The launch of ChatGPT by OpenAI in late 2022 marked a watershed moment for generative AI. Reaching millions of users within days of launch, few industries were spared the shockwaves – banking and payments included. The initial buzz sparked a gold rush of activity: a spike in AI-related hiring, a surge in patent filings, frenzied M&A activity and an explosion of corporate interest.
The clamor has only grown. GlobalData figures suggest the total AI market – spanning software, hardware, and services – will expand from $103 billion in 2023 to over $1 trillion by 2030, with AI platforms emerging as the fastest-growing segment. Within that, generative AI is carving out a rapidly expanding share: valued at just $2.8 billion in 2023, it is forecasted to skyrocket to $75.7 billion by 2028, a compound annual growth rate of 93.7%. And the generative AI market in banking is predicted to carve out a huge chunk of this growth – nearly $8 billion by the end of the period.
As financial institutions grapple with everything from customer service to compliance, lending, and fraud detection, generative AI’s influence cannot be overstated. But what does it all mean for SMEs, who are dependent on the smooth running of payment services, will increasingly have to use these services mediated through generative AI, and are doing so all while attempting to roll out AI-driven products and services of their own? The ability to answer these questions will determine which firms sink or swim in the rising AI tide.
The areas to watch
Generative AI is a huge growth area. Isolating its influence on a number of key banking and payment business areas, and examining where it can be used most effectively, will be critical for SMEs looking to exploit the benefits.
First, the ability to tailor products and services to individual needs. AI models can now extract insights from unstructured feedback – product reviews, call transcripts, even facial expressions. The tech can anticipate life events, suggest risk-adjusted investments, and deliver pre-qualified loans in near real time. As a result, some early-adopting finance firms have reported thirty-fold jumps in conversion rates; with customer advice becoming more nuanced, timely, and profitable all the time, keeping abreast of change is crucial. [1]
Next, channels between businesses and their customers. Ironically, the most visible application of generative AI – chatbots – is where results remain most elusive. Recent GlobalData consumer surveys suggest widespread wariness around bots persists. For now, generative AI enriches advisor workflows more than it replaces them – searching policies, summarizing interactions, and drafting responses. In wealth management, it helps bankers parse legalese and research reports. Although full autonomy is some way off – constrained by regulation, risk, and customer suspicion – deploying it to boost productivity now is a must.
It is in the middle office where generative AI has found its strongest foothold. Synthetic data is transforming fraud detection – training models on high-fidelity simulations of illicit behaviour, rather than relying on sparse real-world incidents. “With the increase of both APP fraud and card details being stolen in the UK and US, it is no surprise that global financial services providers are looking at improving their fraud protection measures and turning to AI to help achieve this,” says Harry Swain, GlobalData banking and payments analyst. “The fast rate at which instant payments are settled means that fraud must be detected in milliseconds. As digitalization continues, other financial service providers and fintechs will leverage the use of Gen AI in their fraud prevention measures.” AI can also ingest alternative data – cash flows, invoices, rent payments – to help with credit scoring, while many laborious compliance tasks are ripe for automation. As AI handles complexity at speed, the manual burden of checks and documentation shrinks – turbocharging productivity for its users.
Finally, the financial system’s infrastructure. Beneath the surface, generative AI is quietly remaking the financial system’s plumbing. The software development lifecycle – often bloated and slow – is being accelerating. AI tools can write 40% of the code in some recent pilots, catching bugs faster, cutting technical debt and liberating engineers for higher-value work. For large institutions, this translates into nine-figure cost savings. Why shouldn’t SMEs explore similar productivity boosts? As embedded finance expands, so too does the need to integrate seamlessly – and generative AI is proving itself a reliable co-pilot in that race. Small as well as large businesses need to keep up.
How SMEs can survive and thrive
Generative AI offers an opportunity to turn financial systems into engines of competitive advantage. For SMEs, that means offering banking experiences once thought prohibitively complex or costly. Tailored lending, proactive cashflow advice and contextual product recommendations are all in the crosshairs; the same technologies powering the rise of super-apps and smart digital banks are now accessible throughout the market. And consumers now expect this level of precision and responsiveness; smaller firms must keep up or risk irrelevance.
From back-office operations like stress testing fraud and credit models, to customer-facing tasks like streamlining onboarding and offering support, few areas of banking and payments will be left untouched by the rise of generative AI. It heralds a revolutionary shift for all businesses, and an existential moment for smaller businesses in the thick of it.
But it is one they needn’t navigate alone. Mastercard Business Intelligence offers deep expertise and powerful tools to help businesses of all sizes stay competitive, make smarter decisions, and thrive in a rapidly changing landscape. Log in with your Mastercard Connect credentials here to explore our full suite of business intelligence solutions. Don’t have access yet? Request your credentials here.
[1] GlobalData report, “Generative AI in Banking,” February 2025.
