COVID-19 forces banks into digital rethink

The coronavirus is accelerating the trend to digitisation and the use of artificial intelligence in financial services, further adding to the urgency created by emerging banking products like buy now pay later providers (BNPL) and neobanks looking to eat into the market share of major banks. However experts suggest that with their vast stores of data, major banks can rapidly scale new products and become ‘incumbent disruptors’ faster and more easily than any new organisation could be formed.





IBM senior partner Kwafo Ofori-Boateng said the rise of the BNPL industry was a “phenomenal” example of fintechs addressing an unmet need, in this case younger shoppers wary of credit cards and more comfortable with paying regular interest-free instalments.





It has powered the astronomical share price rises of fintech players like Afterpay and Zip, who are growing market share in Australia and overseas.





Fintech players like Afterpay and Zip, are growing market share.

“I think consumers more generally have an aversion to long term debt now. And I think that’s extremely prevalent in the millennial generation,” Tommy Mermelshtayn, the chief strategy officer for BNPL provider Zip Co told The Australian’s Forward Slash podcast.





“They prefer really using their own money and doing that through a very simple and transparent product. So the customer sees zip at checkout. They click the button, they’ve created an account relatively easily, and then they complete their purchase and then they pay us back over time.”





Social media has allowed these emerging players to rapidly scale without the expensive above-the-line marketing spend.





 “The ability to reach customers has fundamentally changed over the last decade. Word of mouth (allows) small fintechs that don’t have a large advertising budget to differentiate on product and experience and very quickly amass large customer sets,” Mermelshtayn said.





The growth of fintechs, who are using technology to offer lower cost services with an enhanced user experience, comes at a time when the pandemic is dramatically impacting demand for bank services.





“Globally, banks are addressing the need for very rapid and aggressive cost reduction. Most consumers are not in spend mode at the moment,” Ofori-Boateng said. “So the funnel of new revenue is decreasing.  Therefore we are starting to see a real acceleration of digitization to optimise processes and the operating model.”





Ofori-Boateng said this is accelerating a radical rethink of bank IT systems and the adoption of technologies like cloud, AI, and blockchain, adopting platforms to help expand their differentiation. 





“We’re seeing banks going to an architecture that breaks apart the relatively monolithic technologies that they have into an agile, API-enabled architecture that allows them to compete effectively with the neobanks,” he said.





He said technology adoption played a key role in enhancing the customer experience, especially with innovative data usage.





In one case, a small European neobank shifted the traditional model to where the “entry point” for a new customer was more about opening a relationship with the bank, rather than a specific account.





After that user-friendly introduction, the bank serves as a “one stop shop” for numerous services, whether savings, loans or event trading products – not necessarily provided by the bank itself.





Customers increasingly expect their banks to be a one stop shop.

If banks are to avoid seeing their products commoditized into third-party portals, they need to exploit their scale in terms of data collection.





“The model has got to shift from an incumbent whose operating model is focused on having an ‘account with account features’ to a model that focuses on the need to ‘anticipate what my customer is going to need and enable the agility to quickly fulfill that need,’” Ofori-Boateng said. “This ability to rapidly anticipate and fulfill these customer needs is a fundamental use case of analytics.”





“Customers increasingly expect their banks to be a one stop shop that knows them and anticipates their needs and that makes it easy and seamless to engage and interact with. And, of course, then be free or as close to free as possible.”





Incumbents would need to simultaneously overhaul their internal operations at the same time to ensure they are prepared both for current economic conditions, and longer-term threats to margins.





For instance, amid sharply rising unemployment and economic stress, Ofori-Boateng said financial services providers have faced the urgent need to hone their risk models to understand the elevated risk of credit defaults – and provision accordingly.





“You can imagine the kind of portfolio analysis that needs to happen to figure out what non-performing loans are going to look like in this different and rapidly changing operating environment,” he said.





“As we move towards the new normal, we will start to see banks building in those capabilities as part of ‘business as usual’,” said Mr Ofori-Boateng.





“They will require much more robust operational resiliency.  This requires robust analytics behind, for instance non-performing loans, liquidity impact, financial crimes and fraud to be built in and automated.”





Technology to better understand portfolio risk is not exclusive to big banks, and BNPL players, as well as zero-cost broking houses like Robinhood, based in the US, have been developing deep data sets on enormous volumes of relatively small-scale transactions, enabling them to create robust credit models.





Advanced analytics helped providers avoid the “machete” approach to risk management, enabling product pricing to be better tailored to an individual customer’s profile.





“You can make razor-thin decisions, especially on risk pricing,” he said. “You can attract a larger section of the population because you have a much better view of the risks they carry. And you are able to anticipate what risk you are carrying in your portfolio at any given time because you are able to do that level of analytics and fine-tune your risk model and run them frequently.”





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