Artificial Intelligence and Data Management: The Next Phase of Trading Innovation
Artificial intelligence (AI) is rapidly transforming how firms process and analyze data. For traders, the deployment of AI alongside cloud-based infrastructure is creating new efficiencies in everything from risk management to alpha generation. At the recent TT Connect: The Evolution of Execution event in London, industry experts discussed how advances in AI and data management were reshaping how they approached global markets.
Modern Data Management
A fundamental challenge in today’s capital markets is managing the vast pools of data. The last decade has seen a massive increase in the rate and scale at which data is created. The challenge of managing the sheer volume of data is compounded by the fragmentation of data sources, requiring firms to constantly reconcile data across multiple sources and systems.
AI is increasingly central to managing this complexity. Firms are deploying AI to map diverse datasets, search and analyze across large data pools and improve client experience with use cases that involve accepting unstructured instructions, interpreting intent and routing requests.
At the same time, AI is augmenting human decision-making across multiple workflows from risk and surveillance to client communications. But it is not replacing humans. The companies that are getting ahead using AI today are those that strike the right balance between human judgement and AI.
This balance is particularly important when it comes to deploying AI for regulatory and compliance workflows. If a regulator asks the reason behind a trade, the answer can’t simply be that the model chose it. Firms need to have documented intent for each strategy and provide clear audit trails of decision making.
Creating Transparent Workflows
Providing this visibility across the trade life cycle brings more benefits than just regulatory compliance. In the front office, linking the order management system (OMS), execution management system (EMS) and transaction cost analysis (TCA) creates a single, transparent workflow from pre-trade to post-trade.
Consistency of data across the three systems is key. The data the desk sees before the trade should match the post-trade record with no need for manual reconciliations or entry of missing fields. When the OMS, EMS and TCA systems align, firms can reconcile fills efficiently, both internally and with counterparties, understand slippage and better evaluate alpha. The same, clean dataset should be available internally to portfolio managers (PMs), risk and compliance as well as externally to clients and counterparties.
Harmonization of data across OMS, EMS and TCA also strengthens governance. Each order carries documented intent, real-time conditions and a strategy choice that can be shared with regulators and investors. TCA analysis can also feed back into the EMS intraday to refine strategies and improve the next trade.
AI and the Cloud
Cloud-based architecture has made the management of data at scale far more efficient. For example, the Financial Industry Regulatory Authority (FINRA) Consolidated Audit Trail System on Amazon Web Services (AWS) processes over 650 billion messages annually.
The initial shift to the cloud started as a way to boost data storage and access to compute power. Now, firms across the market from exchanges to trading firms are increasingly deploying applications in the cloud, from analytics to risk management and even exchange matching engines. What used to take months to develop on local infrastructure can now be spun up, tested and pushed into production within hours.
A key theme in the panel discussion was elasticity. Market data volumes, AI-driven workloads and back-testing requirements are driving increased demand for compute capacity. Cloud infrastructure allows firms to increase compute when they need it, for example during periods of high volatility when trading volumes and data requirements rise. This ability to spin up and reduce capacity on demand has changed what is possible for firms in capital markets.
The Future of AI
The combination of the increased capacity to analyze data in the cloud and AI is fundamentally transforming the trading desk. Algorithms are now capable of processing a huge amount of information and adjusting execution in real time.
This is driving the emergence of agentic AI—autonomous AI agents that can make decisions and execute tasks without prompts. For firms in capital markets, this represents the next phase of automation.
Agentic AI changes the nature of controls. However, as autonomy grows, so does the importance of audit trails. The fundamental conclusion of the panel was that while AI automation will accelerate insights and efficiency, human accountability will not go away any time soon.
Ultimately, the future competitive landscape of capital markets will be defined by how organizations manage optimization and also risk and controls across their digital infrastructure.