Why banks still don’t start with your name

An ABF Summit keynote challenged how banks begin customer interactions.

Boston Consulting Group’s Platinion Managing Director & Partner Colin Dinn asked the audience at the Asian Banking & Finance and Insurance Asia Summit, “How many organisations in this room start a conversation asking about a customer’s name?” 

“I asked this question 20 years ago in Singapore, and I got exactly the same response. Bankers and insurance people don’t really start conversations with names. Walk into a hotel, they ask for your name. You talk to a banker, they ask for your account number or your policy number. Very personal, isn’t it? We’re all a number.”

He then asked, “How many organisations in this room have a C-suite executive responsible for customer?” He counted four hands.

Dinn said that, despite improvements, most organisations still design around products, not people. “We’ve always said that the customer is the most important thing. Yet we always design around a product that we want to sell, rather than what the customer really needs.”

He gave examples from banking’s evolution. In the 1990s, banks used propensity analysis to define “what a good customer looks like” and then tried to find others who fit the same profile. The next wave was CRM systems, which he said were “actually leads management” focused on outbound selling rather than real relationships. Later came customer experience initiatives and customer data platforms. Dinn described each as a step forward but said that even now, “most organisations think of their product.”

On digitisation, he said banks investing heavily during Covid, believing that digital would embed them more deeply in customers’ lives. “We thought digital was going to be the lifesaver… but digital is now seen just as a project. It’s not considered or designed around what the customer wants.”

“If you look after your customer, the profit will follow. But that’s my view.”

Dinn presented a case study of an AI-first bank. He described a 22-year-old freelancer named Mai, supported by her personal AI assistant Jai. After Mai’s paycheck arrived, Jai checked her expenses, showed her balance, and suggested investments. Jai set aside money for a trip when she requested it. When Mai got engaged, Jai helped manage her wedding expenses. Later, when she opened a café, Jai analysed her business transactions. After her revenue rose 15%, Jai automatically increased her working capital loan limit.