May
21, 2021
7 min read
Opinions expressed by Entrepreneur contributors are their own.
It’s no secret that artificial intelligence (AI) is already changing the way we deal with money, and it has the potential to revolutionize banking as we know it.
AI is already a common tool used for some up-and-coming fintechs, whether that’s a robo-advisor or an automated-savings tool. Traditional banks are also recognizing its potential to offer a more personalized service for customers amid branch closures — HSBC and Wells Fargo offer an AI experience that mimics a real-life assistant.
However, while there’s certainly an interest in and understanding of the technology‘s potential, the process so far has been slow, complex and difficult. Banks wishing to adopt AI to make a real change are faced with regulation, risk management and limitations within their own organizations.
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What is AI?
Even today, many people still think of AI as a robot with a mind of its own. But AI simply enables machines to make decisions based on data — something banks have in spades but don’t always take advantage of.
AI leads to higher automation, can help control risks and can improve the speed and accuracy of decision-making. According to McKinsey, AI could potentially unlock $1 trillion of incremental value for banks every year.
The adoption challenge
The ability of some of the largest banks in the world to adopt AI remains limited and has thus far been characterized by high failure rates and long times to market. Often, it can take so long to adopt a new solution that by the time it goes to market, customer expectation has shifted once more.
Implementing something new across a bank with thousands of people handling billions of dollars worth of cash is often likened to turning an oil tanker. Banks face limitations with everything from an inexperienced internal team, fear of change within the organization and, of course, the issue of trust. The adoption of any new solution depends on the comfort levels of stakeholders, especially customers, and with AI in banking involving sensitive customer data, privacy issues will remain a challenge until AI is better understood.
In 2021, we should be able to overcome some of these problems by implementing the right systems: investing in platforms and systems, building trust to take the fear out of AI and managing change by reassuring staff that humans and machines can work together.
Related: 8 Ways Digital Banking Will Evolve Over the Next 5 Years
The financial-services revolution
The financial-services landscape is changing fast, and AI is already having an impact — but how does it factor into our long and complicated journey?
As we know, the 2008 economic crash prompted a sea change in many aspects of financial services, and it was from that crisis that some of today’s biggest fintechs emerged, hoping to uncover a “better” way to bank.
Back then, it was fairly uncommon to manage money on a smartphone — but fast forward to 2021, and a reported 25 million people in the UK use mobile banking in some form, with 14 million (over one in four) having opened a digital-only bank account. By the end of this year, digital transactions across the world are expected to reach an annual value of $6685.1 trillion.
This is a somewhat natural progression in the current “fourth industrial revolution,” which involves data, digitalization, automation and AI in all their different forms. The potential for banking is not only to make internal operations more efficient, but also to enable customers to interact remotely — even opening a bank account safely in the comfort of their own home.
I believe we can trace the fourth industrial revolution back to the 1970s, with the foundation of commercial software like Microsoft, Oracle and SAP. These formed a launchpad enabling businesses to innovate at scale. By the 2000s and the dot-com era, data was becoming available to billions of people and has continued to be democratized.
In 2012, we saw deep neural networks — the enabler for modern AI — being introduced to the world on a large scale, most recognizably with things like facial recognition, which was the real beginning of the AI book. A few years later it was adopted by tech giants like Google, Facebook and Amazon with gusto.
Now, deep learning is the driving force behind many industries including search, social media and eCommerce, to name a few. We’re beginning to see this progress in financial services — but turning that oil tanker remains a challenge.
Reportedly, just over half of mid-sized to large FS organizations have adopted AI. However, it’s worth noting that banks are very good at keeping an eye on industry trends and noting successes and failures, so I get the sense there is a lot of innovation waiting in the wings.
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The future of financial services
Banking, financial services and insurance (BFSI) industries are the most regulated in the world, with legislation constantly moving to keep up with new innovations and protect customers. “Slow and steady” is banking’s foundation — frustrating for the innovators, but vital in maintaining trust.
We’re currently in the midst of another crisis, but this time people have turned to their banks for support, like mortgage freezing and small-business loans, which has given the sector the chance to rebuild the trust that was damaged a decade ago.
The digital transformations we’ve seen over the past 11 years have contributed to a “healthier” looking system, which is regaining the trust of consumers, but technologies like AI will be key to the sector’s continued prosperity.
We need AI in banking as much as we need software. The past few years have seen a huge demand for data-related talent in banks, with most big names building centers of excellence for AI and data science. Others are keenly developing incubators and accelerators for fintechs and tech startups to develop ideas the bank could use in the future.
The benefit of partnership
In order to speed up adoption, a robust ecosystem of partners is key. As banks finally come around to the “buy not build” mindset, vendors are partnering with financial institutions to provide AI developments and help them manage data better.
However, even with an internal-innovation program, issues of trust, compliance and speed-to-market remain. All too often, a potential partner develops an idea and sells it to the bank, only for the deal to be halted by compliance. These can take years to come to fruition — by which time, the world has often moved on, and the startup with the solution may have collapsed.
Caution is important, but banks need to consider the benefits of faster innovation through partnership. Even with the risks associated with AI, the competitive advantage it brings should not be understated.
Regulation will play a key role in banks adopting AI, and it’s important that the larger players in the industry work with the startups and regulators to ensure this happens realistically. We’ve seen over the past year that what usually takes years to change within an institution can actually evolve overnight when needed, so it’s my hope that this faster adoption will continue into 2021 as banks look at how many trustworthy, ready-made AI solutions there are to implement.