The banking sector is one of the most innovative and forward-thinking industries around. It was one of the early adopters of cloud computing and artificial intelligence tools, but it is still ripe for further digital disruption.
There is also quite a visible change in the way AI is used in banking, with the classic, data-driven AI evolving to more advanced use cases with generative AI. As with most industries, AI in banking started with automation and data analytics, but it quickly expanded to sophisticated applications in risk management, fraud prevention, and bespoke customer service.
With generative AI, the banking sector is set for further transformation, including changes to the way we save, spend, and generate money.
Challenging tradition with AI
One major area of change in banking is challenger banks – more commonly referred to as ‘Neobanks’ in the US. These are fintech firms that offer online banking via apps or software, with famous mobile banking apps, like Monzo and Starling.
The ‘challenger’ label is quite apt as these types of banks are seen as disruptors, changing the way we think about banking. They tend to be more agile and faster than traditional banks, though most of them are in some way partnered with the traditional banks, many for insurance reasons.
However, these fintech firms are using modern cloud-based technologies and data analytics to entice customers with innovative financial products and quick response services. This is largely a result of being cloud-native, with services built using software and big data tools, like machine learning and advanced analytics. The effect of this for the customer is a much more convenient, seamless experience.
For long-established banks, being subject to stricter regulation than most sectors and reliance on infrastructure that’s now antiquated both present blockers to modernization. This has left an opening for the challenger banks to lead the way on digital transformation in the sector.
By harnessing AI, banks and challengers are starting to create a digital environment that appears uniquely tailored to each of their respective users, which in turn fosters a sense of familiarity and ease that lifts the overall banking experience. This paints a rather exciting picture of the future of banking. With the ground-breaking power of AI and the innovative minds within the sector, we may be on the verge of ever more radical change in the way we use our money.
Generative AI
It’s the nascent nature of generative AI that’s leading financial services companies to rethink their operating models to address the technology’s rapidly evolving capabilities, according to McKinsey.
The industry sees generative AI as a “game changer”, with that sentiment backed by a 2023 banking report from the consultancy, which predicts it will lift productivity by 3 to 5% and enable a drop in operating costs. It also estimates this drop to be between $200 billion and $300 billion.
Key business functions are transformed as AI collects and analyzes huge amounts of data and enables a deeper understanding of customers and their personalized services. AI also enables banks to secure and protect customer accounts, increase returns on investments, and personalize content.
The growing capabilities of AI and the ever-increasing amount of data available mean that financial firms need to implement AI strategies or risk being left behind by their competitors and challenger banks. Scaling AI across a financial organization will face challenges, such as data silos, industry regulations, and data protection. Outdated banking infrastructures also lack the accelerated computing platforms required to train, deploy, and manage AI models, so improving existing applications and enabling new use cases is key.
According to a recent McKinsey forum on gen AI, more than 90% of the banking organizations represented reported having set up a centralized gen AI function as a way to effectively allocate resources and manage operational risk.
The consultancy’s survey also shows that about 20% of the financial institutions studied use a highly centralized operating model archetype. However, these are mainly large institutions whose business units can muster sufficient resources for an autonomous generative AI approach.
Chatbots and fraud prevention
There are many good examples of how AI is being used currently in the banking sector to enhance customer interactions, such as Barclay's fraud detection service.
This system monitors payment transactions to identify and prevent potential fraudulent activities with a proactive approach that not only protects customers, but also builds confidence in the bank's security measures.
Another great example is the Bank of America’s AI-powered research analysis platform Glass. Glass combines market data and bank models, and uses machine learning techniques to identify industry trends to then predict client demands. The results offer individualized investment advice and also positions the bank as a pioneer in using AI for strategic financial insights.
AI-powered chatbots are stalwarts at most banking firms, providing instant responses to customers and round-the-clock assistance (also part of the reason you’re less likely to deal with a human).
One example of what a successful chatbot looks like also comes from Bank of America in the form of its AI chatbot, Erica. Launched in 2018, it provides 24-hour customer support, efficiently handling queries and transactions, leading to reduced waiting times and improved customer satisfaction and in July 2023, it surpassed 1.5 billion interactions.
Advancements in computer chips and the cloud have driven not only new AI capabilities, but also innovation in the financial sector. However, data and language models are also fundamentally improving the services we use for our money. Data is crucial in the age of AI and investing in both cloud and AI is seen as essential for the future success of financial institutions.
