Sat. Nov 20th, 2021

More and more in banking, it’s technology that matters. The new challenger banks are actually known as “fintechs”, financial technology companies, rather than banks – and this is understandable. It’s widely believed in some circles that companies such as Starling, Monzo, Revolut et al have managed to achieve large numbers of signups principally because they were first to automate the process of opening a bank account. What used to involve a physical visit to a bank office with a sheaf of annoying documents, followed by a lengthy wait, is now a matter of a few minutes on one’s smartphone.

Such an automated process is, of course, simply a matter of handling data. The more advanced data processing becomes, the more often it tends to be called “Artificial Intelligence”, or AI.

For many bank customers, the most visible use of AI in banking is friendly chatbots, of the sort which have helped the challenger fintechs offer a decent user experience without any customer-facing staff. This can be an almost complete solution, with China’s WeBank reporting that 98% of all customer inquiries are dealt with by bots. But beyond the customer experience, AI can galvanise banks old and new, cutting costs and improving margins.

The key factor is establishing banking’s biggest asset in the fight against fintech: data, the sort which all established banks have a lot of and which many a startup would love to have. “Incumbents are digitally transforming. Their scale and resources give them a huge advantage over fintechs, allowing them to catch up fast,” Danyaal Rashid, analyst at GlobalData, tells Verdict.

Banks are already investing fast in artificial intelligence solutions, with banking scoring the highest of any sector for AI adoption according to a recent GlobalData report on AI in banking. Leading adopters of the tech include Ant Group, Goldman Sachs and Santander, with usual suspects Microsoft and Amazon found among the vendors. It can be argued that established banks are playing technological catch-up with fintechs, which from the get-go have been cloud-native and incorporated snazzy digital tools. The data the incumbent banks have built up on their large and long-established customer bases, however, could quickly close that gap if it is used as part of an AI strategy.

Once the tech gap is closed, the challenger fintechs may not be all that hard to beat. Some of their business models, after all, have long been something of a mystery to many commentators. Business journalists at the Telegraph have called the fintech banks “little more than struggling current account providers with jazzy apps”, and the Financial Times has noted the disastrous performance of many fintech banks during the pandemic, saying “the idea that the likes of Monzo and Revolut can do anything banks can do but better is crumbling”. Rashid agrees, saying that “whilst fintechs have had the initial technological advantage over incumbent banks, they must continue to innovate and expand their product offering beyond (simply offering) basic account features.”

Big banks, bigger data

There is better technology out there than apps. Ant Group and its MyBank brand provide a good example of the possibilities made available by AI to banks. The digital-only MyBank is similar to Tencent’s WeBank in that it’s arguably a halfway home of sorts between a “tech-driven bank or a licensed fintech,” as aptly put by some commentators.

By 2019 MyBank had pioneered the ‘3–1–0 model’, a business loan that takes less than three minutes to apply for, less than one second to approve, and requires zero human intervention. When applied correctly, AI can help to reduce the time taken to approve a loan and ensure that loans are more robust, by reducing the non-performing loan ratios.

MyBank is not the only example of automated loan decisions. Another is British Business Bank (BBB), provider of finance to businesses through its subsidiary The Start Up Loans Company. Wanting to accelerate loan approvals by further automating the assessment process, and greater operational control over its risk management workflows, BBB adopted Temenos AI. Using Temenos’ Explainable AI (XAI) platform, BBB developed a predictive model which analysed loan applications and assessed the likelihood of defaults.

BBB used historical data from its existing customers to train the predictive model and identify key factors that led to missed repayments. New banks without a past history can’t do this sort of thing: getting hold of a large labelled data set for training purposes is one of the great problems in implementing AI.

 

Read more here.

 

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