Scotiabank’s Sofia is a Retail Banking Pandemic Pivot

The pandemic has brought on an obvious shift to online banking as customers look for new ways to fulfill their everyday financial needs without visiting a physical branch. Almost every financial institution (FI), including giants such as the Bank of America and JPMorgan Chase, has witnessed incredible surges in digital banking, adding tens of millions of new online users during the last 12 months. 

As online engagement rises, the need for digital-first banking solutions has become table stakes, as according to Chase, 54% of consumers agreed that they use digital banking tools more due to the pandemic than they did last year. As far as trends go, the idea that consumers will continue to bank online through 2021 and beyond is as sure a thing as saying “e-commerce will remain popular.”

Scotiabank was no different than other major banks in its need for engaging digital tools as its 21 million customers across the Americas began to head online to meet their banking needs last year. In order to respond to these new dynamics, FIs rushed to deliver tools that not only championed customer experience but also remained secure and accessible enough for anyone between the ages of 16 and 100+ to use. Building a platform for digitally native users is one thing—building online tools for those who have never owned a computer is a completely different task.

Sometimes, the best way to meet digital banking demand does not require starting from the bottom, but instead, looking inward and applying lessons from other divisions of the bank. In Scotiabank’s case, it meant taking tools from the risk management and mid-market sectors and applying new digital banking norms to them.

The result is an intriguing look at how one of the largest banks in North America managed to pivot a digital tool and deliver value to retail customers at a time when everyone else was focused on starting from scratch. 

The power of cash flow prediction

At the start of 2021, Scotiabank unveiled a cash flow prediction tool nicknamed Sofia, which stands for Strategic Operating Framework for Insights and Analytics. The idea of Sofia is not one-of-a-kind, as many other banks and financial software companies have variations of tools that understand spending habits. What’s unique about Sofia is the power and utility its risk management lifecycle affords.

Originally built in late 2019 to speed up the review process for commercial bank accounts, Scotiabank’s analytics team quickly realized that due to the current financial climate, Sofia could also offer predictive insights for mid-market and retail banking clients. 

“Sofia was born in that environment of wanting to get to ultimately a segment of one and provide value on an individualized basis.”

Daniel Moore, chief risk officer, Scotiabank

“That pivot was really driven by the impact of the pandemic and the bank’s risk desires to properly segment our customers and better understand them,” explains Daniel Moore, chief risk officer for Scotiabank. 

This is where things get interesting. Normally, when a major bank rolls out a digital tool that has the ability to positively impact a retail customer’s finances, it’s the brainchild of the bank’s digital team. There is a roadmap with customer experience at the center, and a stated goal to deliver value to every day users. 

But the data gathered by Sofia made it possible to take the same observations that may benefit a mid-market business and apply those to a single user. That’s how someone like Moore—Scotiabank’s top risk management executive—ends up involved in retail banking initiatives. 

Moore explains that Sofia’s original purpose helped limit possible defaults for businesses, and was essentially “an efficiency play so that we didn’t have to constantly retype financial statements into our systems.” Scotiabank advisors would then use that data to initiate purposeful conversations with customers early on in their journey.

Sofia took commercial banking data such as deposits and transactions and combined it with AI to understand how a business would fare over the next four weeks. That forecast would then be updated in real-time as a rolling average, offering advisors the ability to triage problems as they arose and handle any upcoming challenges. Prior to this, the bank relied on historical reporting from customers, which often led to outdated and mistimed conversations. 

It was this focus on business-driven analytics that led to Moore’s involvement with Sofia, and ultimately its evolution to a jack-of-all-trades prediction tool. 

“Before we got going with significant momentum on the business side, we really scaled up in risk, because in risk you have an inherent customer-centric view of the world, instead of a product view of the world,” says Moore. “You also have access to so much data in risk management. So those two things serve you extremely well to build up a strong analytics toolkit in the retail world.”

Risk management’s retail influence

As Sofia worked its predictive analytics on the business and risk management side of things, it opened up lanes for Scotiabank to play both offense and defense with clients. Advisors could recommend solutions to mitigate a lack of cash flow, or even suggest new lines of credit to scale a successful business.

According to Moore, the biggest value proposition for Sofia is the time saved by automatically compiling all of the relevant data and putting it in front of an advisor at the right time. 

“Before we got going with momentum on the business side, we scaled up in risk, because in risk you have an inherent customer-centric view of the world.”

Daniel Moore

“It’s all about realizing that, when we reach out to you, we can’t give you a one-size-fits-all solution,” he explains. “But if we understand the data and the situation you’re in, and can tailor products and services to really help you get through these challenges—Sofia was born in that environment, of wanting to get to ultimately a segment of one and truly understand and provide value on an individualized basis.”

One of the unique things about Sofia is that it is currently both scaling up and down. While it was originally targeted for mid-market businesses, it is now expanding to large commercial operations as well as retail clients. 

“The big first thing was realizing that we needed to get this in the hands of the commercial bankers that dealt with the customer,” says Moore. “That’s where the biggest efficiency and effectiveness play really was.”

As Sofia continues to roll out to different levels of client, retail consumers can expect to eventually see its effects. Moore explains that Sofia will be able to understand data involving mortgage payments, credit card statements, recurring payments, and more, and generate a liquidity profile based on that information. From there, advisors can come to a client with specific retail-focused solutions that enable them to mitigate potential risks. 

Though not currently tied together, Moore says Sofia could one day work with other Scotiabank platforms such as Advice+ – the self-serve online advice hub that centers around pandemic planning and investing. Sofia remains a back-end tool that consumers will never truly interact with themselves, but when combined with consumer-facing platforms such as Advice+, it balances Scotiabank’s approach to customer-centricity. 

“I think it’s like building the body of the car versus building the engine and car—you want to do both,” says Moore. “You’ve got to build a good dashboard, a great set of seats, and a great exterior, but the engine has got to work. With tools like Sofia, they have a way of leaking their way across the enterprise. So they affect every way that you drive for the customer. And every time you enhance that, you change the rest of the vehicle.”

“With tools like Sofia, they have a way of leaking their way across the enterprise. They affect every way that you drive for the customer.”

Daniel Moore

As for Moore and the notion of how risk management and analytics will continue to impact every type of Scotiabank customer, Sofia is the shining example of how the bank should continue to evolve its offerings.

“I think where analytics can fail is when you have really powerful technology and ideas that end up in the lab and nowhere else,” he says. “So the more these things become part of the fabric of risk and the bank itself, the more you have leaders talking about how they deploy data in furtherance of their goals—whether it’s risk management or retail banking. These conversations should really be part of the lingua franca for any senior leader at a bank moving forward.”