Insights

How AI Is turning banks into digital CFOs for SMEs

Amaya Maillo Martín, Client Partner Santander, UST Spain & LATAM

Artificial intelligence is transforming business banking from a transactional service into a predictive, insight-driven relationship. By combining AI with transactional data, banks can help SMEs anticipate financial needs, improve decision-making and strengthen customer relationships, creating value that extends far beyond traditional financial products.

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KEY TAKEAWAYS

• SME banking is evolving from a transactional, reactive model to a more predictive, strategic and data-driven relationship.
• Artificial intelligence enables banks to anticipate liquidity needs, payment default risks and working capital decisions before they become business challenges.
• Banks have a unique advantage: a comprehensive view of a company's financial activity. The opportunity now is to turn that data into actionable insights.
• The real transformation is not about adding new AI tools, but about redefining the value proposition, commercial model and customer relationship.
• Financial institutions that fail to seize this opportunity risk losing relevance to technology platforms, ERP providers and embedded finance solutions.
• Banks that help SMEs make better financial decisions can position themselves as true digital CFOs, building stronger, trust-based relationships with their customers.

Current accounts and credit: is that still enough?

A few years ago, when we spoke to SMEs, the role of a bank was fairly straightforward: it was where money came in and out, and where businesses turned when they needed financing. That defined the relationship. It was transactional, occasional and largely reactive.

For many financial institutions, that model still exists today. Relationship managers reach out when it's time to renew a credit facility, suggest supply chain finance solutions when they spot increased supplier activity, and little else. For many SMEs, the bank is seen as a necessary part of doing business—one that charges fees for services they don't always understand and asks for paperwork when cash flow becomes tight.

The challenge is that SMEs are no longer measuring their banking experience against other banks.

Instead, they’re comparing it to Shopify, which offers funding based on sales data without requiring a single document. They're comparing it with invoicing software that predicts which customers are likely to pay late, or with financial management tools that forecast cash flow months in advance—capabilities that many banks could already provide proactively, given the wealth of data they already hold.

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How is AI reshaping business banking?

What is beginning to happen, and what is likely to accelerate rapidly over the coming months, is a fundamental shift in the role of business banking. Banks are moving beyond the place SMEs turn to when cash flow becomes a problem. Instead, they are becoming partners that help businesses identify and address financial challenges before they arise. The focus is shifting from selling financial products to delivering financial intelligence.

This is what many describe the bank as a digital CFO. And it is far more than a concept; it is already becoming a reality:

Real-time cash flow forecasting. Not just a better-looking bank statement, but predictive models that show business owners what their cash position is likely to look like in 30, 60 or 90 days. By combining invoicing data, payment history, business seasonality and upcoming financial commitments, today's AI models can achieve forecasting accuracy of 90–95% over a four-week horizon. By comparison, the spreadsheet-based forecasts still used by many SMEs rarely exceed 78% accuracy.

Proactive liquidity alerts. Instead of reacting when an overdraft has already occurred, AI can detect that, based on current payment and collection patterns, a business is likely to face a cash shortfall weeks in advance, giving it time to act before liquidity becomes critical.

Early payment default prediction. AI models analyse customer payment behaviour, including payment frequency, changing invoice values and progressively longer payment delays, to identify potential default risks before they materialise. This goes far beyond traditional credit scoring, using hundreds of variables in real time and continuously improving with every transaction.

Intelligent working capital optimisation. The system can recommend supply chain finance solutions when early supplier payments would improve the company's financial position or proactively suggest factoring when it identifies slow-paying invoices from creditworthy customers, all without the business having to request them.

FX hedging aligned to real business activity. For SMEs operating internationally, foreign exchange recommendations can be tailored to actual buying and selling patterns rather than relying on a standard catalogue of treasury products.

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Why is business banking changing now?

Three factors make this the right moment for business banking to evolve, and waiting another two years may simply be too late.

1. AI has moved beyond experimentation. Not the AI making headlines, but AI delivering measurable business outcomes. Machine learning models are already achieving cash flow forecasting accuracy exceeding 90%. AI-powered treasury agents can reduce the time required to build a cash flow forecast from 2–4 hours to less than 15 minutes. And large language models (LLMs) are enabling conversational banking experiences, allowing business owners to ask simple questions such as "How is my cash flow looking?" and receive clear, actionable answers instead of navigating complex reports.
2. Digital platforms are already redefining customer expectations. Shopify offers funding based on merchants' sales performance. Amazon provides contextual credit to its sellers. Invoicing platforms are beginning to predict payment defaults, while ERP solutions increasingly include predictive treasury capabilities. If banks do not claim this space, others will. And when a platform becomes the place where an SME manages its financial decisions, it becomes the primary relationship, leaving the bank to operate in the background as little more than the infrastructure moving the money.
3. The window of opportunity is narrowing. According to Oliver Wyman, 46% of SMEs in Western Europe already use embedded financial services through non-banking platforms, while 64% plan to increase that usage over the next year. Every business that becomes accustomed to receiving financial intelligence from its ERP or commerce platform relies less on its bank for strategic support. And once banking is reduced to moving money, it becomes a commodity.

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Why is this a business transformation, not just a technology project?

