AI to lift domestic HK banks’ earnings by 8-17%

This analysis is by Bloomberg Intelligence Senior Industry Analyst Francis Chan and Associate Analyst Nicholas Ng. It appeared first on the Bloomberg Terminal.

Artificial intelligence could boost pretax earnings by up to 17% or as much as $1.5 billion a year at domestic Hong Kong banks BOCHK, Hang Seng and BEA on a follow-through basis, based on BI estimates, 2023 filings and Deloitte research. The boost to HSBC and StanChart may be even bigger. Staff cost savings and improved loan pricing are potential gains. HSBC currently has over 1,000 AI use cases.

HSBC, HK banks go with tide of AI adoption

HSBC, Standard Chartered and other Hong Kong banks are going with the tide of global AI adoption, with more use cases likely to emerge by 2030. Though AI may not have yielded much cost and revenue impact so far, the technology has been broadly applied in three areas: risk management, client engagement and administration. For instance, HSBC relies on AI tools in screening 1.35 billion transactions per month for financial crimes and sanctions. The technology is also deployed in treasury operations, loan underwriting, customer services, asset-liability and liquidity management in HSBC, StanChart and BOC Hong Kong.

HSBC, with the largest branch network in Hong Kong, had more than 1,000 AI use cases as of late October. The bank is currently testing 100 other solutions riding on generative AI.

AI to boost domestic HK banks’ pretax profit by 8-17%

Success in fully integrating AI tools with operations at three domestic Hong Kong banks — BOCHK, Hang Seng Bank and BEA — could boost their pretax profits by 8-17% a year on a follow-through basis, based on our estimates and Deloitte research. Their combined profit could rise up to 5% on workforce efficiency, and 2.8-4.3% on lower credit costs, by applying assumptions in Deloitte’s report “Changing the Game” to 2023 financials. Total cost savings might hit $240-$781 million.

On the revenue front, tailored loan pricing adjusted for risks could lift pretax profits by 3-4.5%, improving customer acquisition and retention by 1.3-2.5%, along with market analysis plus trading algorithms (0.8-1.2%). Their combined revenue might rise $434-$698 million.

By the same token, HSBC and StanChart can boost group pretax profit by 9-25%.

Bloomberg Terminal clients can access detailed assumptions here.

What impact can AI have on banks’ bottom lines?

HSBC, StanChart’s billions of savings from digital, AI

There’s no one-size-fits-all approach in determining what constitutes a robust training set for AI models in drug development. Yet we believe the most useful to industry will be diverse training sets constituting both positive and negative outcomes derived from multiple sources, multimodal across data types and relatively free of bias through means like random sampling. To the final point, larger-cap companies with long histories of drug development and broad internal datasets might be advantaged, given the universe of published data is frequently cherry picked to propagate a hypothesis.

We think combinations of clinical, cellular and chemistry data can provide a more robust decision engine than any one individually, but it carries the challenge of properly annotating multimodal datasets.

Data challenges vs. customer service, trading opportunities

The Hong Kong financial-services industry, led by banks, will consider adopting more AI over the medium term. According to a joint survey by PwC and the Hong Kong Trade Development Council, industry participants see the biggest AI opportunity in facilitating automated customer service, followed by algorithmic trading, investment management, fraud detection and claims processing. At least 60% of respondents view these as key areas of AI applications. Meanwhile, 68% of those taking part in the survey cited data availability and quality as the key challenge in AI usage, followed by cybersecurity and data privacy, and a skills gap in the workforce.

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