How will upcoming technology impact Singapore and Hong Kong trading floors?
The adoption of technology in the trade finance sector has become more popular amongst top banks and financial institutions across Asia. This is largely due to the high competition between the Asian Tigers – Singapore and Hong Kong – to become the best trading hub in region. Technology has offered a way for organisations in Asia to innovate and keep up with customer demand while remaining efficient and productive.
Why is technology taking over trade finance?
Yahoo Finance reported that since 2017, trade finance transactions were worth over SGD9 trillion. Yet, the value of it is limited given the high costs incurred as the industry still remains heavily reliant on legacy systems and cumbersome paper-based systems. Today, banks are facing a paradigm shift to become more technology-driven to automate processes, enhance security, and save costs incurred on trading floors.
What are the current platforms used in the trade finance sector?
In many cases, the right platform infrastructure depends heavily on what strategies an organisation is interested in pursuing within trade finance. Below are the three main platforms:
- High Frequency Low Latency
- Medium Frequency Medium Latency
- Low Frequency Low Latency
Frequency of trading determines the pace of buying and selling of securities over a period of time. For example, High Frequency Trading (HFT) is the buying and selling of securities over a period of time, usually hundreds of times an hour or more. This high volume of buying and selling is carried out to profit from time-sensitive opportunities that arise during trading hours.
Latency on the other hand determines the speed of booking a trade. Both factors come together to impact the profitability of trade, depending on processing rates.
As profits are largely time-sensitive, HFT invest heavily in developing the ability to trade with low latencies, so they can reap opportunities of the trade first. Financial trading platform usually need very low latency trade processing – meaning that trades have to be processed quickly as the market changes rapidly.
What are examples of technology adopted within the above platforms?
- Automation and algorithmic technologies
There has been increasing pressure to adopt robotic advisors and algorithmic trading within the sector. These technologies serve as an automated form of investing, and is currently widely used in the trading industry. Its functionality allows the user to specify certain trading strategies and then executes trades based on that strategy.
Algorithmic technology allows for back-testing which gives traders the opportunity to test their strategies against historical market performance. It also suggests room for constant refinement of strategies which makes traders more agile in reviewing the performance of their strategy and optimise their trading decisions. This is also part of the flexibility that algorithmic trading allows for in reviewing their strategies and systems.
UBS has announced in early 2019 that it is making use of machine learning automation to run the algorithmic trading systems for its foreign exchange business. Currently investing millions of dollars in algorithmic technology, it aims to cut back on trading teams and rely more on automatically-computed strategies to trade more efficiently. JP Morgan, which also reported double-digit growth in its algorithmic trading business in 2019, released a new machine learning algorithm this year. Citibank is another top player in electronic currency trading who announced in March 2019 that it is planning to join UBS with an electronic currency trading and pricing platform in Singapore, setting up systems to boost liquidity in Asia’s biggest foreign-exchange hub.
- Distributed-Ledger Technologies (DLTs) and blockchain
Banks are using blockchain to digitise trade documents and automate multiple trade finance processes. This is widely expected to mitigate the risks of fraud in letters of credit (LoC) and other relevant documents in the industry. For example, Standard Chartered underscored that the blockchain solution had streamlined the digitisation and exchange of trade documents between parties in the supply chain network — providing not only more efficiency, but also increased security and transparency.
Last year, Hong Kong and Singapore’s HSBC banks linked their trade finance platforms they are developing with blockchain technology, to reduce potential fraud and errors in the multi-trillion-dollar funding of international trade. HSBC, Bank of America Merrill Lynch and government agencies such as the Infocomm Development Authority of Singapore are currently looking to use technology to make trade finance more efficient and reduce the risk of fraud in Singapore.
With the influx of technology in the sector, technologists are in for greater opportunities with their in-demand skillsets.
Technologists are taking over – how can your organisation prepare for the future?
Python is still the most common programming language for trade finance with packages for data analysis such as SciPy and Pandas. R also remains popular as it is the default programme used for statistical analysis in many local and overseas university courses.
Familiarity with the Hadoop ecosystem is also important, along with C++ know-how. Particularly in HFT, newer languages like Go are better suited for concurrent processing of big data sets.
We can help
The roles of trade finance developers and technologists on the trading floor is changing. They are multi-taskers who are capable in overseeing processes in an increasingly complex trading environment. Technically-savvy trade-desk managers assume more responsibilities and monitor complex products, markets and even algorithms in a highly dynamic financial world.
If you are looking to hire people with such skillsets, please contact us by filling up the form below. If you’d like to be up-to-date with industry trends, follow us on our LinkedIn page.
