Artificial Intelligence

Year In AI: Higher Education For Machines

Artificial intelligence (AI) was one of 2017’s hottest industry buzzwords as many have begun turning to machines to solve problems that are simply too large for humans to calculate. 2017 has seen AI applied to fraud and security, identity verification, customer service, and many other use cases.

Once upon a time, AI was an academic pursuit — but now it has become more affordable and attainable to pursue on a smaller scale, opening it up to use by a variety of companies for a variety of purposes.

Feedzai recently told PYMNTS that Big Data paved the way for this shift, and that by 2020, U.S. companies could be saving as much as $60 billion thanks to the help of AI and machine learning, according to Forbes. Business management consultancy Accenture expects AI to add $8.3 trillion in economic activity for the U.S. by 2035.

It’s clear that this trend is building some significant momentum in the payments space and adjacent industries. These are some of the top developments of the past year.

 

Love/Hate/Hope/Fear

Earlier this month, PYMNTS asked 30 industry professionals what they thought had been the year’s biggest developments, and AI came up more than once (read more in the PYMNTS year-end eBook).

“Artificial Intelligence (AI) will soon be at the heart of every major technological system in the world,” predicted Brighterion CEO Akli Adjaoute – “including cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing (NLP), computer vision, electrical grids, nuclear power plants, air traffic control and the Internet of Things (IoT).”

CO-OP Financial Services CPO/CSO Shazia Manus noted that, according to Accenture Consulting, the majority of consumers (62 percent) say they are comfortable with AI, while 52 percent say they are already interacting with AI-powered apps on at least a monthly basis. This healthy level of consumer acceptance indicates strong momentum for AI and machine learning going into 2018 and beyond.

But Pew Research Center paints a slightly different picture, citing 72 percent of Americans expressing worry over AI advancements. However, this may be tied to fears of robots replacing human workers in low-skill labor environments like driving and fast-food preparation.

Still, the number of people who are actually willing to put their lives in AI’s hands by riding in a driverless car remains low. 60 percent said they would not want to ride in a driverless vehicle, and 30 percent believe the roads will become more dangerous, rather than safer, when AI takes the wheel.

 

AI Vs. Fraudsters

2017 was the year “rules-based” became a dirty word in AI. Rules-based algorithms do exactly what one may expect: they follow rules, nothing more, nothing less. They are based on past observation. While they’re good at going through lots of data really fast, they cannot exercise any sort of discretion.

If a fraudster figured out the rules and started to work around them, rules-based AI couldn’t catch him, because to that machine, the bad guy would appear to be playing by the rules. Only after a human identified the pattern and fed the machine a new set of rules would it be able to catch the fraudster.

A platform like, say, Fraugster, analyzes and learns from transactions in real time, collecting over 2,000 data points, such as name, email address and billing and shipping address. All of this data is then sent to be analyzed by the company’s artificial intelligence engine, which then reviews each transaction’s data individually for signs of fraud.

Neurala’s deep learning AI platform Neurala Brain, which powers toys, cameras, drones and self-driving cars, was developed to process information like the human brain – including learning autonomously and providing actionable information from cameras and sensors in real time.

Max Laemmle, CEO and cofounder of Fraugster, said, “Existing rule-based systems, as well as classical machine-learning solutions, are expensive and too slow to adapt to new fraud patterns in real time. We have invented a self-learning algorithm that mimics the thought process of a human analyst, but with the scalability of a machine, and gives decisions in as little as 15 milliseconds.”

At Radial, VP of Payments KC Fox says that, because fraudsters will always devise new ways to get around rules-based systems, AI is effective but not infallible in the fight against fraud. Humans are still necessary. Radial believes the best solution is a blend of man and machine.

CO-OP’s Manus agrees that advanced AI technology is within reach for not only the biggest and most well-heeled banks, but for America’s myriad credit unions as well – as long as it’s used intelligently with humans at the helm.

Conversely, Brighterion’s Adjaoute wouldn’t even call a machine “artificially intelligent” if it was not capable of figuring out patterns and learning from them on its own, without human intervention – and moreover, he believes such AIs already exist, and that humans must step aside and let them do their thing if they want the most effective fraud protection that is possible today.

 

AI For Customer Service

2017 has seen a proliferation of chatbots for customer service purposes.

Whether it’s for banking (meet KAI, the chatbot by Kasisto that not only helps customers transfer funds, but also answers questions and offers financial guidance) or commerce (shopping malls, Facebook Messenger), organizations are beginning to see AI as the most effective way to solve customers’ problems at the point of pain (though to be fair, some of these bots are more efficient than others).

There is talk of bringing AI into call centers to serve a similar purpose as voice technology improves. In neither case is the goal to replace human workers, but rather to harvest the low-hanging fruit of customer service.

What FAQs are agents sick of answering a thousand times a day? Are there checklists that a bot could run through with a caller, such as, “Is the computer plugged in?” “Is it turned on?” AI could help weed out some of the simpler and more repetitive tasks, freeing up agents to spend their time handling more complicated requests.

That improves the service experience for those with complex issues and also drives efficiency for the organization, whatever it may be – thus helping to drive down staffing and operational expenses.

 

Words Of Caution

CO-OP’s Manus noted that because AI is an exciting field right now, many organizations are working with it, whether or not it is the right tool for the job. The true value of AI, said Manus, is improving the user experience, from credit unions like CO-OP to credit card analysts looking to optimize their portfolios.

Brighterion’s Adjaoute cautions that not all AI is created equal, and although the technology is not new, it is just beginning to go mainstream – which means there could be hidden vulnerabilities in some systems.

His conclusion? One big year for AI was not enough to get the technology where it needs to be in terms of human acceptance, but it definitely propelled it forward by leaps and bounds.

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Latest Insights:

Our data and analytics team has developed a number of creative methodologies and frameworks that measure and benchmark the innovation that’s reshaping the payments and commerce ecosystem. In the November 2019 Mobile Order-Ahead Report, PYMNTS talks with Dan Wheeler, Wahlburgers’ SVP, on how the QSR balances security and seamlessness to secure its recently launched WahlClub loyalty program.

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