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AI in electronic trading: from signal to control

AI in electronic trading: from signal to control

AI is entering electronic trading through the controlled layer of the stack: better signals, sharper execution insight and stronger surveillance, not autonomous decision-making. That is where the near-term value sits, and where the near-term risk needs managing too.

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At a glance

75%

UK financial services firms already using AI in 2024, with adoption still rising

Source: Bank of England and FCA, Artificial Intelligence in UK Financial Services (2024)

2%

Reported AI use cases that involve fully autonomous decision-making

Source: Bank of England and FCA, Artificial Intelligence in UK Financial Services (2024)

1 in 3

AI use cases that rely on a third-party implementation, concentrated among a few providers

Source: Bank of England and FCA, Artificial Intelligence in UK Financial Services (2024)

Summary

Electronic trading is already automated, fast and highly controlled. AI is now being added into that environment as a source of analytics, predictions, recommendations and monitoring signals.

For buy-side firms, sell-side firms, market makers and trading venues, the opportunity is real. So are the risks. Poorly governed AI can create model risk, conduct risk, market abuse risk, operational fragility and weak auditability.

This paper gives leaders a practical view of how AI can be used safely in electronic trading environments, without overstating what the technology can do.

This paper sets out where AI can add value, where deterministic low-latency controls still matter, and what leaders need in place before moving AI-enabled trading capabilities into production.

What this paper covers

  • Where AI is being used across electronic trading today.
  • Why most production AI is embedded into controlled workflows, not left to trade autonomously.
  • How latency affects where AI can and cannot be used.
  • Where AI fits in the electronic trading stack — and where deterministic controls still matter.
  • What governance, testing, model risk, kill switches, fallback rules and monitoring need to be in place.
  • How firms can move from AI strategy to controlled production implementation.

Who should read this

This paper is written for COOs, CIOs, Heads of Markets, Heads of Electronic Trading, Trading COOs, CROs, Compliance leaders, Transformation leaders and senior executives responsible for electronic trading, market access, surveillance, controls or trading technology.

Capmark perspective

AI in trading should be treated as a controlled production capability, not a side experiment or an autonomy story.

The question is not whether AI can produce a signal. The question is whether the firm can govern that signal in live operation, integrate it into the trading workflow, monitor it, evidence it, challenge it and stop it when required.

Capmark helps clients move from strategy through implementation into live operation, with practical support across trading operating model design, algo governance, EMS/OMS integration, model risk, surveillance uplift, delivery roadmap and BAU transition.