The US Fed’s decision to keep interest rates higher for longer continued to benefit Hong Kong banks’ performance in 2023, with notable increases in net interest margins (NIM) and operating profit.
A recent report by global consulting firm KPMG revealed a 30 basis points increase in NIM, to 1.84%, for all licensed banks in Hong Kong, which includes global players such as HSBC, Standard Chartered, DBS Bank, as well as Chinese and local institutions including Bank of China Hong Kong (BOCHK) and Hang Seng Bank.
Operating profit before impairment charges for all licensed banks increased by 34.7% to HK$295 billion ($37.8 billion), compared to 2022. Total assets expanded by 2.7% to HK$23 trillion.
Local banks enjoyed strong margin performance and moderate growth in their overall balance sheets during last year, according to Paul McSheaffrey, senior banking partner, Hong Kong, at KPMG China.
At the same time, he also noted that as timing and pace of a rate cut remain uncertain, banks should plan their strategies accordingly. Treasury outlook from the Oversea-Chinese Banking Corporation (OCBC) pointed out that as recent inflation readings had boosted the Fed’s confidence on bringing down inflation, rate-cut odds shifted to the dovish side. Bank of America (BofA) is forecasting a first rate cut in December, despite a growing possibility of an additional one in September.
With a lower interest rate environment in view, the KPMG team anticipates supporting tailwinds for banks’ investment banking business, as equities are expected to be more attractive.
Additionally, cost optimisation was underlined as one of the key themes for banks globally, with some of them aiming for 10% cost efficiencies over the next 12 months and up to 20% to 30% over the next three years. This is in part due to anticipated rates reductions by the Fed, but also tied to a relatively weak investment banking segment in markets such as Hong Kong, and a greater need to manage credit risks in their loan profiles.
“Banks will be focused on consolidating common capabilities, the elimination of non-value-added activities, digitising key functions, reducing labour costs, critically linking process metrics to customer outcomes, and managing credit risk,” a press release from the consulting firm noted.
GenAI hype
Along the macro trends, banks have turned to generative artificial intelligence (GenAI), with ambitions of improving customer experience, increasing efficiency and optimising cost and returns ultimately. But for now, much is considered hype and will require more time for concrete developments.
Jia Ning Song, head of banking and capital markets, Hong Kong, at KPMG China, underlined in the press release: “While generative AI is the trending topic in 2024, it will likely take some time for use cases to emerge, and true adoption and productivity gains from GenAI will probably become the story of 2025 and beyond.”
While a majority of GenAI use case trials run by banks focused on customer service, Song told FinanceAsia that the team is expecting the emerging tool to bring more value to middle and back-office functions, helping with manual intensive duties such as compliance. But that might still take some time.
“A lot of the banks we talked to are not ready for scalable adoption of GenAI yet, with a lack of adequate data or infrastructure,” he said.
Luc Hovhannessian, chief revenue officer, treasury and capital markets, at financial software provider Finastra, echoed this view in a separate conversation with FA.
According to Hovhannessian, a wider adoption of GenAI across banking institutions would not happen before it is integrated into a bank’s core system, a process that requires sophisticated coding, cautious scrutiny and absolute security.
From the team's point of view, the technology has so far been helpful during the process of product design for programmers, those inhouse with banks or at third-party fintechs. While at the same time, it’s moving slowly towards an integration into products themselves, with pilot projects being tested out, he shared.
The industry in general is still cautious around scaling up GenAI functions in core products, before conducting rigorous security checks and launch of designated modules, he added.
Hovhannessian said that the adoption of GenAI will, ultimately, lead to cost reduction generated from better-informed decision-making processes and higher efficiency compared to purely manual labour.
Still, he believes that GenAI will be “the most influential AI revolution” for financial institutions on the treasury front.
For example, the Finastra team has been exploring use cases in risk assessment and modelling, where GenAI is expected to analyse historical data to assess interest rate risk, credit risk and liquidity risk; also, it has looked at algorithmic trading and execution, where it helps with strategy execution based on predefined rules and market conditions. In addition, it has looked at stress testing and scenario analysis, where the technology helps simulate stress scenarios and assess their impact on a bank’s balance sheet.
“This can lead to the treasury front office being better equipped with information before trading,” he added.
Financial institutions have been pushing forward a more general level of digitisation across functions, apart from cutting-edge technology developments such as AI.
Taking treasury functions as example, Hovhannessian pointed out that Apac banks are increasingly asking for quick deployment, open platform, scalability and resilience from their external fintech partners. Quicker adoption, the ability to add in new functions and products at relative ease, as well as high availability infrastructure are what the banks need the most.
He told FA that the team is now in talks with a leading Chinese bank in terms of system applications, where “over 80%” of the conversations have been around building a resilient and secure platform.
Banks are adopting innovative approaches to help bolster resilience and security across their operations, he added, saying: “There is growing interest in exploring the benefits of private cloud infrastructures and proactively vetting qualified third-party vendor partnerships to ensure all points of the customer value chain remain safe and secure as they continue to innovate."
Despite these benefits, barriers exist.
“The challenges are deep domain knowledge of treasury management and digital skills gap, complexity of implementation, high cost of digital transformation, increased dependency on legacy technology and organisational siloes," Hovhannessian concluded.
