Banks are cautiously adopting AI in deposit pricing to improve customer segmentation, understand rate sensitivity, and deliver targeted offers, while regulatory hurdles and trust concerns are limiting its use in dynamic pricing. Institutions are moving from intuition-driven or static rate sheets to analytics and machine learning models that assess customer behavior, preferences, and competitive positioning to make more data-driven pricing decisions. With greater insight into customer attributes and preferences, banks say they are using AI making more informed calls on when to offer pricing exceptions, or how to price their deposit offerings more broadly. Research on rate-sensitive customers through AI can help paint a picture of future pricing moves across customer segments. For example, Chris Nichols, director of capital markets at SouthState Bank notes that some less rate-sensitive customers may care more about good customer service than shaving a few basis points off their savings or CD rate. Valley National Bank said it’s in the early stages of using AI to inform pricing strategy. Sanjay Sidhwani, the bank’s chief data and analytics officer, says the bank has used AI — traditional AI and machine learning — to garner insights on which customers are rate-sensitive. But he emphasizes that AI-based analysis isn’t a hands-off process. The bank can use it alongside other data to help inform pricing strategies, including how long someone has been a customer, what products they use, and how they interact with various channels. The bank is also using AI and machine learning to compare its historical competitive positioning — such as how its rates ranked on aggregator sites — with how many new deposits that approach brought in. These insights help inform pricing decisions and also guide how much to spend on marketing a particular deposit product. To implement true dynamic pricing, banks must navigate regulatory barriers, including compliance with rules on unfair, deceptive, or abusive acts or practices (UDAAP), and concerns about price discrimination, Sidhwani says. Without dynamic pricing, argues Sidhwani, banks “won’t be able to compete…they’re going to have to get there,” he says. Sidhwani predicts dynamic, AI-powered pricing could arrive within two to three years.