3 days ago
What happens when money thinks for itself?
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This content has been selected, created and edited by the Finextra editorial team based upon its relevance and interest to our community.
This is an excerpt from The Future of European Fintech 2025: A Money20/20 Special Edition.
The evolution of financial technology is characterised by increasing levels of simplicity, efficiency, and integration. We saw this in 2016, when Europe's second Payment Services Directive (PSD2) encouraged financial institutions to open up their data and infrastructures – paving the way for banking-as-a-service and embedded finance. Fast-forward to 2025, and preparations are already being made for PSD3 – and even deeper levels of functionality and harmonisation.
But the technology's development is hardly linear, and every so often innovations land that spark a deep and wide cross-industry revolution. Few would argue that artificial intelligence (AI) lacks this potential, particularly in the world of financial services and product personalisation. So, what might the dawn of AI mean for stakeholders? What happens when money thinks for itself? Enter the stage: Embedded intelligence.
Standard Chartered's Vibhor Narang, executive director of structured solutions cash management, transaction banking, Europe said in an interview with Finextra: 'AI and big data are redefining the landscape of financial services, propelling the industry toward an era of hyper-personalisation and smarter client engagement. At Standard Chartered, we see AI as the engine driving a shift from generic offerings to deeply tailored financial experiences – leveraging advanced data analytics to anticipate client needs, delivering bespoke advice, and streamlining every touchpoint.'
Indeed, industry data shows that AI-powered personalisation can reduce operational costs and boost customer retention considerably, underscoring its transformative impact on both efficiency and loyalty. 'Our commitment is to harness these technologies,' Narang added, 'not just for incremental improvements, but to reimagine how we build trust and relevance with every client interaction.'
This level of transformation, however, will not come without challenges.
The complexity and opacity of advanced AI models – along with the volume of data involved – creates heightened concerns around privacy, explainability, and bias, Narang argued.
'We believe responsible innovation is non-negotiable: robust data governance, transparency, and ethical AI frameworks must be embedded at every stage,' he said. 'As financial institutions, our true competitive edge will be measured not just by how smart our algorithms are, but by how deeply we earn and sustain client trust in a digital world.'
So, by balancing cutting-edge innovation with unwavering stewardship of data and privacy, Standard Chartered is hoping to set a robust standard for the future of personalised finance. Conor McNamara, EMEA CRO at Stripe, emphasised the need for balance of personalisation and security, stating that businesses need to manage the complexity of checkouts carefully, 'to strike the optimal balance between fraud prevention and conversion; doing it poorly introduces needless friction, and causes legitimate sales to be blocked or abandoned.'
A spokesperson from NatWest was in alignment on the utility of AI for personalisation, as well as the surrounding data and ethical issues: 'AI is undoubtedly a key enabler of our ambitions and is quietly reinventing how we operate – freeing up our colleagues and helping them to provide the service our customers expect. As we increasingly use AI to support personalised interactions, such as with our AI-powered chatbot Cora, which provides everyday banking support, we are seeing real improvements in customer satisfaction and colleagues' productivity. At the same time, we are taking a considered and measured approach to make sure any AI usage is managed responsibly.
'That's why we've developed a set of ethical AI and data principles to ensure our systems are subject to human oversight, technically robust, free from unfair bias or discrimination. Privacy is a critical focus, particularly in light of [the European Union's General Data Protection Regulation (GDPR)] regulation, and our robust code of conduct ensures we evolve with new regulations while maintaining trust and transparency.'
BNY Mellon's Carl Slabicki, executive platform owner, treasury services, added that for treasury clients, AI and big data can help deliver personalised cash management strategies, predictive analytics for liquidity forecasting, and customised risk management solutions. However, Slabicki stressed that achieving this requires 'careful consideration of key challenges, including data privacy and security, regulatory compliance (such as GDPR), and maintaining consumer trust through transparent and ethical AI practices.'
It would seem that striking the balance between innovation and these critical factors will be essential for the widespread adoption of AI-driven treasury services across the market, in the short to mid-term.
Ahmed Badr, chief operating officer, GoCardless, said that 'the combination of large language models (LLMs) and machine learning (ML) means that it's now possible to deliver personalisation at scale. ML is stronger when it comes to mining structured data for the deep insights that are needed for truly personalised offerings, and AI provides the automation and efficiency to help organisations manage a myriad of personalised offerings.'
Used in the right way, these technologies can be a powerful combination, though Badr also acknowledged the need for institutions to tread carefully: 'The 'watch-out' is knowing where to draw the line when it comes to using personal or identifiable data which would rightly be viewed suspiciously by customers. Financial organisations have access to vast amounts of data which can be anonymised and used to power personalisation models, without breaching confidentiality and trust. This approach allows for highly personalised interactions with AI agents that can respond in natural language, giving customers greater control and a more intuitive, tailored experience.'
McNamara highlighted the potential of agentic AI to proactively act on behalf of the user in ecommerce spaces, something that Stripe is currently developing: 'Our new AI Agent SDK and Order Intents API enable AI agents to independently perform actions like purchasing products and finalising transactions based on natural language instructions from users, with security measures comparable to traditional mobile payments. Imagine instructing a digital assistant to secure travel insurance, instantly receiving optimal policy options, directly completing the payment, and immediately receiving documentation—all without manual intervention.'
The arena of payments was underlined by Magnetiq Bank's Julija Fescenko, head of marketing and communication, as a key area for the AI's use case within financial services: 'Predictive analytics and AI are poised to revolutionise the future of payments, significantly enhancing fraud detection, personalising user experiences, and improving risk scoring. This development will make solutions like Buy Now Pay Later (BNPL) more accessible and secure for both businesses and consumers.
