Artificial intelligence is reshaping almost every industry, and finance is high on that list. Some estimates suggest that AI could boost the income of the financial services sector by more than a third and significantly support economic growth.

In such a competitive space, firms are constantly looking for tools that give them an edge. AI is already used in areas like credit scoring, risk management, fraud detection, trading, personalisation of financial services and process automation. Digital assistants help people plan their finances, set goals and manage day-to-day spending more easily.

In short, AI is becoming one of the engines of productivity and value in finance. At the same time, it can also introduce new vulnerabilities and risks.

This article looks at how AI can support liquidity providers (LPs), where the main challenges lie, and why the way LPs use technology matters for brokers.

AI as a Supporting Tool for Liquidity Providers

For brokerages, capital efficiency is everything. To grow revenues and deliver a good trading experience, they need optimised pricing and solid liquidity conditions for their clients.

When AI is applied to liquidity provision, it can help LPs:

• analyse market conditions in real time,
• identify attractive entry and exit levels,
• adapt quotes to changing market dynamics,
• support data-driven decisions with advanced analytics.
  

It’s no surprise that a large share of market participants see strong potential for AI to transform investment and liquidity processes – not only at the trade level, but also in pre- and post-trade decision-making.

Why Market Knowledge Is Still Imperfect

In theory, having perfect information about the market would make pricing and liquidity decisions much easier. In reality, information is fragmented and often incomplete.

Liquidity providers typically:

• do not know exactly who they are competing with at any given moment,
  
• cannot see their competitors’ full real-time pricing strategies,
  
• do not have a direct view of total liquidity demand – they only see the orders routed to them.
  

Because of this, LPs operate with partial visibility. AI and advanced analytics can help them make better use of the data they do have, but they do not magically remove asymmetry or uncertainty.

Some approaches described in the industry involve concepts like price prediction and scenario weighting to support more dynamic liquidity allocation. The idea is to use data and modelling to refine how liquidity is provided over time, not to automate everything end-to-end.

Key Challenges and Threats in Using AI

Using AI-driven tools for pricing and speed adjustment can offer clear benefits, but it also raises a number of practical questions.

Among the main concerns are:

• Regulatory and compliance issues – firms must ensure that automated tools respect rules on fair treatment, best execution and transparency.
  
• Data protection and privacy – handling more data, more intensively, can increase the surface area for potential misuse or breaches.
  
• Data quality – AI models rely on clean, relevant and accurate data; poor data can easily lead to poor decisions.
  

These challenges are not unique to liquidity provision. They are common to many sectors going through digital transformation, including banking and capital markets. For LPs, they appear in areas such as:

• unauthorised record retention,
• cybersecurity and attack resilience,
• risk of data leaks or unauthorised access.
  

There are also more technical risks:

• Over-reliance on algorithms can create blind spots. Models are always trained on finite historical data, while market conditions can change in ways that past data did not capture.
  
• Scalability and flexibility of AI models matter. Tools need to cope with evolving markets, new asset classes and growing acceptance of digital assets.
  
• Ethical and governance questions remain open. The industry is still debating how to handle edge cases and ensure responsible use of AI.
  

In other words, AI does not remove complexity. It can help manage it, but only when there is strong supervision, clear processes and a solid risk culture on the LP side.

How AI Can Enhance the Efficiency of LPs

The role of liquidity providers has already evolved. It is no longer just about quoting the tightest possible spreads. Today, LPs also look at:

• sensitivity to skew and market impact,
  
• how their quotes affect order flow and risk,
  
• how to allocate resources efficiently while still offering competitive execution.
  

At the same time, the volume of data that needs to be monitored has grown enormously. Markets move quickly, new instruments are added, and client behaviour changes across sessions and regimes. This makes real-time analysis and rapid adjustment more important than ever.

AI and advanced algorithms can support this by:

• helping to process large data sets more efficiently,
• improving aspects of price formation in fast markets,
• adding structure to the way qualitative or “subjective” effects are measured – for example in liquidity reviews and ROI assessments.
  

The aim is not to replace the judgement of experienced teams, but to give them better tools for looking at complex systems.

The Future of AI in Liquidity Provision

Looking forward, many in the industry see AI as part of the next phase of liquidity management. One of the concepts often discussed is dynamic liquidity provision (DLP) – an approach that has its roots in decentralised finance but is being explored more broadly.

The long-term objective of AI-supported liquidity solutions is to:

• use measurement, simulations and optimisation as inputs into execution systems,
• create feedback loops that continuously improve how liquidity is managed,
• take into account not just explicit transaction costs, but also factors such as cost of capital or cost of clearing.
  

This could, in theory, lead to more precise pricing and a clearer view of the true cost of liquidity.

Whether we call it AI, advanced analytics or simply better tools, the direction is similar: more data is used, more systematically, to support decisions that were once driven mainly by intuition and experience.

Friend or Foe: What Does AI Really Mean for LPs?

There is no doubt that AI is becoming more visible in liquidity services. As data plays a greater role in business decisions, tools that can process and interpret it effectively will naturally gain importance.

At the same time, AI is not a silver bullet. Its impact depends entirely on how liquidity providers design, govern and supervise their systems.

Used wisely, AI can:

• improve the agility of liquidity provision,
• support better market monitoring and prediction,
• help optimise transactions and processes behind the scenes,
• contribute to smoother order placement and execution for end-clients.
  

All of this can strengthen the relationship between LPs and partner brokers, making it more transparent and efficient. But there are conditions: platforms need to remain scalable, flexible and robust, and the human side of risk management cannot be removed from the loop.

Choosing the Right Liquidity Partner

For brokers, the practical takeaway is simple: select partners not just for the technology buzzwords they use, but for the stability and quality of the liquidity they deliver.

A good LP should have:

• solid technology infrastructure and dedicated teams to run it,
• solutions that can support a broad range of instruments,
• a clear approach to risk, compliance and transparency.
  

X Open Hub is a global institutional liquidity provider offering access to over 5,000 instruments and operating under multi-jurisdictional regulatory oversight. Our mission is to deliver stable, reliable liquidity and to support our partners in navigating an increasingly complex electronic market.

To learn more about how we can support your brokerage with institutional-grade liquidity under different market conditions, get in touch with our team.

This article is for informational purposes only and does not constitute investment, legal, tax, or other professional advice. Any references to markets, instruments or strategies are illustrative and do not constitute a recommendation to buy or sell any financial instrument. Past performance is not a reliable indicator of future results. Trading leveraged financial instruments involves a high level of risk and may not be suitable for all investors.

X Open Hub provides liquidity services only to regulated institutional clients and does not offer services directly to retail investors.