AI Revolutionizes Banking: How AI Is Already Changing Your Financial World and What’s Next

AI is the hot topic in today’s world, changing how we do business in so many different industries. But perhaps the novelty and remarkable accuracy of AI bots like ChatGPT has obscured just how much AI is already a huge part of our lives, and already a huge part of many industries we already take for granted.

The business value of AI in banking is already upwards of $150 billion worldwide, and estimates suggest it will double by 2030.

Today we’re going to go over the precise ways that AI is already making your life easier, and then give you a sense of what’s to come, and the hurdles the banking industry faces as it incorporates more and more AI into our lives in order to make banking safer, more efficient, and more secure.

Top 5 Ways AI is Already Used in Banking

You’ve probably had a call from the bank at some point in your life asking you to confirm a purchase. Maybe you actually did make the purchase and had to resignedly tell the person (or machine) on the other line not to cancel your card. Or maybe it truly was fraud. Well, either way, the way that purchase was detected was through AI combing through millions and millions of transactions per second and determining your personal purchase profile, noting that something was unusual in the purchase it flagged. AI can detect patterns in your purchases and run models to suggest the probability of fraud for any purchase. This means that even in real time, a bank can nullify a purchase made using your account if it appears to have a particularly high probability of fraud.

AI is quickly becoming better and better at imitating and replicating the kind of language humans would use, and that other humans can understand easily. This means that chatbots and support can complement what they offer in person with more and more digital services. The benefits of digital customer service that’s almost as good as in-person service are obvious. You can ask any questions from the comfort of your own home or own phone, and you can do it 24/7. Whether you’re halfway around the globe or simply don’t feel like getting out of your pajamas, customer support is steadily improving its ability to answer any issues you have. On top of that, the customer profiles that AI can build can help banks to identify which services are most appropriate for which customers right off the bat, improving the onboarding process immensely.

The fact that AI can analyze millions of data points in seconds is always going to speed up any process. When it comes to credit qualifications, AI can instantly determine whether customers qualify for loans, as well as use an algorithm to determine maximum borrowing thresholds for each individual customer based on their profile and histories. This lowers the risk of mistakes made as well as speeds up a process so that churn and frustration with a slow-moving bureaucratic process are impossible. AI is also capable of keeping up with market trends in real time, so it will never make decisions based on out of date information – a requisite in a field where information from one week ago is often woefully out of date.

Again, the ability of AI to collate millions of data points and effectively analyze them all has changed the way trading is performed. You’ve probably heard of “high-frequency trading,” which is essentially the way banks can make money by making trades every micro-second to take advantage of the fact that many traders don’t have access to sophisticated AI. It means that algorithms can set risk assessment levels and essentially ensure that their trades most effectively capture the swings of the market. On top of that, “robo-advisers” can manage an investors portfolio on a personal level based on the user’s individual levels of comfort with risk and goals. Investment strategies can be changed instantly based on whether users prefer high risk/high reward scenarios or are focused on stability and long-term growth.

Every bank has strict AML and KYC compliance regulations. AI allows banks to sift through attempts by customers to hide ownership and focus on Ultimate Beneficial Ownerships (UBOs), again both through predictive algorithms that have access to vast amounts of data and by being unable to be tricked into human error. As with loan decisions, AI allows banks to analyze all the data in a profile immediately to make a decision, as well as monitor huge numbers of transactions and place them into one broader financial picture. This allows banks to have a more specific understanding of each of their customers on an individual level and to prevent them from being open to risky or illegal activity.

The Future of AI in Banking

Whether you were aware of it or not, you’re already using AI whenever you bank. It’s already an essential technology to the way we go through the world and the way transactions are performed. However, it figures to become an even larger part of banking in the future. Here are five ways that AI might completely revolutionize banking in the future more than it already has.

The primary benefit of blockchain technology, both for the present and the future, is that it gets rid of the need for third parties due to its peer-to-peer secure format with a full, decentralized record of all transactions and a guarantee of trust and security. The banking industry in particular is an industry that relies heavily on third parties for security and trust, as well as basic bureaucratic tasks. Blockchain technology can therefore cut costs for any time third parties are used. For instance, the Society for Worldwide Interbank Financial Telecommunications (SWIFT) is the main vehicle through which global transactions are performed between banks. When lenders are connected directly to each other through blockchain, transactions can be cheaper and more efficient. On top of that, SWIFT has been hacked multiple times in recent years, so blockchain technology would be more secure as well, on top of the added benefits of automated services reducing the risk of human induced error.

One final potential application of blockchain technology in banking is the introduction of digital currency, turning hard currency into digital currency. China is already experimenting with this, and the idea that bitcoin would replace physical currency rather than complement it is potentially a flawed understanding of the potential of digital currency.

Classical economics based on the theory that humans will always act rationally has been proven to be irrevocably false, and that irrational emotions play a larger role than we’d ever believed. Emotion recognition software as the potential both to help banks build loyalty, as well as to help in fraud detection. Often-times, what customers say is not how they actually feel. Not only that, psychological research shows that often what we think we feel is not what we actually feel. Emotion detection technology can dig below what customers think and see how they feel both to establish a real emotional connection with them, and also to analyze emotions they’d prefer to be hidden, such as “fear of getting caught.”

