Canadian banking group Scotiabank has noted that its strategic investments in machine learning (ML) technologies are beginning to pay off during the Coronavirus crisis, allowing it to effectively serve clients as they try to cope with these uncertain and challenging times.
Analysts at the bank’s global risk management department have been using ML tools to develop a cashflow prediction software program, called Sofia or Strategic Operating Framework for Insights and Analytics.
Sofia makes use of historical commercial banking information, like customer deposits and various trends from previous years along with machine learning to predict what clients might expect in the coming weeks. This rolling average, which gets updated in real-time, provides the banking platform with a better understanding of which customers are more likely to be impacted by the economic downturn and how to effectively address their requirements.
This means that relationship managers are able to proactively approach and work with clients whose cashflow might be under pressure. They can offer assistance to these customers, like providing information about different customer help programs or options such as short-term lending.
John Phillips, Director and Head, Credit Solutions Group at Scotiabank, stated:
“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 ML tool was developed before the Coronavirus pandemic began. It’s meant to expedite the review process for managing commercial banking accounts. But the COVID crisis has given these ML tools a new purpose, helping to effectively assess and predict which customers will require assistance during these unprecedented times – which have seen record levels of volatility in financial markets. These tools have been launched throughout Canada for commercial and retail clients.
Daniel Moore, Chief Risk Officer, Scotiabank, remarked:
“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. 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.”