Executive Summary
- Deposit pricing tools have come a long way, but there’s a disconnect between what they produce and action that hits the market. Generative AI, handled properly, can accelerate implementation measurably.
- Pricing recommendations devised via GenAI can reach decision makers in hours, instead of weeks.
- Timely action can garner deposits when they’re needed at favorable costs, leaving competitors in the dust.
In just the past few years, deposit pricing has moved from competitive surveys and sheer intuition to optimization based on a breadth of industry-wide and market-segmented data and analytics.
Financial institutions can now access platforms and tools that help identify opportunities for margin improvement and quantify the impact of pricing decisions on new deposit growth, retention of existing balances, and customer acquisition. They can apply facts and data to the perpetual challenge of balancing pricing and volume — achieving the targeted growth needed to meet funding objectives that maximizes net interest margin.
This is achieved by employing constrained optimization modeling of pricing elasticity and marginal cost of funds. This leverages advanced machine learning to capture variation by product, geographic region, and customer segment. That analysis then rolls up to overall portfolio performance.
In the rising rate environment that began in March 2022, banks that used this kind of optimization grew deposits more than their peers. And their cost advantage expanded to 15 basis points by Q2 2023.
However, the limitation of today’s state of the art is “time to action.” This results from bottlenecks in efficiency and accessibility.
Typically, the end users in a financial institution are pricing analytics teams. They run a multitude of scenario models. Taking the optimal scenario to market testing generally takes weeks. The results of each optimization scenario generally need to be interpreted by that same team of specialized analysts and then communicated to business decision makers to determine whether to operationalize the pricing.
This requires organization of the findings for product, treasury, ALCO and marketing executives in a manner that is straightforward to understand and act on. But all that takes time and effort.
While industry results demonstrate that the outcome is demonstrably better than rate surveys and intuition, the time involved can result in missed market opportunity. In other instances, an institution won’t have the resident expertise required to go through these steps, nor the budget to acquire it.
Enter generative AI.
Getting Pricing Strategy in Place While It Still Matters
When applied to deposit-optimizing technology, generative AI can significantly accelerate decision-making and time to market for deposit pricing refinements. This reduces the effort needed to interpret results, and present findings in straightforward language with supporting data assets that virtually any responsible party in the organization can act on.
Such accessibility can allow the results of scenario-based queries to be delivered to a decision-making audience within hours. Rather than requiring a team of analysts to pore over and report on those results, the scenario output can be organized in a manner that generally requires the attention of only one specialist to audit and validate.
That individual can then directly present the results to management, complete with charts, tables and reports, with minimal manual intervention or formatting. In many cases, the responsible managers themselves can interpret and act on the output with no need for a human intermediary. This can accelerate the decision-making process and the impact of launching a given pricing approach.
Even though it’s still early days, it’s safe to say that AI’s impact on managing deposit pricing is a sea change in the making.
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