Of course, emotion recognition software used in conjunction with traditional predictive AI is how to optimize both how connect with customers and detect fraud. The technology doesn’t yet exist in anything close to an error-free state at the moment, but even at its best it will only help banks to make decisions rather than be the sole factor.

Passwords and PINs are known to be security risks. Facial recognition, voice recognition, and fingerprint scanning are naturally significantly more secure, and require advanced AI to be used quickly and securely. This also affects KYC and AML, wherein you can authenticate who someone is instantaneously, without the need to sort through massive documentation. In theory, biometric authentication could mean the end of credit cards or payment entirely. You could walk into a store, pick up what you want, and walk out – all with AI automatically identifying you and withdrawing the funds necessary from your account.

Customers in the United States have become more and more comfortable with voice assistants helping them facilitate their lives, with upwards of 40% of the country already using them on a regular basis:

As with all biometric authentication, voice recognition software is more secure than passwords or PINs, and allows customers to perform transactions and actions they want without having to find the transaction on a website or to head to the bank in person to explain it to a physical teller. When combined with emotion recognition software, voice recognition software can detect when a customer is angry, or frustrated, or anxious, and give them a different set of answers and actions based on their emotional state, improving the overall customer experience.

Quantum computing is essentially just the ability of computers to accurately handle even larger and more complex amounts of data, equally efficiently and quickly as it handles smaller loads. Banking is an industry where speed is of the essence, whether it comes to high-frequency trading, or real-time fraud detection, risk analysis. Processing massive and often contradictory sets of information has exceedingly high stakes in the banking industry, and the ability of quantum computers to process that information at a higher level than either modern computers or humans are capable of has obviously benefits for a bank’s bottom line and for the safety of its customers.

The Hurdles Yet to Overcome with using AI in Banking

While we’ve painted a fairly rosy picture so far with both how AI is already being used and how it might be used in the future, there are also incredibly significant hurdles to overcome. AI technology is very new, and while we don’t know all of the potential problems it might end up creating, there has already been a significant amount of research into its pitfalls. None of these are things we can’t overcome or mitigate, but it’s something that banks and programmers alike need to be aware of moving forward.

This is clearly the biggest issue with AI, or at least potential issue. Because humans have historically been biased against particular groups and minorities, this historical data gets reinforced even by machines that “learn” that discriminatory practices are the optimized way to do business. For example, a 2019 study by the National Bureau of Economic Research found that a commonly used credit scoring algorithm was twice as likely to deny loans to black applicants than white applicants with similar financial profiles. There are ways to reduce the risk of bias in algorithms, but it is a huge problem for any AI that relies on historical data, where the data itself is already biased and compromised.

We’ve already explained how AI goes through massive amounts of data, and that beyond that quantum computing promises to handle even more complex and larger data still. Explaining to a layperson why they’ve been refused for a loan or why there are limits on the actions they can take can be incredibly difficult when often-times the humans monitoring the algorithm themselves don’t fully understand it. Furthermore, in the rare cases that errors do occur with an AI-based decision, it can be difficult to determine where the error originated from if the method whereby an algorithm made a decision is already too opaque for human minds to comprehend.

We’ve established a number of ways that technology makes banking more secure than with humans in control, but naturally the downside risk of cyber attacks increases when everything is reliant on AI systems. AI systems by definition mean that vast amounts of data are located in one place, which is maximally attractive to hackers who want access to all of it, including customer data and banking information. Again, strong cybersecurity protocols should be sufficient to prevent these kinds of attacks, but security needs to be a focus and be constantly updated to ensure that attacks are impossible.

New technology always leads to redundancy, as AI replaces jobs previously performed by humans. This can affect both bank employees on an individual level and the economy more broadly. But new technology tends to mean that new jobs open up, so while there is a destabilizing feature of technology as various jobs become obsolete and others open up, an increased efficiency and the ability to improve the economy overall will simply mean that people have greater opportunities to establish fulfilling lives in other ways.

People are wary of too much technology. Intuitively, people trust people more than a machine, and do not necessarily trust a machine telling them what they can and can’t do. History tells us that this is more an issue of “people getting used to it” rather than something that can’t be overcome, but it still means that it can be hard to onboard customers to banks that utilize many of the benefits of AI.

Key Takeaways

AI in banking is clearly here to stay. We already know the various ways it makes our lives safer, even at a time where having our passwords, monetary information, and even identities stolen digitally is ever-increasing. The ability of AI to help catch fraud, and to protect our funds is something we’re all acutely aware of. But the future is sometimes even more exciting that we realize, and the future of technology like blockchain or emotion-recognition software promises to revolutionize banking all over again. Still, even with the future so bright, we need to know that there are a number of hurdles yet to be overcome by AI technology, including the huge issues of bias and error which has yet to be completely solved. The banking industry tends to be one of the more conservative due to the importance of the data it protects, so even as it moves forward, it moves forward with the caution required to protect its customers and their precious data. We’ll see what the world has in store for us, but it’s quickly becoming a truism that anything humans can do, a machine can do better.