'The integration of AI and big data will transform how financial services are tailored to individual needs,' Fescenko continued. 'Imagine hyper-personalised credit scoring, customised financial advice, and real-time product recommendations that anticipate customer needs even before they are expressed. While this level of personalisation is promising, it brings forth essential considerations around privacy. Our challenge is to design systems that embrace innovative personalisation while upholding privacy-by-design principles and ensuring full GDPR compliance.'
Transparency, data minimisation, and user control will be vital in maintaining a harmonious balance between pioneering advancements and consumer trust, concluded Fescenko.
Tom Moore, head of financial services at Moore Kingston Smith, underlined that smarter data will be pivotal. He pointed to three ways firms can use data more wisely:
1. For tailored product recommendations and customer experience improvements;
2. To gain a competitive advantage by analysing and predicting customer
behaviour; and
3. To improve fraud detection and risk management.
If firms can achieve this, Moore argued, they will be 'better able to innovate and stand out in a crowded sector.' However, he caveated that using AI can be expensive and not everyone will have the resources and talent to implement it.
Perhaps an even bigger challenge is 'balancing innovation with the essential privacy and compliance requirements,' Moore continued. 'Legislation needs to be understood – balancing the risks and ethical issues that are inherent in AI's [commercial use]. It's not always cheap to build AI into a business or to provide the right resources and talent to implement it. The sheer amount of personal data that needs to be managed and then scaled will pose a big challenge.'
As we have seen in the UK recently, holding a large amount of sensitive or personal information can make financial services players a prime target for cybercrimes. According to one Telegraph article, 65% of financial services firms were hit with ransomware attacks in 2024. This was up from 34% in 2021 and marked the third successive annual rise. Clearly, this is an issue that is not going away.
'So much to do with financial services, including the decisions customers make, hinges on trust,' Moore concluded. 'Boards have got to actively commit to using smart data to transform their businesses – and be transparent about how they use it.'
The application of AI: Ensuring access and affordability
A debate around the ethics of AI's roll-out would be incomplete without touching on the underserved and unbanked. With fintech services becoming increasingly AI-driven, what steps can be taken to ensure individuals in emerging markets also benefit from easy and affordable access?
BNY's Slabicki told Finextra that 'by providing innovative solutions to our clients, we believe we can play a significant role in bridging the gap between governments and corporates, and the underbanked communities with whom they need to transact.'
Slabicki highlighted BNY's alliance with MoCaFi, which aims to provide equitable financial services to unbanked and underbanked communities across the United States: 'Our alliance offers digital disbursement services, including prepaid and reloadable debit cards, accessible through a mobile app. These services provide secure and seamless access to funds, financial literacy tools, and Federal Deposit Insurance Corporation (FDIC)-insured accounts. By leveraging MoCaFi's expertise in benefit disbursement and program management, the alliance helps distribute payments efficiently, empowering individuals with limited access to traditional banking services.'
Magnetiq Bank's Fescenko echoed the importance of making AI-driven fintech accessible and affordable for underserved communities: 'We envision creating inclusive algorithms that actively work to eliminate bias, while also developing mobile-first and low-data solutions specifically designed for users in emerging markets. By collaborating with local fintech companies and non-governmental organisations (NGOs), we can effectively bridge the digital divide.'
Fescenko added that integrating financial literacy programmes, maintaining transparent fee structures, and offering multi-lingual support should help ensure that innovation 'uplifts rather than exacerbates inequality.'
How is AI making wealth accessible?
McNamara stated that Stripe has turned to stablecoin as an opportunity to expand global access to financial markets, explaining: 'Migrants use stablecoins instead of traditional financial services to avoid high fees and delays. Turkish Grand Bazaar merchants prefer them for supplier payments due to speed. In inflation-prone countries with dollar shortages, many adopt stablecoins as savings vehicles. This matters tremendously when approximately 1.3 billion people live in countries with average inflation rates exceeding 10%. Connecting a large part of the world's population to a faster and cheaper payment method directly benefits emerging markets.'
In alignment with stablecoin's potential, Stripe has acquired Bridge, a platform for businesses that want to build with stablecoins, that works with fintech companies that facilitate payments in Latin America such as DolarApp and Airtm. McNamara added: 'Throughout history, improvements in how money moves have expanded economic opportunity. From coins to banknotes, from gold to fiat currency, and from paper to digital payments - each transition has made commerce more efficient and inclusive. It is from this vantage point that we see promise in stablecoins.'
On the topic of inclusivity, NatWest confirmed that it is prioritising bank-wide simplification to become more efficient and effective, making it easier for its customers to do business with them:
'A key part of this is supporting more inclusive innovation. For example, we have made a minority investment in Serene, an early-stage AI platform dedicated to tackling financial vulnerability. Through real-time customer insights driven by AI and behavioural science, Serene helps identify early signs of financial distress and predicts risks to help institutions deliver personalised support at scale. This builds on our long-term commitment to improving access to affordable credit and financial resilience to vulnerable groups.'
'What I've found with clients,' Moore added, 'is that these innovations help unlock financial tools that were once out of reach for many. For example, robo advisers are making wealth management more accessible and affordable, which allows a wider audience to access high-quality financial advice without the cost. With the trend for global expansion, fintech firms can strategically target growth markets in Asia, Africa, and Latin America, where digital financial services are even more in demand. From what we've seen, the convergence of this global outreach with AI-driven affordability points towards an industry movement to better serve these underserved populations.'
The question of whether AI, big data, and embedded thinking, can transform the financial services sector has been answered: it already is. Equally easy to answer is the question of how AI should be applied – and how individuals can best be protected. Putting these values into practice, however, may take some work.
