How AI is reshaping financial services: key trends, applications and business impact 

Not just automation: AI as a way to rethink services, decisions and trust

Artificial intelligence can be seen as one of the great accelerators of our time. But the most relevant question is not only what AI can do. It is what financial services can become when AI is used to redesign decisions, experiences and trust.

In financial services, this question carries particular weight. AI enters processes that involve money, identity, credit, risk, payments and customer relationships. Every technology choice can therefore influence how a customer is recognised, how a transaction is authorised, how fraud is detected or how a credit decision is assessed.

Talking about AI in financial services means going one step further: is artificial intelligence simply a tool to speed up and automate processes, or can it help banks, fintechs, PSPs and companies rethink how financial services are designed and delivered?

Do we want to use AI only to make processes faster, or to make them more adaptive and useful? What changes when a financial service appears at the exact moment it is needed? Who is responsible when AI starts acting on behalf of people or companies? And how can trust be protected when fraud itself becomes more intelligent?

These are the questions that make artificial intelligence in financial services a strategic topic before it is a technological one. They are also the questions to start from when looking at what is really changing for businesses and customers.

Will AI only make us faster?

Speed is often the first benefit organisations look for in AI. And the financial services sector, where processes are complex, regulated and distributed across multiple functions, has already moved well beyond experimentation.

In many cases, AI finance applications are still used to make already structured activities more efficient, especially in areas such as:

• Onboarding and document verification;
• KYC and compliance checks;
• Reconciliation and exception management;
• Customer support;
• Compliance and reporting.

The risk, however, is stopping at surface-level optimisation. Using AI to automate a single step can save time, but the real leap in quality comes when AI is used to redesign how that step fits into the entire workflow.

Think about onboarding, credit assessment and fraud prevention. Depending on data quality, customer profile, risk level, entry channel or applicable regulation, these processes may need to follow different management paths. Used at a higher level, AI can make them not only faster, but also more adaptive.

The same applies to teams. AI used as a simple accelerator can reduce repetitive operational tasks. AI integrated into the workflow can also suggest priorities, reduce information noise and help people focus on activities where relationships, judgement and accountability remain central.

AI will therefore make financial services faster, and it is already doing so. But the real objective is to understand when automation is enough and when the process itself needs to be redesigned.

What happens when the financial service arrives before the request?

For a long time, financial services belonged to recognisable flows: the banking app, the provider portal, the checkout page, the back office. Today, those boundaries are shifting. Payments are increasingly embedded into purchase journeys, credit solutions can appear within cash-flow management experiences, and insurance coverage can be integrated into journeys already in progress.

These are examples of Embedded Finance. According to Mordor Intelligence, the global embedded finance market is expected to reach around USD 155.96 billion in 2026 and USD 454.48 billion by 2031, with a CAGR of 23.84% over the period.² This growth reflects the ability to bring financial experiences directly into sectors and digital flows that were once far removed from finance.

But bringing a financial service into a digital experience is only the first step. What is the user doing in that moment? What signals emerge from their behaviour? What is the transaction risk? Which payment method is most likely to work? Which offer can be useful without becoming intrusive?

AI can help read these signals and recognise when a financial service is truly relevant. For banks, fintechs, PSPs and companies, the challenge is therefore to turn a technical integration into a contextual financial experience: the right service, at the right time, with the right level of control.

This means ensuring that payment is no longer just the final step of a transaction. It becomes a point of observation, a source of data and a moment in which trust can be built.

When AI acts, who is responsible?

Until now, many AI banking and AI fintech applications have worked mainly in support of people. The rise of agentic AI marks a further step: artificial intelligence begins to perform tasks autonomously, while still operating within controlled environments and defined objectives, limits and permissions.

In the financial sector, this shift opens important possibilities. An AI agent can collect documents for onboarding, prepare an application and operate within payment or credit processes. The Cambridge Centre for Alternative Finance report notes that agentic AI is already in active adoption among 52% of the financial institutions surveyed, with 23% in more mature scaling or transforming phases and 29% still in piloting.¹

Concrete examples are already emerging. Mastercard has introduced Agent Pay, a programme designed to integrate AI agents into payments through tokenisation, agent registration and authentication, consumer control, transparency and fraud protection.³ In March 2026, Santander and Mastercard announced the completion of Europe’s first live end-to-end payment executed by an AI agent within a regulated banking framework.⁴

The point, then, is no longer only what AI is able to do, but within which boundaries it can do it. If an AI agent can execute a payment, companies need to define who authorises it, which limits it cannot exceed, and which controls are applied before and after the action.

Trust by design

If fraud becomes intelligent, simply adding new controls to existing processes is no longer enough. Security strategies need to evolve, starting with fraud prevention systems that support the entire user journey.

Trust begins to be built through the way the service is designed: which data it uses, which signals it interprets, which limits it applies, when it asks for additional verification and when it involves a person.

The objective is to make AI part of a stronger service model, where security, user experience and responsibility are not separate elements, and where fraud prevention becomes part of the financial experience itself.

This is also where machine learning financial services applications, generative AI banking solutions and broader AI governance converge: not only to detect anomalies, but to design more secure, contextual and accountable experiences from the start.

Where is competitive advantage built?

Returning to the initial question: in which direction will the financial services sector move thanks to the push of AI?

For banks, fintechs, PSPs and companies, the key will be to understand that competitive advantage does not come from having more AI tools, but from using them with a clear direction: where to automate, where to personalise, where to increase control, where to leave room for human judgement, and how to move from isolated technology projects to a coherent architecture.

From this perspective, AI can make processes more efficient, but above all it can help build more useful services, more informed decisions, safer payments and simpler experiences. The challenge is to prevent technology from remaining an operational shortcut and instead use it to rethink how the financial sector creates value.

This is where an open and modular infrastructure becomes essential. Through Fabrick’s Open Finance platform, companies can connect services, APIs and payment capabilities to build new use cases and business models. Depending on country availability and regulatory framework, solutions such as payment orchestration, embedded finance, account-to-account payments and fraud-prevention services can help organisations design financial experiences that are more connected, governed and ready for the next wave of AI-enabled interaction.

AI will not automatically make financial services better. It will be up to companies to decide whether to use it to accelerate what already exists or to build better, more intelligent models that are closer to the real needs of the market.

This is the real artificial intelligence financial services challenge: moving beyond experimentation and turning AI into a strategic architecture for the AI financial industry.