Banks Flirt With AI Deposits but Fear Dynamic Pricing Backlash
By Suman Bhattacharyya, Contributor at The Financial Brand
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Executive Summary
- 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.
- While dynamic, AI-powered pricing faces technical, regulatory, and transparency challenges, bankers see it as an inevitable shift within the next few years as competition intensifies and customers gradually grow more comfortable with personalized, demand-driven rates.
Amid increasing competition in the deposit space and expected rate changes coming from the Fed, banks are under pressure to improve their deposit pricing strategies.
For years, they looked to tech to solve this challenge. Now AI is seen as a tool to better align deposit pricing with business goals. But it’s hardly a silver bullet: Banks are treading carefully, sticking to narrow use cases like customer churn likelihood or targeted offers. They’re also evaluating customer trust issues before introducing more disruptive changes like AI-powered dynamic pricing for deposit offerings.
“The use of AI in pricing is in a cautious space due to the regulatory hurdles,” says Adam Stockton, managing director at banking analytics firm Curinos. Instead, banks are typically using AI to segment customers, understand rate sensitivity and deliver relevant offers, he adds.
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From Rate Sheets to AI-Powered Pricing
Banks originally used static approaches to pricing, according to Neil Stanley, CEO and founder of The CorePoint, an Omaha, Nebraska-based banking consulting and software company.
“In a pre-internet world, static worked because your options were so limited that you had a lot of time to react before you saw a lot of movement,” he says.
Rate changes, he noted, were typically based on a banker’s intuition rather than a systematic analysis of transaction trends or a customer’s likelihood to vote with their feet if rates became unfavorable.
“[Bankers would think] that’s a really good customer — let’s give them a better deal,” he says. “I call that the ad hoc exception.”
The problem with static rates was that they were one-off decisions that weren’t informed by a broader analysis of customer data and behavior.
More than a decade ago, banks began using analytics software to assess economic conditions, competitor pricing and customer behavior to help determine the rates they should offer on deposits. Since then, efforts have expanded to draw on a broader range of data, with AI and machine learning tools assessing customer intent, rate sensitivity and competitive positioning.
“Ten years ago, it was just a straight linear regression [model],” says Chris Nichols, director of capital markets at SouthState Bank. More recently, banks have been using neural networks, “where you can take in a lot of different attributes — such as your proximity to the branch, age of your account…and then have some feel for how rate sensitive or service sensitive you are.”
Research and Competitive Positioning as a Core Use Case — For Now
With greater insight into customer attributes and preferences, banks say they are using AI to make 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, Nichols 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.
“SouthState is not a big rate player…our theme is very much local market driven and customer service oriented,” he says.
Morristown, New Jersey-based 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.
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“That was not the case 10 or 15 years ago — now financial institutions can bring it all together,” he says.
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.
“You have to compete with other banks — how much are you willing to pay to be in the top search results?” he says. “You want to optimize your marketing efficiency.”
The Road to Dynamic Pricing for Deposits
The concept of dynamically pricing deposits on a digital rate marketplace — like what consumers see on travel or event booking platforms — is still early-stage in the U.S. market, analysts say.
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.
Compounding the challenge, if AI-driven pricing lacks transparency, regulators are likely to push back.
“If it’s just black-box AI spitting out a quote and I can’t explain how it got there, I can’t defend it to regulators,” says Carey Ransom, managing director at BankTech Ventures.
Banks also face technical barriers, particularly around data quality, Sidhwani notes.
“Dynamic pricing requires investment—both in data infrastructure and platform capabilities,” he says. “I’m not sure many financial institutions are ready to execute on that.”
Despite these hurdles, bankers say dynamic pricing is the inevitable future of deposit pricing — not because banks especially want it, but because they won’t be able to avoid it.
Without dynamic pricing, argues Sidhwani, banks “won’t be able to compete…they’re going to have to get there,” he says. “I don’t think it’s just the banks…if they disclose [information about the underlying decision model] more customers get more comfortable with it, and then the other stakeholders, the regulators.”
Sidhwani predicts dynamic, AI-powered pricing could arrive within two to three years.
Others say consumers could eventually get comfortable with dynamic pricing as they shop for deposit products — much in the same way they got used to it when browsing airfares or concert tickets.
“[It’s] controversial on the retail side, but yes, I think we’ll move more towards a demand-supply model over time. I think that’s the natural offshoot of having machine learning and having the ability to do more personalization,” says Nichols.
Despite AI’s potential to enable dynamic pricing based on demand, customer attributes, and other factors, bankers should recognize that AI won’t always make the right call, and plan accordingly.
“It’s not just destiny that AI is going to make the perfect pricing decision every time… AI is a lot like a human being. You give it the exact same question three days in a row, it may respond differently,” says Stanley. “That means it’s art — it’s not just science.”
