At the beginning of the digital revolution, Bill Gates said that banking is indeed necessary, however, banks are not. This was back in 1994. After 30 years of digital change that’s disrupting dozens of sectors, it is still not clear whether he was right.
The biggest changes within banking to date have focused much more on the how element than the what. Customers go ahead and interact with banks way more frequently now than in the pre-digital days; however, those interactions mostly go on to consist of checking balances as well as moving money between the accounts. It has now been 25 years since the beginning of the dot-com boom, and not one digital-only bank has truly hit the scale as the traditional banks go on to dominate.
This has gone on to create, in certain circles, a hunger for the next big thing, which is truly going to disrupt banking.
Generative AI may as well be the most disruptive tech the banking industry has gone on to see in 30 years, and the fact is that it will radically change the way banking gets delivered, but one cannot believe it will fundamentally shift the basics of banking, such as collecting and safeguarding deposits as well as lending money.
It may, however, change everything else.
The revenue option for banks
Most banks go on to think generative AI happens to be a cost-takeout play, however, the revenue upside can also be the real story here. Almost every bank role will get transformed in some way, and the effect on bottom lines can indeed be eye-popping. One of the new Accenture studies modeled the effect on the banking sector and found that generative AI can go on to provide a significant boost.
And there happen to be hundreds of potential use cases as well as applications. It is worth noting that in the case of risk management, generative AI happens to be already transforming anti-money laundering as well as the know your customer practices. Its coding applications happen to be some of the most exciting ones and range from the capacity to reverse engineer decades of spaghetti COBOL code to developing new digital customer experiences, all at unprecedented speeds. Generative AI’s uses within customer service happen to be just as exciting as well as far-reaching. The tech happens to have the potential to raise the bar in terms of customization while at the same time helping reps solve customer inquiries at a faster pace.
But, the real power is going to be in augmenting workers. For instance, imagine a relationship manager who, apparently, during a client conversation, can look to a navigation map-like guide. If the conversation happens to be red with the customer displaying signs of disinterest, the system could very well provide them with a detour as well as prevent them from heading down dead ends as well as the detours in the conversation.
It is instructive to compare the urgent impact of generative AI with certain other recent technologies. When blockchain along with metaverse emerged, there was indeed a stretched discussion on how the technologies could go on to change banking. Due to Gen AI, one has already gone on to see banks come up with thousands of choices and opportunities. The challenge is not what one does, it is all about what one decides not to do, which goes on to reinforce the belief that it is indeed a true paradigm shift.
It is just like going from the slide rule to the calculator. As a matter of fact, a majority of banking leaders- 71%, to be precise point to generative AI as one of the major levers in their continuous reinvention strategy, as per a recent Accenture study, and two-thirds- 66% see the tech as more of an opportunity than a threat.
2023 has gone on to see a staggering adoption as well as advancements in maturity, with banks going beyond the proof of concepts to use cases. Shouldn’t come as a surprise that banks that have already invested in the AI structure as well as analytics previously, and which have certain robust digital cores, are going to be leading the way as far as generative AI is concerned.
The fact is that perhaps the most disruptive element when it comes to generative AI is that one does not need to be a goliath or may have 100 PhDs within AI or machine learning so as to take advantage of it.
Thanks to cloud-based services, most of this technology can be purchased on a credit card. It just needs the right timing as well as focus.
It is well to be noted that the next step when it comes to generative AI’s evolution within the banks will be establishing the right infrastructure within the bank’s four walls. This goes on to include building appropriate guardrails within security and risk as well as ensuring that AI models, systems, and processes happen to be responsible by design. Model risk management is going to be critical since large language models proliferate and banks go ahead and also customize existing models or, in certain scenarios build their own.
Reinvent talent as well as ways of working
Due to the fact that generative AI will go ahead and impact almost every part of the bank, it will also need the banks to reinvent almost all processes and roles.
The point over here is that one really cannot go out as well as hire someone with five years of generative AI experience. This doesn’t exist. Hence, one needs a culture that goes on to embrace a willingness to shift so as to take advantage of and, at the same time, scale the technology. If the culture happens to be one with a rear-view mirror approach, it is going to be a kind of benchmarking against what competitors happened to be doing years ago and never taking that step forward.
The very nature of talent as well as bank roles could as well change dramatically. Banks will need to consider how their talent moves across in different ways and also embrace skills-based HR.
Although, generative AI is not likely to disrupt banking, there is a good reason to believe it is going to be a driving force when it comes to continuous reinvention, thereby making banking infinitely better and more meaningful for employees, customers, and investors alike. For staff bogged down due to the process-oriented work, generative AI can go on to handle the mundane, thereby freeing up more time so as to help customers. When it comes to the end customer, it goes on to mean a faster, more personalized, as well as seamless experience from the bank.
