Share:

Tech talk is easy to oversimplify. You’ve undoubtedly heard of artificial intelligence, or AI, and maybe you’ve heard of machine learning, too. There’s also virtual reality and its not-so-distant cousin, augmented reality. These concepts all describe somewhat similar types of technology, but don’t make the mistake of using them interchangeably. Each one has a unique utility that makes it ideal for specific applications.

For example, you probably know that AI is the science of programming computers to be “smart”– that is, to complete tasks that typically require human intelligence. You likely encounter some form of artificial intelligence in your everyday life. When you ask Siri or Alexa what the weather will be like next week, you’re using one of the most common consumer applications of artificial intelligence.

Chatbots are another example of applied AI and many financial services have adopted them to handle basic customer needs such as looking up recent transactions or reviewing account balances. A recent survey from digital banking provider Personetics found that more than three-quarters of financial institutions surveyed believe chatbots are a “viable commercial solution” now or within the next couple of years.

Bank of America launched its own digital banking assistant, Erica, late last year. Once deployed, Erica will not only help customers with basic banking tasks, but will also predict bill payments, track customer spending and keep tabs on FICO scores. This use of applied AI is similar to the online budgeting app, Mint.com, which uses artificial intelligence to track spending and send users reminders when bills are due or when they’ve exceeded spending in certain categories. Mint also intelligently categorizes certain transactions automatically based on customer data. If you spend $50 at Applebee’s, Mint will automatically categorize this as restaurant spending, based on the data it has on hand. This is where machine learning comes in.

Machine learning is a type of artificial intelligence in which computers, machines, or applications are able to learn on their own without being programmed. In other words, computers can change and evolve themselves when they’re exposed to new data.

“Machine learning is used to reinforce and adapt the AI through either simulations or big sets of data,” said Elbert Perez, CTO of Doghead Simulations, a virtual reality conferencing company. “The applications are almost limitless as every industry generates data that machine learning can churn through.”

Instead of teaching machines to do every single task, programmers use neural networks to teach machines to think for themselves, in a sense, then give them access to data so they can do just that. A neural network is essentially a computer system that’s designed to sort and process information similar to the way the human brain sorts and processes information. The innovation of machine learning is largely due to the internet, which stores and transmits massive amounts of data constantly. The more access the AI has to data, more easily it can learn on its own.

Engineers use neural networks to program computers to recognize certain images, for example, which is how Google Photos can now tag your cat in pictures. “Google is a big believer in machine learning as they use it for image searching, Google street view, Google maps, pretty much every Google product,” Perez said.

While still an emerging technology, machine learning is becoming much more mainstream. In the credit union space, CO-OP is partnering with the technology firm Feedzai to develop a tool that uses machine learning to detect fraud. Other financial services use machine learning to predict customer default. The peer-to-peer loan service Lending Club uses a machine learning algorithm to analyze borrower data, including credit history and other financial information. When borrowers apply, Lending Club assigns the loan a letter-based rating, which potential lenders can then review and decide if they want to finance.

Virtual reality is another emerging technology that companies are having fun with. “Innovation-wise companies are just starting to scratch the surface,“ Perez said. “Developers like myself explore and push the boundaries of what we can do with it.”

Virtual reality (VR) is a computer-generated image of a three-dimensional environment and virtual reality headsets like the Oculus Rift allow users to play games, use apps or connect with friends and chat in a virtual world. Some fintech companies envision using virtual reality to make data more approachable. Fidelity Investments released their own app for Oculus that takes users into a three-dimensional environment to help them visualize investment data that’s typically limited to basic graphs and charts. Mastercard has been working on ways to make payments possible inside VR worlds as well as create VR banks – imagine visiting a “brick and mortar” credit union from the convenience of your VR headset.

Augmented reality, or AR, is a bit different. While Perez says there’s a big push to wrangle these technologies under the umbrella term XR, or extended reality, AR offers businesses some flexibility that VR might not.

“Virtual reality is a contained virtual experience; the user is entirely immersed in the virtual environment and cannot see the outside physical world. Augmented reality, on the other hand, is a digital reality integrated with the physical environment,” said Martina Welkhoff, Board President of Seattle Women in Tech and Founder of Convene VR. “In other words, the user can still see their physical environment and engage with the physical world while using augmented reality.”

In other words, AR superimposes three-dimensional images onto our natural environment – think of the Pokemon Go craze. Unlike VR, AR isn’t limited to a headset, which potentially means more possibilities. “Outside of gaming, AR is mostly being used for workplace collaboration, medicine and real estate,” said Welkhoff.

In a white paper, Infosys suggests that the financial services industry might use this technology to help customers find ATMs and branches, book appointments, look up location-based merchant offers, and search for properties and get home loan information.

All of these technologies offer exciting opportunities for businesses and industries. While still in their initial stages, AR, VR, AI and machine learning all have the huge, looming potential to impact many different industries, financial services included. Further, these technologies are expected to evolve along with biometric technology like voice and facial recognition, which would also allow companies to improve their security – a chief necessity in the financial world.

Perez says that AR and VR offer potential for almost every field. In the automotive industry, VR is replacing clay models as a way to showcase concepts internally. Clay models are time-consuming to build and make changes to quickly, Perez explained. And any company can use VR for phone meetings and conferences.

“Communications using voice only have always been the bane of a every single distributed team. It is awkward, the time delay between voice makes it hard to have multi person conversations. But with VR, you can bring back a lot of the social queues we are used to in real life, like hand gestures, head movements and spatial awareness,” Perez said. “Every single industry has a use for VR and AR.”

If you’re interested in making sense of Artificial Intelligence, Virtual Reality and Everything in Between, please sign up to receive the next issue of THINK Review Magazine: The Data Strategy Issue.