A strategic approach is essential if artificial intelligence (AI) is to bring about truly sustainable change in the financial services sector in Europe.
To achieve meaningful transformation in Europe’s financial sector, the strategic application of artificial intelligence (AI) is essential. While AI holds the promise of fundamentally reshaping the financial landscape, the road to realisation is paved with challenges. The question remains — how can financial institutions ensure scalable, sustainable success in their AI initiatives?
Through my numerous discussions with our clients and industry experts, one point has become clear. AI holds significant potential in Europe, but its success depends on several critical factors. Institutions that prioritise data quality, regulatory compliance, and a strategy rooted in alignment of their business cases with truly client-centric solutions stand the best chance of capitalising on the advantages AI
offers.
Generative AI and AI Agents Shaping the Future
Generative AI and AI agents present a unique opportunity for the financial sector, driving efficiency and accelerating revenue growth. These tools are no longer theoretical. Generative AI is already delivering personalised product and service offerings tailored to customer needs, while AI agents automate routine operations in testing, document analysis, and payment processing.
Additionally, these technologies play a pivotal role in enterprise modernisation by assisting with tasks such as translating legacy programming languages like COBOL and identifying optimisation opportunities, particularly in reporting protocols. Given the prevalence of legacy systems and strict regulations in Europe, its financial institutions are well positioned to benefit from these advancements.
Data Integrity and Compliance for AI Success
Modernising outdated core banking systems has become a critical priority for European financial institutions. While AI provides an efficient approach to this challenge, stringent regulatory requirements add a layer of complexity to its adoption. Implementing AI in this environment requires a deliberate and structured approach, starting with centralised data access, data governance, and improved data quality.
The importance of addressing data quality lies in the fragmented IT systems and siloed information with which financial institutions often struggle, making it challenging to achieve a unified customer view. High-quality, accessible data is essential for scalable AI solutions to deliver meaningful and impactful results.
Meanwhile, adhering to regulatory compliance in Europe’s financial sector remains non-negotiable for widespread adoption of AI. Institutions must meet rigorous standards set by regulatory bodies such as the EBA, ESMA, and EIOPA to maintain secure and reliable systems while avoiding penalties.
Building the Foundation for AI Strategy in Financial Services
Ensuring the successful implementation of AI within European financial institutions requires four key measures:
- Define Clear Business Goals
AI initiatives must align directly with clearly defined and measurable business objectives. Centering the business case on the customer and understanding its value — combined with clear success criteria and metrics — ensures projects are built on a robust foundation. - Engage Business Stakeholders
Transparency and collaborative involvement from key decision-makers foster trust and prevent disconnected projects that fail to contribute meaningfully to the broader business strategy ending up stalking in the PoC phase in sandbox and never getting to be scaled for full adoption. - Regulatory Automation
Embedding regulatory compliance into AI systems through automation ensures secure, reliable, and adaptable solutions that remain future-proof against regulatory changes. - Technology Partnerships
Collaborating with specialised technology providers bridges knowledge gaps and accelerates the AI implementation process, especially when scaling existing systems.
Collaboration Driving the Future of AI
AI is no longer a distant prospect but a critical pillar within the strategic frameworks of leading financial institutions today. However, effective AI integration demands business alignment, compliance-centric execution, robust data infrastructures, and a spirit of innovation fostered by collaboration.
By prioritising these elements, financial institutions can position themselves as leaders, leveraging AI to drive operational excellence and unlock new avenues for growth.
Stay tuned for my next article, where I’ll explore the specific value of COBOL modernisation using LLMs. Also, don’t miss my upcoming session at the AI in Banking 2025 conference on the 3rd and 4th of September, where these topics will be discussed in greater depth.