Mobile banking case study: RBC – farewell friction

Royal Bank of Canada (RBC) is moving with the times with a series of useful innovations and launches.

RBC has been proactive with its digitisation strategy. Instead of aiming to offer all things to all people it has instead chosen to focus on best use of technology in areas where there is a real friction point and a problem to be solved. By doing this it has added value to its customer base and seen impressive take up figures of its various initiatives.

Its most recent play was a mass scale artificial intelligence (AI) digital service, NOMI. This offers its customers insights about their financial habits and the ability to automate their savings. Its mobile offering meanwhile, has seen new iterations based on building its P2P capability and it is also in the throes of introducing a loyalty scheme and other partnerships to bolster its offering.

Rami Thabet, VP of mobile at RBC, comments: “The mindset here at RBC is that we want to have a digitally-enabled relationship with our customers. We want this relationship to be meaningful and to offer a blend of channels that makes for a much better user experience than when customers use single channels. But instead of looking at cutting-edge technology that is at the early stage we look to innovate in areas where we can tackle the everyday issues that our customers have. In this way we offer something that actually adds value to our customers.”

Making the everyday better was behind the recently introduced NOMI proposition. This is made up of two parts; NOMI Insights provides personalised, timely, and relevant information from a client’s account to give them visibility and empower them to manage their day-to-day finances through the RBC Mobile app. NOMI Find & Save meanwhile, helps make saving simpler for clients by using predictive technology to find amounts of money clients can spare, and automatically saving that money for them.

Thabet comments: “We are always about the everyday case use. NOMI is all about the helping people to manage their finances as they go about their business. This is something that many people struggle with.”

NOMI was launched in 2016 and went live in autumn 2017. Since launch RBC says that client engagement with its mobile banking app has increased by 20%. It also says that average time-in-app increased 6% and that more than 100 million insights were read by clients in the first five months alone. With the Find & Save function the bank has found that those using it save twice as much as those that do not.


The Insights element is available to all mobile users. It works by giving a baseline idea of how and where they are spending their money. This gives customers insight into their spending patterns and it also works to establish a level of trust in the bank.

Insights uses Engage, a pre-built AI application from Personetics. The app comes with a library of pre-built insights to include banking-specific triggers and workflows, with new insights added on an ongoing basis. Thus once the Insight has been applied as an overlay to a customer account it can learn over time about individual spending patterns, apply user feedback and essentially give the account holder actionable insights into their activity and account.

Thabet says: “Insights is about highlighting spending patterns that a customer might not notice. For example, if an insurance policy rolls over automatically but there is a price increase then flagging that is obviously useful. This sort of accidental expenditure adds up over time and many people simply aren’t aware of it. So the aim is to give our customers financial insight and the means to access a meaningful overview on their expenditure. Previously clients would need to operate an Excel spreadsheet alongside their mobile app to marry the two but now this function sits within the app and creates a call to action.”

Find & Save

NOMI Find & Save also uses Personetics technology – ACT, an AI-powered, automated money management programme. This is a personalised, self-adjusting automated savings account co-designed and created with RBC to help each customer save money but without over saving.

Like Insights, Find & Save is integrated into RBC’s mobile banking app and is available to all digital banking users with no configuration or setup required. However activation is a requirement – customers do need to opt in.

Find & Save uses predictive analytics to find pockets of money in a client’s cash flow to automatically move into savings. The idea is that customers can have their spending analysed and automatically save money. The technology has stops built in so that it never sets aside more savings than a client can afford, and clients can receive a push notification alerting them every time money is saved to help balance savings and day to day banking.

“Once customers opt into this process an algorithm establishes a baseline and then adapts the savings level up and down – depending on activity. It is real time and predictive and can work well after only a few days monitoring an account,” says Thabet.

Thabet says that the concept of savings came from the fact that they are in decline and in fact most people don’t think they have enough cash flow leverage to be able to save for a special holiday or rainy day.

“Our testing showed that was not the case,” he says. “With our find and save capability we’ve provided a value proposition that gives customers visibility over their everyday interactions and then the opportunity to automatically set aside where possible. This solved two friction points – the data that showed spending points and patterns and automatically making the saving,” he says.

Thabet describes the road to going live. “One of the prerequisites to getting this up and running was doing robust client profiling to give us a map of what cashflows were like and how algorithms could affect that. Once we had found the right algorithm we then applied it in a real-time testing environment so as to eliminate latency and provide the most up to date fit. The AI then was applied and tweaked so that it could learn from customers’ cash flows; the more an account is used the more active the user is the more quickly it can refine and hone itself. In this way we can offer personalised and predictive savings capability.”

But applying AI to improve customer engagement is not a straightforward process. Language and context are both really important and it takes process of continual learning and improvement to do well, particularly when its use could have a negative impact on a customer’s financial affairs and thus directly impact the customer experience.

Thabet says that there were lessons to ingest along the way: “The learning came in two areas; firstly we learned that clean and accurate data, its process and governance are central to feeding the AI. This was a technical learning curve. Secondly we needed to hone our mind set and make sure that although the AI was a compelling story to technical people, to the majority of people the compelling story is automated and intelligent savings capability. It is tempting to boil the ocean but the reality is that we need to keep things simple and solve a problem that exists for our clients,” he says…

By Alison Ebbage, editorial contributor to FinTech Futures