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Learning from the rest: How can banks keep up in a disruptive landscape?

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In many regards, the financial services sector acts as a shining example for others to follow. Adept in keeping customer service channels open 24/7, processing vast quantities of transactions securely and ensuring compliance with complex regulations – businesses across every industry strive to follow the banking model in one way or another, adopting similar processes and technologies to improve their services.

For all their praise, there is plenty that banks could learn from the successes and failures in other sectors. Digital transformation has typically been one area where financial services lag behind the rest, with McKinsey noting less than 30 per cent of transformation initiatives are successful. However, fintechs and challenger banks have brought data-driven services to the sector, meaning digital transformation must be front and centre for established banks if they are to keep up. So what can banks learn from other sectors?

Accelerating transformation with technology

In their struggles to get digital transformation initiatives over the line, banks typically take one of two approaches. The first is to create a separate internal organisation with a remit to develop new digital products and services, unencumbered by the bank’s existing legacy processes and technology. In some cases, banks have even acquired one of their former fintech rivals to take advantage of its digital skills and provide this internal innovation capability.

The second approach is to focus on incremental digitisation by enhancing existing processes with digital technologies. For example, a bank might seek to enhance contact centre operations by embedding intelligent decisioning capabilities that use artificial intelligence and machine learning to help operatives make more personalised offers to customers. This strategy has the advantage of building on the strengths of existing ways of working, instead of starting from scratch. But it may also be more difficult to implement and require significant investment from senior leaders to drive the required cultural change.

It’s difficult to say which of these approaches is best. And in practice, banks will probably require both, depending on the type of transformation they are trying to achieve. But one interesting insight, again from McKinsey, is that whichever approach they follow, organisations whose transformation initiatives are successful tend to deploy or try more technologies than those who fail, particularly in areas such as cloud, mobile, IoT and artificial intelligence. And this links strongly to the fail-fast mantra in introducing digitalisation.

Taking an experimental approach

There’s a connection here. Banks are buying fintechs to take advantage of their digital expertise, and fintechs have earned that expertise through their willingness to adopt and experiment with new technologies. But that experimental approach isn’t something that the fintechs invented on their own. It’s a lesson they learned from the big technology companies.

For example, while Facebook’s famous mantra of “move fast and break things” sounds like a frightening idea in the highly regulated world of the financial sector, it’s basically the same idea that Tesla calls “first principles engineering.” You take a new idea, try to implement it using whatever technologies seem most promising and expect your first attempts to fail. But because you expected some form of failure, you learn from the experience and do better on the next iteration.

Perhaps some of the new technologies you try end up in the final product, and perhaps they don’t. The point is, you make the cost of the experiment and the price of failure as low as possible so that you have space to explore the problem and come up with the right design for your business.

Lessons from big tech companies

Take Monzo, for example, which is one of the UK’s biggest success stories in the new wave of challenger banks. In its mission to build a banking system from the ground up, Monzo’s engineering team decided to build a loosely coupled microservices architecture, specifically because “large internet companies like Amazon, Netflix and Twitter have shown that single monolithic codebases do not scale to large numbers of users.”

In its willingness to learn from the tech giants, Monzo experimented with different technologies before settling on Kubernetes – the same technology that Google uses to manage containerised workloads at a massive scale. (Incidentally, at SAS, we’ve been through a similar journey in developing our own cloud analytics platform and came to a similar conclusion. We’re now running our new services on Kubernetes too.)

The same principle applies to the adoption of analytics tools for artificial intelligence and machine learning. Even more so than classical statistical modelling, AI inherently requires an experimental, iterative approach where you learn as much from your failures as you do from your successes. In many cases, the wisest path is to try a wide range of different approaches and technologies, including all the latest open source frameworks, to discover what works best. Once you have found the right approach, you can then industrialise it using a production-grade analytics platform such as SAS Viya, and even provide it to your clients as a service.

Believe in humans

We’ve established that banks can profit by following the example of the big tech companies when it comes to designing the technical architecture and processes around digital transformation. But technology isn’t everything. Successful digital transformation also has a strong human element.

To see why this is important, let’s look at a counterexample. Another fintech company that has enjoyed rapid growth is Robinhood Markets, whose mobile app has made it easy for a new generation of investors to start trading stocks, ETFs, options and cryptocurrencies. However, in early March 2020, the Robinhood app suffered a series of systemwide outages that prevented users from opening or closing their positions.

The cause of the problems was a technology failure. In a subsequent blog post, the company’s founders noted that their infrastructure couldn’t handle the combination of “highly volatile and historic market conditions; record volume; and record account sign-ups.” But the impact was human. When the app failed, there was no contact centre to act as a backup for booking trades.

The dangers of technology reliance

The result? Many of Robinhood's small investors were helpless as the markets turned against their positions, or unable to make trades to take advantage of opportunities they spotted during a week when the coronavirus pandemic sparked a mass selloff. While it’s not yet clear how Robinhood will weather the storm, it’s reasonable to expect that there will be compensation claims, potential lawsuits and, worst of all, a catastrophic loss of customer confidence in the business. As one customer quoted in The New York Times put it: “For me, the moment they get [back online]I am going to try to get out and switch out to someone else.”

Without a human element that can take over when technology fails, businesses expose themselves to significant risk. And even if the technology is completely bulletproof, it’s a bad idea for banks to use it to replace human contact entirely. When customers apply for a mortgage or a loan, they’re often going through a high-stress situation, such as moving house or expanding a small business. While the loan approval decision can and should be handled by sophisticated modelling techniques, the customer wants to hear more than just “computer says yes” (or “no”).

The best customer experience comes when the model is able to explain its decision to a customer service agent, who can then act as an intermediary to break the good or bad news to the customer. This is assistive AI in action.

The public sector as an example

This is an area in which the public sector has been a key driver. In healthcare for example, AI is helping to streamline processes and improve patient care, whether it’s with predictive diagnoses or disease prevention. Here, the technology plays an advisory role and is used in conjunction with human clinicians who always have the final say. Elsewhere, government agencies are using it to improve decision-making in call centres by providing agents with the most relevant insights to improve outcomes.

The same principles are equally applied in the private sector. For example, car insurers are using explainable AI to assess whether damaged vehicles must be written off, enabling service teams to give the most effective advice. Digital transformation will ultimately be mission critical in the return to normality, and established banks must make it a reality. Recognising the value AI can bring to the table is a good place to start, providing businesses with the necessary insights to make relevant and timely decisions.

Paul Jones, Head of Technology, SAS UK & Ireland