Analytics boffins at the Canadian bank's global risk management unit have used machine learning to develop a cashflow prediction tool called Sofia (Strategic Operating Framework for Insights and Analytics).

Sofia uses historical commercial banking data, such as deposits, and trends from the past year combined with machine learning to forecast what clients could expect in the next four weeks.

This rolling average, which is updated in real time, gives the bank a better sense of which clients are more likely to be hit by the economic downturn and how to best respond to them.

This means that relationship managers can proactively approach those whose cashflow may be under pressure and offer help, such as providing information about customer assistance programmes or options for short-term lending.

John Phillips, director and head, credit solutions group, Scotiabank, says: “It’s intended to provide us with insights into accounts which may be trending down so that we can get in front of it and have discussions with our customers that are informed by the data.”

The tool was built before Covid-19 hit to speed up the review process for commercial banking accounts.

However, the pandemic has given the technology a new purpose, helping to assess and predict client needs during massive volatility. It is now rolled out nationally for commercial and retail customers.

Daniel Moore, chief risk officer, Scotiabank, says: "Developing these kinds of tools and analytics had already been on our roadmap, but what has been supercharged by the pandemic is the demand side for those analytics."

Continues Moore: "Either as individual or business owner, if your bank comes to you and says your account balance is showing stretched liquidity, we’d like to sit down with you and discuss how we can help you out, that’s a highly different conversation than six months later when the client is having difficulties.

"An early conversation is good for the Bank and good for the customer. That’s how we should be using data."