At the Hong Kong Budget 2025-26 speech, Financial Secretary Paul Chan said that the Hong Kong Stock Exchange (HKEX) will gradually introduce new functions to its post-trade system from the middle of this year, to ensure technical compatibility with the T+1 settlement cycle by end of this year.
Additionally, the HKEX, together with Hong Kong’s regulator the Securities and Futures Commission (SFC), will review the “board lot” trading system, and is expected to put forward proposed enhancements to facilitate trading processes this year.
Trading efficiency improvement is raised as one of the key strategies for Hong Kong to help its status as a global financial centre. And all over the world, streamlining settlement and operations have become a key focus for many financial institutions, facing pressure from clients, or a more competitive market.
This comes when artificial intelligence (AI) and large language models (LLM) have become disruptive technologies that could ‘revolutionise’ the way financial institutions traditionally trade, according to multiple sources.
Institutions face the need to increase competitiveness and balance the cost of investments into thesis emerging technologies. Meanwhile, access to trading tech has been made easier and such tech tools are being democratised, through widespread experiments with AI tools.
To decode the power of AI and generative AI (GenAI) in financial markets, especially when it comes to trade operations, FinanceAsia spoke to several tech leaders on how this space is evolving for financial institutions.
Power of AI
One example is KCM Trade, a Mauritius-headquartered CFD (contract for difference) broker, launching its ‘AI Mentor’ function on February 6. According to the press release, the new feature helps provide personalised insights to traders on investment strategies through analysis of past trading records.
Similarly, FBS, a foreign exchange broker, introduced its AI Assistant that offers analysis within seconds, simplified trading insights based on price trends, patterns and key market signals, as well as trade ideas, to traders on its platform.
Brokers and trading tech providers have been exploring the power of AI, especially GenAI, to optimise trading processes. Larger companies such as Broadridge Financial Solutions rolled out its BondGPT application in 2023 to help traders identify fixed income opportunities and respond to bond-related enquiries.
Similar trends apply to the post-trade stage – Broadridge launched OpsGPT early last year to facilitate more efficient trade settlement through the visibility of transaction data and the autosuggestion of risk factors. In January, an advanced analytics feature was added to generate visualisations directly from users’ trade data.
The R&D team at KCM Trade explained to FA that the “AI Mentor” was built to serve as a personal mentor to support traders.
On the one hand, the algorithm aggregates financial market updates from public sources; on the other, it analyses a trader’s personal trading history and patterns. Combining these two, the AI tool will be able to identify a trader’s weaknesses and make timely suggestions.
“Traders need only a few minutes to absorb the key insights and make informed decisions,” the team explained. “AI Mentor simplifies complex financial content into clear, actionable insights, making trading easier and more accessible, even for beginners.”
More broadly, cybersecurity risk detection and risk modelling are two of the most common applications of AI and machine learning across financial services’ businesses, according to research from FIS. For example, in securities processing, this could be real-time monitoring of trade breaks.
Jon Hodges, head of trading and asset services, Apac, at FIS, said that there is a growing trend of buy-side financial institutions, such as hedge funds, asset managers and insurance companies, to adopt traditionally sell-side technologies.
This is due to growing pressure to find new revenue streams, reduce risk, drive operational efficiencies, and deliver new value for customers.
Two of FIS’ traditionally sell-side focussed solutions – cleared derivatives platform, which provides direct access to trading venues and clearing houses; and cross-asset trading and risk platform, which helps with asset diversification and scaling up new strategies – are seeing growing adoption by buy-side players.
“While technology is changing the dynamics of the buy- and sell-side relationships, it presents an opportunity for the sell-side to redefine their value proposition,” Hodges said.
“Many are offering value-added services such as bespoke analytics, risk management tools, and customised trading solutions,” he added.
Consolidation
AI comes at a time when financial institutions are required to improve operational efficiency to compete, while, at the same time are looking to save costs.
Mike Sleightholme, president at Broadridge International, pointed out that margin compression has driven financial institutions to look for more effective ways to grow, but with a lower average cost of trading.
That leads to a certain degree of consolidation, where trading firms tend to work with only a handful of key strategic service providers, on many fronts, for economies of scale across asset classes.
The FIS team’s research suggested a similar simplification and streamlining approach to save costs and improve efficiency. Many organisations used to work with various vendors on point solutions, which results in siloes across functions and the high costs of maintenance.
Sleightholme added that AI-driven tools will be most useful in terms of trading research in the front office; and risk management during the operations and settlement processes. In addition, resiliency and cybersecurity will emerge as more important topics over the next few years.
A solution for larger players who have adequate capital to deploy is to develop an in-house tech system, in order to keep sensitive trading data under control; on the other hand, smaller organisations are cautiously evaluating the risk of outsourcing such adoptions.
Additionally, regulatory pressure for financial institutions to gain effective oversight of the technologies they are using is growing; efforts include the Monetary Authority of Singapore’s (MAS) technology risk management (TRM) guidelines.
Demand for hosted Software-as-a-Service (SaaS) solutions is also rising, said Sleightholme. A SaaS approach helps mutualise costs across asset classes and the value chain, and at the same time enabling a financial institution to host and run the technology suites, themselves, for greater controllability.
Looking ahead, AI will still play an important role in financial firms’ cost-cutting and efficiency optimisation journey.
Meanwhile, blockchain-based adoptions will increase to provide an alternative that helps speed up trade settlements. The US Securities and Exchange Commission (SEC) has implemented a national shift towards a T+1 settlement cycle, which has particular challenges for traders based in Apac due to wide time differences with the US.
"There is a clear expectation that the world will continue to move to shorter settlement cycles,” said Sleightholme. "The ongoing push for innovation means that firms must adapt quickly to stay competitive in a rapidly changing landscape."
