For banks, the capabilities of artificial intelligence (AI) have always been evident. The only question is when and how to implement them in a way that reaps the best returns.

Banks, for instance, have always had large volumes of data, over which it performs monitoring, analysis and insights-generating functions. Yet AI now presents banks with a future worth considering: what will it look like for banks to integrate advanced machine learning capabilities to help with all those processes?

Banking on Artificial Intelligence (AI)

AI, with its wide spectrum of technologies could either remain a passing trend today or transform banking for ever. The key difference is the whether there is a strategy in place. At present, some banks have experimented with AI across the board, while others may have chosen to focus on specific areas.

Banks’ AI budgets have varied from anything below US$3 mil to US$15 mil. Meanwhile the fact that any likely success of any one strategy may only become evident in the long runs makes assessment difficult for now.

On the one end, banks don’t have much to lose from implementing AI. The long-term benefits are there: cost savings and higher revenue. Some say that they will keep their expectations within existing AI capabilities, but there is also a risk that some will over-invest in some areas while neglecting those with potentially higher gains.

The possibilities are being explored by banks in different ways, and in the case of UBS Group AG is being tried and tested through its innovation labs in Zurich, London and Singapore.

While these localised hubs are positioned to keep an eye out for local fintech trends, they also position banks to collaborate with local fintech ecosystems on a ground level whenever the opportunity arises.

As an incubator for co-ideation, the UBS Wealth Innovation Lab was first piloted in 2014, and seeks to bring different industries to brainstorm common solutions together. This in turn is key to developing cross-industry solutions which ultimately help expand its business scope.

Transforming wealth advisory models

Regardless of how this strategy is conceived and implemented, investing in AI is not just to revamp current processes, but to create added value for customers.

Three identifiable advisory models of wealth management are human advisory model, the purely automated advisory model, and the hybrid advisory model.

While it has artificial intelligence-based investing capabilities, the hybrid advisory model incorporates the human element that about 51% of 1,300 survey respondents interviewed by Accenture saw as the most reliable option for new investment ideas. Yet at the same time, many also cited the availability of computer-generated recommendations as the difference-maker.

In fact the idea that artificial intelligence is a construct completely independent from human intervention is at best, false. As Bethan Turner of Honeycomb points out, it is still human beings who create, then decide on how to clean the data collected. They are also the ones who decide how that data is to be used.

“AI is about the quality of the data that you have, but in my opinion, we are never going to be able to leave those decisions to an algorithm – we will still need a human being to be part of that,” UBS Investment Bank COO Bea Martin has noted before.

Hybrid model wealth management in practice

In terms of wealth management, the hybrid model can offer customers the rewards of a long-term relationship. The long-term view needs to be considered since clients may not need financial advice 24/7 but may instead may want the security of knowing where to seek advice as they grow their wealth.

“I think that’s one of our biggest challenges now. We’re not spending enough time building those relationships. This platform will help us do that and capture dollars moving forward,” commented UBS advisor Jennifer Susco, upon the launch of the UBS Advice Advantage hybrid advisory model in the US earlier this year.

Hybrid advisory could also mean advisory solutions that transcend the physical borders of a banking establishment, and the availability of 24/7 access to banking advice, regardless of where in the world customers happen to be. This is a possibility with the use of voice-activated virtual assistants, for example. Amazon’s Alexa, who was pilot tested in 2017 to answer simply financial queries on demand, is simply one instance of financial advice infiltrating our private spaces in the future.

Even as current advisory roles evolve to accommodate digital advisory, banks will also need position their workforce accordingly. While UBS’ WM Innovation Maker Box aims to encourage current employees to pioneer innovations of their own, an AI-integrated future will also require a host of other skillsets, including advanced IT and programming skills, as well as basic digital skills, to name a few.

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