AI is rapidly rewriting the rules of finance
Artificial intelligence has quietly become the organizing principle of modern banking – more akin to a new operating system than a bolt-on technology. Yet the way banks use AI will have to change dramatically if they’re to keep customers loyal.
Today, banks deploy AI most visibly in customer service. Chatbots and virtual assistants triage routine queries, adjust card limits, reset passwords, and even help restructure debt – often with human-like fluency. Behind the scenes, these systems use natural language models and real-time account data to deliver responses around the clock, at far lower cost than call centers. In many banks, these tools are no longer experiments, but scaled platforms that are embedded across mobile apps, branches and contact centers.
Risk and fraud are where AI earns its keep. Machine learning models ingest millions of transactions and behavioral signals to spot anomalies – a card used in two distant cities within minutes, or a business suddenly changing its payment patterns. These systems continuously learn from confirmed fraud cases, improving their hit rate and reducing false alarms.
Efficiency provides a quieter but equally important rationale. Banks use robotic process automation to handle back-office work, such as onboarding, identity checks and regulatory reporting. Combined with AI, this can turn multi-day workflows into near-real-time processes, cutting operating costs and freeing staff for more complex tasks. With a ‘ZeroOps’ mindset, manual intervention in systems becomes the exception rather than the norm. As margins are squeezed by competition and higher capital requirements, this type of automation is less a choice than a necessity.
But what of the future? The next phase will be less about sprinkling AI across existing processes and more about redesigning the bank around the technology. The AI-first bank will be designed to personalize offers, manage risk and orchestrate interactions across every channel in near real time.
Hyper-personalization is likely to become the main competitive battleground. Rather than segmenting customers by crude demographics, banks are beginning to analyze context – location, time of day, recent spending, even inferred emotional state – to tailor products and advice. A customer browsing flights might be offered travel insurance; a pattern of missed payments could trigger early, empathetic intervention rather than automated penalties. Done well, this promises to turn commoditized financial products into bespoke services. Done badly, it risks feeling intrusive or exploitative.
The possibilities are vast – yet in truth, the future of AI in banking will be defined as much by constraints as by possibility. Regulators are sharpening their focus on explainability, bias and model risk, wary of opaque systems making consequential decisions – be it about access to credit or about financial crime. Decision traceability – being able to show why a model produced a particular outcome – is emerging as a central requirement, forcing banks to build governance frameworks and AI ethics committees into their architectures. Not to do so risks both fines and costly reputational damage.
There is also a competitive shock looming from outside the industry. As agentic AI evolves on the consumer side, shopping agents could automatically move deposits to higher-yield accounts, or refinance mortgages without the customer ever speaking to a banker – eroding the inertia on which many banks rely and compressing profitability significantly. McKinsey has long estimated that AI could unlock up to $1 trillion in annual value for banks; it may equally destroy value for laggards.
The strategic question is no longer whether to adopt AI, but how fast and on whose terms. Those that deploy it to build transparent, resilient, customer-centric models will define the next era of finance. Those that treat it as a cost-cutting gimmick risk finding that the technologies automating their workflows can just as easily automate their customers away.
Joseph DiVanna is managing director of Maris Strategies and a Duke CE educator