This is where many organizations get it wrong. The answer isn't to buy a cash flow forecasting tool and add it to a banking app. That's simply adding another feature. The real opportunity lies in rethinking the entire value proposition for SMEs.

Consider what banks have that no one else does: a complete transactional view of a business. Every incoming payment, outgoing payment, payroll, tax payment and account transfer. No ERP, software platform or invoicing solution has access to that complete picture. Banks do.

But having data and creating value from it are two very different things. Turning that advantage into meaningful outcomes requires three key shifts:

• Transform data into actionable intelligence. Move beyond reporting and dashboards to deliver recommendations that help customers make better decisions. Instead of showing what has already happened, banks should be able to say: "Based on your current cash flow, you're likely to face a liquidity gap in six weeks. Here are three actions you can take today to avoid it."
• Redefine the customer relationship. Relationship managers should evolve from selling financial products to delivering insights customers don't already have. Imagine a conversation that starts with: "We've noticed that one of your key customers is taking longer to pay. Would you like to explore a factoring solution before it starts affecting your cash flow?" That fundamentally changes the nature of the relationship.
• Adopt a proactive rather than reactive model. Instead of waiting for SMEs to ask for help, banks can anticipate challenges, recommend solutions and create value before problems arise. That is the real shift: moving from responding to demand to actively helping businesses make better financial decisions.

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What's at stake isn't market share; it's relevance

For many banks, the real challenge is not keeping pace with technology. It’s recognizing that customer expectations are fundamentally changing.

SMEs account for more than 99% of businesses in Spain and generate around 65% of employment. Yet, despite their importance, they have historically been underserved because delivering highly personalized financial services at scale has been costly. AI changes that equation. It enables financial institutions to deliver more sophisticated, data-driven services with greater efficiency and at a significantly lower marginal cost.

The institutions that move first won't just gain market share; they'll earn something far more valuable: relevance. They'll become the trusted partner SMEs turn to, not because they have to, but because they consistently deliver insights and support that no one else can.

And in a world where, according to McKinsey, 70% of commercial banks already use AI in at least one core business function, competitive advantage will not come from adopting AI alone. It will come from using it to create better customer experiences and deliver meaningful business value.

Those that fail to make this shift risk competing on little more than pricing and fees, a race few institutions can truly win.

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Turning strategy into reality

At UST, we've been working alongside financial institutions to make this transformation tangible, not as an innovation concept, but through measurable business outcomes. Our work includes AI-powered cash flow forecasting, next-best-action engines for relationship managers, enterprise data platforms that create a unified customer view, and AI solutions designed to anticipate financial needs before they become business challenges.

Across these initiatives, one lesson stands out: technology is no longer the barrier. The capabilities already exist; they are mature, proven and ready to scale. The real challenge is organizational. Success depends on recognizing that this is not simply a digital initiative, but a transformation of how banks create value and build relationships with business customers. That requires business, data, technology and risk teams to work toward a shared vision.

The financial institutions making the fastest progress understand one simple truth: they won't lose SME customers because another bank offers a better financial product. They'll lose them to organizations that deliver a better experience. And the next generation of business banking won't be defined by institutions that simply sell financial products; it will be defined by those that help businesses make better decisions every day.

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Final reflection

The transformation of business banking is already underway. The technology to anticipate financial needs, generate intelligent recommendations and deliver more proactive customer experiences is already here. The real differentiator will not be AI adoption itself it will be how effectively financial institutions use it to redefine the value they create for SMEs.

At the same time, customer expectations are evolving. SMEs no longer compare their bank solely with other banks. They compare it with the digital experiences they have every day through the platforms and tools they rely on to run their business.

The opportunity is no longer technological. It is strategic.

Ready to redefine business banking?

At UST, we help financial institutions transform data into actionable intelligence, enabling more predictive, personalized banking experiences tailored to the real needs of SMEs.

Discover how we help organizations accelerate their digital transformation and unlock the full potential of artificial intelligence: ust.com/es

FAQs

Q: What does it mean for a bank to act as an SME's digital CFO?

A: It means evolving from a product-centric banking model to one built around financial intelligence. By combining data and artificial intelligence, banks can help SMEs anticipate liquidity needs, optimize cash flow and make better financial decisions before challenges arise.

Q: How can artificial intelligence improve business banking?

A: AI enables banks to analyse vast amounts of data in real time to generate cash flow forecasts, identify potential payment default risks, uncover working capital optimization opportunities and deliver personalized recommendations tailored to each business.

Q: Why are SMEs expecting a different banking experience?

A: Increasingly, SMEs compare their banking experience not with other banks, but with the digital experiences offered by platforms, ERP systems and business software. As a result, financial institutions are expected to provide more proactive, personalized services that deliver real business value.

Q: What are the benefits of a more predictive banking model for SMEs?

A: A predictive approach helps businesses anticipate liquidity challenges, improve financial planning, reduce risk and make better-informed decisions based on timely, data-driven insights.

Q: How can financial institutions move toward this model?

A: The first step is turning transactional data into actionable intelligence. This requires combining artificial intelligence, advanced analytics and a new customer engagement model to deliver more proactive, personalized banking experiences that address the real needs of SMEs.