data-centric offload architecture that has been employed by the world’s most advanced machine learning platforms,” says Anton Jacobsz, managing director of value-added distributor, Networks Unlimited, a distribution partner of Mellanox.

Mellanox Technologies announced in June 2017 that the leading deep learning frameworks such as TensorFlow, Caffe2, Microsoft Cognitive Toolkit, and Baidu PaddlePaddle now leverage Mellanox’s smart offloading capabilities. Mellanox RDMA and In-Network Computing offloads and NVIDIA GPUDirect are key technologies enabling users to maximise their application performance and system efficiencies.

TensorFlow is an open source software library originally developed by researchers and engineers within Google’s Machine Intelligence research group. With the inclusion of RDMA technology in place of traditional TCP, TensorFlow data exchange performance between nodes was accelerated by 2X, enabling faster image processing.

Baidu’s PaddlePaddle (Parallel Distributed Deep Learning) is a flexible and scalable deep learning platform. PaddlePaddle supports a wide range of neural network architectures and optimisation algorithms, such that it is possible to leverage many CPUs and GPUs to accelerate training. PaddlePaddle leverages RDMA to achieve high throughput and performance, and takes advantage of the more advanced acceleration capabilities of the combined NVIDIA and Mellanox architectures to accelerate deep learning training time by 2X.

“Advanced deep neural networks depend upon the capabilities of smart interconnect to scale to multiple nodes, and move data as fast as possible, which speeds up algorithms and reduces training time,” said Gilad Shainer, vice president of marketing at Mellanox Technologies during the announcement. “By leveraging Mellanox technology and solutions, clusters of machines are now able to learn at a speed, accuracy and scale that push the boundaries of the most demanding cognitive computing applications.”

The announcement was also accompanied by a statement from Duncan Poole, director of platform alliances at NVIDIA: “Developers of deep learning applications can take advantage of optimised frameworks and NVIDIA’s upcoming NCCL 2.0 library, which implements native support for InfiniBand verbs and automatically selects GPUDirect RDMA for multi-node or NVIDIA NVLink when available for intra-node communications. NVIDIA NVLink is available in Pascal-based Tesla P100 systems, including the NVIDIA DGX-1 AI supercomputer, which has four Mellanox ConnectX-4 100 Gb/s adapters. This allows developers to focus on creating new algorithms and software capabilities, rather than performance tuning low-level communication collectives.”

In conclusion, deep learning is helping to solve numerous big data challenges – it is perhaps best summed up by elite deep learning researcher, Silvio Savarese, an associate professor of computer science at Stanford University and director of the school’s SAIL-Toyota Centre for AI Research, who says the following:

“Everything is powered by deep learning. We can do things we’ve never done before.”

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The cost of fraud is rising.  According to CNBC.com, fraud and identify theft cost consumers more than $16 billion in 2016 – nearly $1 billion more than in 2015.

LexisNexis research cited on chargebacks911.com finds that for each dollar lost to fraud, “online merchants can ultimately expect to lose $2.40 in revenue due to the associated fees, lost merchandise, sales potential, and more.”

Machine learning promises to become our best weapon in the war on fraud.  But, why, all of a sudden, is it the panacea to the world’s fraud epidemic, and just how will machine learning affect your own security strategy?

The Ultimate Supercomputer

New advances in technology and science have dramatically enhanced the performance of machine learning algorithms, making them much more capable than they were just a few years ago.

Today’s machine learning systems also vastly outperform the modern neural network, pulling in and distilling far greater amounts of data by comparison.

Plus, they streamline and automate fraud detection in unprecedented ways. This is because machine learning systems evolve and improve their performance over time, without explicit programming.

Not only do they “learn as they go,” but they also learn at a mind-boggling pace.  Machine learning platforms today can identify even the most obscure threats in real time, catching and blocking new instances of fraud as they occur.

Addressing Big Data Challenges

According to the Nilson Report, credit card transactions rose 48 percent, debit card transactions 46 percent, and electronic transactions 45 percent between 2010 and 2015, for a collective increase of 34.2 billion transactions annually.  The proliferation of data can weigh heavily on traditional fraud detection resources.

Transaction data typically spans disparate systems and applications as well, which further complicates fraud detection – especially with new mobile wallet, IoT, P2P and digital banking technologies hitting the market daily.  As LexisNexis reports, “fraud through remote channels is up to 7 times as difficult to prevent as in-person fraud.”

Fighting Fire with Fire

But the most compelling reason to embrace machine learning now is this: Fraudsters are constantly evolving their tactics, and they are starting to use the technology themselves.  

This means that when your credit union creates a new rule going forward, tech-savvy fraudsters will find it much easier to get around it.  Machine learning can deliver the speed and flexibility needed to stay ahead of their advancements.

CO-OP’s Vision

To protect credit unions and their members in this new era of fraud, our team at CO-OP is developing a machine learning platform that unifies transaction data across all our systems and applications.

Initially, the platform will work side by side with advanced neural network technology. Over time, we may switch to machine learning entirely or keep both systems in place as the ultimate safeguard.  In the near term, we expect to have the platform in place on the account side by the end of this year, with credit and debit systems to follow.

The Importance of Scale

Achieving scale is critical to the success of any machine learning implementation; the more data these systems can access, the better they perform.

This year, CO-OP is on track to process more than 4 billion transactions. While we won’t share real data across credit unions, we will aggregate it for modeling. This means that whether you’re a $3 billion credit union or a $300 million credit union, you’ll receive all the benefits of our new machine learning technology.

AI and Digital Transformation

Fraud remains a hot topic.  Because the cost of fraud is so high, our investment in machine learning will reap dividends for our organization and client credit union community for years to come.

The initial process of aggregating data alone brings with it far-reaching benefits.  An important step in our own digital transformation journey, data integration at CO-OP will enable, for example, advanced predictive analytics and other forms of AI.

Ultimately, machine learning is probably the most important technology to emerge in the past five years. The fact that credit unions will soon be able to put it to work full force – first against fraud, then to improve marketing and the member experience – is big news for the industry.

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