Events
July 3, 2025
Last week in London, financial leaders gathered for a landmark event hosted by Mirai RiskTech – its debut in the UK capital. And true to form, London delivered.
In the elegant setting of Hispania London, over 20 senior ALM professionals from 14 institutions (including both traditional banks and neobanks) came together for an evening marked by sharp insights and lively exchange. The event reaffirmed the city's enduring role as a global financial powerhouse.
From Compliance to Catalyst: ALM and Balance Sheet Management in the Age of AI
At the heart of the discussion was a timely and thought-provoking theme: “From Compliance to Catalyst: ALM and Balance Sheet Management in the Age of AI.” Featuring perspectives from Ignacio Núñez, Treasurer at Santander London, and Miguel Ángel Penabella Aláez, former Global Director of Structural & Market Risk at Santander, and expertly moderated by Eoghan ÓGriobhtha, UKI Managing Director at Mirai RiskTech, the session explored how artificial intelligence is reshaping the future of balance sheet strategy and regulatory engagement. The evening opened with remarks from Eoghan ÓGriobhtha and continued as a conversation among the panelists.
A shift in ALM
The event opened by acknowledging the disruptive environment facing treasury and risk professionals. In an era where macroeconomic shocks ripple with renewed frequency, and where technology accelerates faster than institutions can sometimes adapt, the role of Asset and Liability Management (ALM) has never been more critical.
Over the last five years, volatility has returned to the rates market with force, shining a brighter spotlight on the strategic role ALM plays within banking. With this shift, fundamental questions emerge:
- What challenges are created by rate volatility?
- What strategic advantages do banks gain from strong ALM practices?
- And where are the winners and losers starting to emerge?
The biggest challenge has been the reawakening of interest rate risk, not just as a risk to be hedged, but as a key driver of strategic decision-making. The margin for error has narrowed, and effective ALM is no longer just about regulatory compliance or balance sheet optimization: it’s about enabling agility.
The most resilient banks are those that can model multiple outcomes, understand complex correlations, and align funding and capital decisions in near real-time. In this context, banks with strong ALM teams have the upper hand; not only can they weather shocks, but they can lean into opportunity when others are retreating.
The horizon, however, holds more uncertainty. Political instability, shifting regulatory expectations, and a rapidly evolving economic landscape mean that the pace of change is unlikely to slow. ALM must evolve from a traditional oversight function into a forward-looking strategic engine.
And this is where the second pillar of the event´s discussion — Artificial Intelligence — enters the conversation.
Artificial Intelligence: Embedded, evolving, and here to stay
Switching gears, the room turned its attention to AI: not as a buzzword or something hypothetical, but as a tangible force already being integrated into the day-to-day.
From meeting notes to complex forecasting, banks are already integrating AI tools, such as ChatGPT or Copilot, into their daily workflows to enhance productivity and research.
These tools are not fringe; they are now foundational.
The conversation around AI pivoted on two central questions:
- What is AI’s true role in this highly analytical, regulation-heavy space?
- Can large enterprises adopt the tech fast enough to innovate, or will inertia hold them back?
At first glance, the field may appear too niche or quantitative for natural language models, but that underestimates the dual nature of the work: part science, part art. Stochastic models dominate, but there are also policy frameworks, scenario analysis, and decision support areas that are ripe for AI augmentation.
What role does AI have to play in ALM?
Adoption is still complex. Large enterprises move cautiously for good reasons: there are challenges in data quality, system integration, and model interpretability. The reality is that most banks have committed to an AI strategy, but very few have operationalized live models, especially in ALM. This isn't due to a lack of interest but rather a necessary caution: getting it wrong has real implications.
Initial findings show that AI is the most effective in ALM as a partner, not a replacement. The deep domain knowledge required means that models work best when built hand-in-hand with practitioners.
Some highlights
- ALM & BSM Use Cases: From machine learning models for stochastic simulations to the early integration of LLMs for policy and scenario planning, AI is already reshaping workflows.
- Adoption Curve: Financial institutions may be lagging behind tech in the cloud race, but signs suggest a more proactive posture on AI, albeit cautiously optimistic.
- Model specificity matters: Not all data is "AI-ready." As little as 10–20% of banking data is currently usable for LLM training, underscoring the urgent need for data strategy reform.
- Regulatory & Ethical Challenges: Data privacy, legacy architecture, and interdepartmental silos remain persistent blockers. But these are not seen as insurmountable.
Key reflections
In the end, the mood in the room reflected the theme of the event: we are standing not at the end of a process, but at the start of a reformation. Compliance may have been the foundation, but AI is fast becoming the catalyst, and it’s not about being first to implement; it’s about understanding the capability, finding the right fit, and driving value.
Consider key reflections from the evening:
- ALM is no longer just a control function; it’s a strategic partner, increasingly relied upon in times of uncertainty.
- The return of volatility and the rise of AI are not two separate topics, but intertwined dynamics shaping the future of banking.
- AI will not replace the judgment and experience of ALM teams, but it can amplify them. The challenge and the opportunity lie in finding that balance.
- Banks should adopt AI on their own terms: large institutions don’t need to act like fintech, and fintech shouldn’t try to replicate scale. Strategic advantage comes from leveraging inherent strengths.
Acknowledgment and thanks
From Mirai RiskTech, we want to extend a heartfelt thank you to all the speakers and attendees who made this event possible.
That evening in London reinforced our commitment to innovation, technical excellence, and staying closely connected to the financial sector. We're continuing to drive forward solutions that help banks streamline their path toward a more agile, accurate, and future-ready balance sheet management, adapting to new tools and knowledge that lies ahead.
Join our next events and help us build a thriving ALM community: https://mirairisktech.com/events
