AI adoption in the banking sector is not a ‘race’ but a question of trust: HSBC

AI achievers enjoyed 50% greater revenue growth on average.

safer, easier to drive, and also faster in races. That’s similar to banking.”

Instead of a self-piloting customer interface, HSBC is using AI to enhance its security and the backend, according to Wong.

“We use AI to protect our customers’ money, and our data as well. For example, we use AI to help us fight financial crime. We use AI to monitor our customer activities, to make sure that we can recognize any irregular behavior, so we can spot fraud,” she said, on fraud. “We use AI to make sure that we’re not doing anything illegal or unethical when it comes to our trading practices. So that’s the safer part.”

She added that AI has been used to make loan processing faster, and to help with speech recognition processing in foreign markets.

Banks and industry leaders should not see the adoption of artificial intelligence (AI) as a “race”, as safety and maintaining the trust of customers should remain their first priority when embracing new technology, says HSBC’s Charmaine Wong.

“I wouldn’t say we’re slow to adopt or we failed. I think that we’re doing it (AI adoption) in a safe way, and not necessarily visible to the customers,” Wong—who is HSBC’s head of group BI & analytics as well as group head of ESG data & analytics–told attendees of a panel on AI in banking during the Singapore Fintech Festival 2022. “Banking is about the business of trust; [and so in] adopting AI, we need to do it in a safe way that maintains the trust of our customers.”

Wong’s comments come after the panel’s moderator, Accenture managing director Lee JoonSeong, shared a survey of 1,200 executives globally that indicated that the banking and capital market executives don’t believe that banks are high in the AI maturity curve.

AI adoption can offer a muchneeded boon for banks: the study noted that leaders who mentioned AI on their 2021 earnings calls are 40% more likely to see their stock prices shoot up. In the same study, this time looking into 1,000 companies, Accenture found that only 12% are what can be called AI achievers. These are firms whose AI maturity is advanced enough to achieve superior growth and business transformation.

According to Lee, AI achievers can attribute nearly 30% of their total revenue to AI on average. And even in the pre-pandemic era, they enjoyed 50% greater revenue growth on average, compared with their peers. They also outperform in customer experience and sustainability, Lee said.

In response to these, Wong noted that customers may have a different expectation of how AI should be adopted by banks versus how adoption is in reality. “Let’s rewind back time, about five to 10 years ago, if you were to say where AI in banking is, we would have imagined that AI would be doing banking for us, and there would be very little human interaction. But we all know now that’s not necessarily the case. [For example] within the automative industry, AI is being used to make it

Two halves

Standard Chartered’s Manohar Chadalavada has a different take on the maturity of AI adoption amongst banks. Chadalavada, who is the bank’s global head of ecosystems and open banking, noted that in Asia, integration of AI into their products depends on whether a service is classified as “high risk” or “low risk.”

“The adoption of maturity on low risk items is slightly higher,” Chadalavada said, with banks in Asia readily adopting and using robotic process automation (RPA) tools, conversational AI, chatbots.“But when you go into high risk models, I think the maturity is still way off because we are still in the experimental stage and it will take us some time to adopt that.”

In that space, banks are lagging behind their fintech peers, he said.

“The high risk models which they use in many banks, which has an impact on capital or customer decisioning, is still nascent. We are way behind many of the fintech lenders who are operating in this market,” Chadalavada noted.