Three high-tech steps to transform your retail banking operation
The advent of mobile applications in the late 2000s wasn’t just a game changer: It created a whole new game. For everyone. For customers, apps carry out everyday tasks via a smartphone, tablets, wearables or the Internet of Things at their own convenience. On the other end of the cycle, developers who spot a customer pain point can whip up a code to address it, build an app and make millions of dollars on iOS and Android platforms. Could there possibly be a new season?
Yes—in fact, get ready for yet another game to top them all. Artificial intelligence will drive the next big wave of retail banking change—and it’s a different beast, to say the least. Stanford University’s AI Index found that the number of AI startups is currently 14 times larger than in 2000, while VC investment in AI is six times larger.
This two-decade AI boom points to that industry’s long-term value—though trying to capitalize on it without the necessary data is like trying to hit a home run without a bat. That noted, many big corporations have that information and understand how it can drive positive results in any vertical.
Louis Vuitton parent company LVMH, for example, wants to enter the hospitality space. To make that happen, the company will leverage retail customer data to craft its hotel experience. What can banks take away from LVMH’s approach? Because finances dictate so much of people’s decision making (including the hotels they check into) banks can harness their data to leap beyond core banking. As a result, they can power decision-support engines in other verticals.
Accelerated AI adoption
Of course, shifting verticals can make banks and startups wary about partnering. But each side stands to gain—big—and will benefit from access to the other. If banks can see AI startups as decision-support allies, and startups view banks as data suppliers with enriching and engaging AI tools, the two groups can partner without fear.
While retail banks gradually integrate AI tools, the industry at large isn’t entirely ready to accept such transitions. According to PwC, two thirds of U.S. financial institutions report limitations that govern their AI implementation—and concern is mounting that the technology will replace humans.
However, compelling efforts to ease that apprehension have emerged. A Forrester study revealed that 10 percent of companies plan to incorporate human expertise into their AI processes in 2019 to compensate for its limitations. Automation and robotics might easily replace workers in manufacturing. But AI should serve as more of a decision-support tool that enables humans to leverage data to reach more accurate conclusions.
Transcend risk, transform decision making
While innovative, AI isn’t always outfitted with what it needs to account for context. For instance, a neural network that optimizes decisions might eagerly give a credit card to a first-year college student. But—and it’s a crucial question—does it do so without understanding the additional risk involved? Enter Bayesian-based AI, which looks as the probability of an event based on prior knowledge of related conditions. This iteration is better equipped to consider the human element and existing information in retail banking.
AI is a resource, but unlikely to replace human context and experience in banking. Instead, financial institutions should follow these steps to augment human decision-making with AI technologies.
1. Align with young companies
A McKinsey study found that data scientists represent 235,000 of the 150 million workers in the U.S. Finding talent can be difficult, but banks that partner with fintech startups can jump-start an AI initiative and give themselves time to gradually build out internal capabilities.
To succeed and create a lasting presence in the tech space, banks must learn how to give startups the necessary tools. For example, a procurement process that takes six months will hamstring agility and defeat the purpose of bringing on smaller organizations. Working with these early-stage companies allows retail bankers to view AI as an additional resource—not a disruptive one.
2. Step outside the status quo.
In the early 2000s, the internet went from a content hub to a framework where everyone could participate. The financial services industry is likely to undergo a similar transition and banks will serve as a data hub surrounded by an ecology of entrepreneurs, innovators and researchers.
Encourage third-party participation that allows banks to create additional value without having to micromanage every aspect of a partner startup’s budget, trajectory and growth. Outside influence helps banks see the big-picture results that AI provides.
3. Tread lightly with customer data.
Banks have very sensitive consumer data. Exposing it to the outside world isn’t really an option. Still, it’s important to leverage data assets and allow fintech partners to train their AI tools.
One promising method involves encrypting data so that machine learning tools can still generate insights from existing patterns without exposing the bank and its customers to unnecessary risk. This might involve the use of data and social networks to build a revenue stream largely independent of service fees.
In banking, almost all digital executives tell the same story. A few years ago, IT was just a small component of their bank until they embarked on digital transformation. Now, IT is everything. Even banks with the best intentions must overcome and overhaul legacy technologies before they can adopt the theoretical use cases that originally inspired digital transformation.
As banks look to stay relevant, they must participate in a much larger ecosystem. By using AI to connect high-net-worth individuals with increasingly affluent, advice-seeking millennials, banks will attract the next generation of revenue-producing customers and provide access to an array of financial decision-making support tools.
And that’s a game everybody wins.
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