Magnus Haglind, head of products for marketplace technology, Nasdaq
The first wave of gen-AI use cases across capital markets technology has sparked widespread energy and excitement about its future potential. At the same time, it has triggered a sense of urgency across infrastructure providers globally that they must act now to avoid being left behind.
Market operators face two critical questions today. Firstly, what is the right operating model and the critical capabilities they want to develop and maintain in-house or source from external providers? And secondly, how do they ensure that they will have access to these advanced capabilities, given the extraordinary level of energy and compute capacity that will be required to power their markets in the future?
If we look ahead to capital markets over the next two decades, the future of trading infrastructure will be built on a fabric of interconnected markets with a common data architecture, seamless connectivity throughout the ecosystem of exchanges and participants, minimal latency, and advanced AI-powered tooling.
Operators don’t have long to embark on their data and tech modernisation journey to get there, and we’re proud to be supporting so many of our infrastructure clients on this path.
Gideon Mann, global head of AI technology, Millennium
What was cutting-edge generative AI (GenAI) in early 2024 will look outdated over the next twelve months. Successful organisations will adopt a long-term GenAI strategy, balancing immediate applications with the flexibility to adapt to future innovations in this space.
In the investment management sector, the diversity of challenges allows us to identify some of the most promising applications of GenAI. These are likely to result from a collaborative approach between technologists and their end users including investment professionals, legal, compliance and finance teams, among others. We have seen early applications of GenAI in the areas of market observability, risk assessment and operational efficiency. In 2025, organisations will be looking to scale the use cases that have shown the greatest potential.
Jamil Jiva, global head of asset management, Linedata
As we leave 2024 behind, artificial intelligence is set to transform industry practices. From risk management and regulatory compliance to predictive analytics and cybersecurity, AI promises to bring a new era of transparency, efficiency and innovation to the finance ecosystem.
The widespread adoption of Explainable Artificial Intelligence (XAI) in risk assessment and management systems marks a decisive turning point. This technology is finally lifting the veil on the ‘black box’ of algorithms behind AI inference systems, offering a clear understanding of AI decision-making processes. This creates an opportunity for financial institutions to renew and reinforce the confidence of customers and regulators while improving the accuracy of their risk models.
Nick Wood, AI product manager, FINBOURNE
While AI clearly has the potential to enhance operating margins and reshape the asset management industry, serious adoption remains slow. This hold up is largely due to a lack of confidence in the incumbent data management processes, which need to be designed to support AI technologies.
While AI can certainly act as a feature and capability in an overall workflow, firms must be able to explain the models and trust the quality of the underlying data to get there. With AI showing so much promise, prioritising modern data infrastructures to address data quality concerns will be a priority for many asset managers next year.
Jim Kwiatkowski, chief executive, LTX (a Broadridge company)
This year, the fixed-income market experienced notable advancements, with credit market volumes reaching an average daily volume of $49.8 billion, reflecting a 23% year-over-year increase. This growth is projected to continue into 2025, fuelled by a steady increase of credit e-trading.
Looking ahead, AI is poised to play a pivotal role in bond trading, transforming how fixed-income traders, analysts and portfolio managers process and leverage the growing volume of data from electronic trading. By enabling streamlined access to vast, disparate datasets, AI enhances decision-making in areas like bond selection, trade list construction and protocol optimisation. As AI adoption scales, the market can expect a more vibrant secondary trading environment characterised by improved pricing, enhanced liquidity, and stronger overall performance.