Thai banks told to use dynamic pricing

They are struggling because their pricing strategy is not transparent and too reactive.

Banks in Thailand should use artificial intelligence (AI) to improve pricing, putting customers in different categories based on their willingness to pay, according to a banking consultant.

Financial institutions in Asia tend to offer unstructured pricing and discounts, in which nonleading products end up being heavily discounted despite high service costs, Silvio Struebi, a partner at Simon-Kucher & Partners, told the Asian Banking & Finance and Insurance Asia Summit in Thailand this month.

“There is a big lack of data,” he said. “The whole intelligence that a bank or financial institution has is not available to relationship managers at the point of sale, and this often leads to inconsistent pricing.”

Financial institutions also struggle because their pricing method is not transparent and too reactive, Struebi said. Banks could harness AI in analysing customer behaviour and then classifying them based on their price sensitivity, he added.

“This segmentation informs price elasticity models, mapping customer responses to pricing changes,” he said. “By doing so, we can predict which clients are likely to withdraw funds and which will remain despite rate reductions. This scientific approach eliminates guesswork from deposit pricing.”

Struebi said banks could adopt dynamic pricing, where they adjust prices based on clients’ willingness to pay using data provided by a marketing automation engine. Minor details such as wording, context, and background imagery can drive desired behaviours.

Struebi said banks that adopt dynamic pricing should be aware of ethical issues.

“Dynamic pricing should not exploit clients but rather enhance transparency and fairness,” he said. “Ensuring that pricing recommendations are justifiable and aligned with regulatory requirements is a key focus for [Simon-Kucher].”

Struebi expects advancements in AI-driven pricing, particularly with real-time market integration.

“Imagine a future where relationship managers receive automated, data-backed recommendations as they interact with clients — enhancing both efficiency and client satisfaction,” he added.