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06. 13. 2018

How can banks put AI to work? Interview with Adam Shardlow at Royal Bank of Scotland (RBS)

AI in banking webinar

“Running a lot faster” is something big tech firms could teach banks that are yet to dip their toes in using artificial intelligence (AI), says Adam Shardlow, Lead Journey Manager for AI at the Royal Bank of Scotland (RBS), who has previously also worked for Amazon. He believes AI and machine learning (ML) makes it possible for financial institutions to personalize their offers and revolutionize the way they communicate with customers. We sat down to discuss the opportunities AI offers to banks to get a single view of the customer, enhance customer relations and boost digital sales.

Adam is going to be one of the speakers at W.UP’s upcoming free webinar on AI & ML on 27 June, where we will share best practices in AI that banks can put into practice with little effort. Participants will also learn all about developing AI capabilities to increase digital sales, unlocking the power of ML in analytics, real-life use cases of AI in digital banking and innovations that help banks explore AI and provide five-star user experiences. Get more details and sign up here.

Adam Shardlow, RBS

Adam Shardlow, RBS

AI is a big thing in banking now, but its practical applications remain a mystery for many. How do you see the current state of AI and its future evolution in banking, especially in digital sales?

You see lots of customer-facing AI applications around improving customer service, allowing clients to transact with a bank in any way they want, through any channel they want and get the level of information that they require – in ways, which they have never done before. In the future, these applications will allow customers to interact with their banks more frequently and on a more micro level.

On the other side, internal AI applications make it possible for financial institutions to create far more personalized products than ever before. The traditional banking model has always been around ‘one size fits all’, but that model has required that you have a full-time job, own a property, a car and have 2.4 children. Going forward, that is not the way customers will necessarily live their lives and banks will use AI to create products in a far more personalized way.

Deep neural nets in machine learning are able to harness data, which a lot of the more traditional banks have had real problems utilizing. Traditionally they have built in silos, but in the future, all of this data will be brought together and institutions will have a single view of the customers and be able to make far more predictive solutions for those individual customers going forward.

Firms often jump straight to the most challenging uses of new technology and miss out on the easy-to-implement applications with high benefits. What do you think these low-hanging fruits are in AI?

The first area is just the way banks interact with their customers. AI is used in a chatbot functionality and you’ll very shortly see those chatbots become almost like ‘personal bankers in your pocket’. Customers will be able to use them to interact with financial institutions in ways they never did in the past, to carry out microtransactions or to understand or alter their spending habits.

That is probably one of the low-hanging fruits, that is where the initial benefit will come from the customer’s point of view. From the bank’s point of view, it will be better understanding customers by utilizing data in the background, getting much more of a single view of the customer and knowing the amount of products they have with the bank or other institutions.

Developing Cora, an AI-driven chatbot has been a key element of your digital strategy at RBS. How is this project coming along and what are the other key points in your AI strategy?

We’re in year one of a five-year program that is currently very much focused on a text-based chatbot serving customers through all of our digital channels. We’ll see that evolve in the next few years to include personalized data, so the chatbot will know who it is talking to and give clients much more personalized information. We expect to see growth in actual channel usage, for example, we expect speech to become a strong channel in the future, and we’re also looking at digital avatars and visual representations of an AI to interact with customers.

You previously worked at Amazon for about five years. What strategies do you think banks should learn from big tech firms regarding the use of AI?

First of all, it is the ability to run a lot faster. Financial institutions using the traditional banking method didn’t necessarily dip their toes into AI too quickly. It is also the better working practices and the ability to think big – that is sort of one my mantras I’ve certainly brought from Amazon. Go after something that is going to make a difference for your customers.

Fore more on how tech giants make great use of machine learning tools, what best practices other industries may offer for implementing AI and how to boost digital banking sales with AI, join our free webinar on AI & ML with RBS  on 27 June!

AI in Banking webinar