Artificial intelligence and machine learning have dominated headlines over the past year, changing the rules of the game across industries and countries. In digital banking, embracing these technologies is more of a top priority than ever before, with customer experience and bottomline performance at stake.

Artificial intelligence (AI) and machine learning (ML) are not going anywhere in financial services. Far from it: they keep cropping up in industry outlooks as one of the must-focus areas in 2019 and beyond. But what are AI and ML to begin with? AI is a catch-all term referring to “the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity”, as defined by McKinsey. As for ML, it’s basically an application of AI: “ML algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction.”

Let’s see a few current applications in the banking industry. Customers may interact with AI-powered virtual assistants and chatbots, and complex algorithms go through huge amounts of data behind the scenes to generate better customer insights that banks can in turn cash in on. But that’s not all: financial institutions can also make great use of artificial intelligence in back office operations, compliance, customer experience, product delivery, risk management as well as marketing.

And there’s still more. AI-driven data analytics can help banks understand their customers better and personalise products to satisfy the exact needs of each and every one of them. As The Financial Brand predicts, “In 2019, many banking organizations will go beyond personalization by segment, to develop individualized communication and experiences for the segment of one. This is the ultimate level of innovative personalization allowed through data, advanced analytics and digital technologies.” It’s a pretty long list for such short words, and that’s exactly why AI and ML shouldn’t be overlooked.

All eyes on AI-driven customer experiences

The digital consumer is no stranger to artificial intelligence thanks to tech and e-commerce giants that are fast becoming masters of everything AI and are setting the standard for other industries. Think Google, Amazon, Facebook and Apple, or simply GAFA. Clients will more and more expect, or rather demand, their service providers to know and understand them, the Digital Banking Report says. They’ve got used to GAFA’s automated assistants, such as Google Home, Apple’s Siri and Amazon’s Alexa, and it’s high time banks also jumped on these new channels, PwC suggests.

Recent survey findings also underscore the emergence of AI as a must-have business tool for banks. Marketforce’s latest retail banking report shows that three out of five banks already use chatbots, almost half the respondents deploy AI to personalise the customer experience and the same proportion of them have adopted it to offer value-added services. In addition, 39 percent already deliver robo-advice to customers and a further 22 percent have a pilot underway.

The case for artificial intelligence

But it’s not only the likes of the GAFA companies that banks could learn from: other retailers are also making good use of AI and ML systems to boost sales. The race is already on: “To stay competitive, banks need to remain cognizant of developments within and from outside the banking industry,” Capgemini warns. Here are some examples, compiled by BizTech magazine:

  • TGI Friday’s had collected data from point of sale systems, social media posts, credit card transactions and mobile devices, then used AI, bots and mobile apps to develop tailored ad campaigns. The technology was first put to the test in the summer of 2017, during a heatwave in New York. If an app user, say, tweeted that they were craving a Long Island Iced Tea, TGI Friday’s was straight on it. The bots flagged the post and the algorithms quickly matched it to the customer in the restaurant’s database. TGI Friday’s then sent a message to the person’s mobile, referencing the drink and inviting them to pop into a participating restaurant during happy hour. The result? Revenue soared by 70% during happy hour.
  • Coca-Cola used the data it piled up from self-service Freestyle soft drink fountains that allow customers to mix different drinks. There are more than 40,000 Freestyle units in the US, serving 14 million drinks in total every single day. Using the huge amounts of data it analysed, Coca-Cola rolled out Cherry Sprite and Cherry Sprite Zero last year. And Greg Chambers, global director of digital innovation at Coca-Cola, says that in the future, AI will play an even more important part in the group’s vending machines and chatbots.
  • Harley-Davidson deployed an AI-powered marketing platform called Albert from the company Albert Technologies to pump up sales leads at a dealership in New York. They launched their first AI campaign, “48 Bikes in 48 Hours,” in early 2016 to reduce seasonal overstock and determine how quickly Albert could ramp up its audience knowledge to meet the brand’s conversion goals. Prior to Albert, the all-time sales record of the dealership was eight motorcycles in a single weekend. In its first two-day campaign, Albert almost doubled this, selling as many as 15 bikes.

Little wonder that according to industry experts, the foundation for all banking trends in 2019 will be the use of data, AI and advanced analytics as reported in the Financial Brand’s 2019 Retail Banking Trends and Predictions survey. And it’s a trend that’s expected to shake up financial service providers all across the board from banking giants to small community banks. As David Kerstein, Founder of Peak Performance Group, puts it: “2019 will be the year that machine learning and artificial intelligence really begins to make a difference — not just in bank efficiency but more importantly in the customer experience. The cost and ease of implementation has decreased dramatically, putting technology within reach of even smaller financial institutions. One place where it will be most apparent is in small business lending, where automated ‘near instant’ decisions will become the norm for the majority of loans.”

Machine learning: banks go for big wins

Some of the digital leaders in the banking scene have already started the ball rolling, and with considerable success. Here come some industry examples, collected by Marketforce, Capgemini and the Bank Administration Institute (BAI), a US non-profit organization advising financial institutions:

  • Bank of America rolled out Erica, its virtual assistant, in 2018. The chatbot uses predictive analytics and cognitive messaging to provide 24/7 financial guidance to customers. Erica can help with balance information, simple transactions such as transferring money between accounts, view past transactions and schedule payments. Customers can also chat with Erica through voice or text message, similarly to Amazon’s Alexa.
  • BBVA, a Spanish pioneer of digital innovation, makes great use of big data to offer value-added services to its customers: Bconomy is a financial wellbeing tool, BBVA Valora assists users with calculating the best price at which to rent, sell or buy a home and Baby Planner helps them better understand how starting a family will affect their financial situation. The bank is also planning to roll out a new app that uses biometric technology to automate payments, and allows users to book tables at restaurants, place orders from their smartphone and leave after the meal without having to ask for the bill or pay manually.
  • Citibank has made a strategic investment in Feedzai, a leading data science company, and can now go through large amounts of data in a flash and alert customers of fraudulent activities in real time.
  • JP Morgan Chase launched its COiN (contract intelligence) platform to cut down on loan-servicing mistakes and time in 2017. The ML technology used can review 12,000 annual commercial credit agreements in a matter of seconds, saving about 360,000 hours of human work per year.
  • Wells Fargo piloted an AI-driven chatbot through Facebook Messenger as early as in 2017. It delivers live information to help customers make better financial decisions, from how much they spent on food the week before through account balances and payment due dates to the nearest ATM.
  • The Royal Bank of Scotland (RBS) is using an automated lending process to approve commercial real estate loans up to $2.7 million in less than 45 minutes. This process would normally take days. The 2017 AI-driven launch is part of the bank’s broader digital and innovation agenda. RBS has also adopted a cognitive chat bot, powered by IBM Watson Conversation, to answer customer queries.

Will you be next?

This post was originally published on 1 February 2018 and has been updated to include recent developments.

Rise of the sales machines: AI and ML in banking digital sales

Rise of the sales machines: AI and ML in banking digital sales

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