Can banks be more resilient to interest rate fluctuations?

In a declining interest rate environment, Portuguese banks face mounting challenges in sustaining profitability, primarily due to the widespread prevalence of variable-rate loans. As interest rates fall, net interest margins (NIM) are compressed, directly impacting Net Interest Income (NII) and limiting banks' ability to generate revenue from traditional lending activities.

The effects of falling interest rates ripple across both sides of a bank's balance sheet. On the asset side, lower rates reduce the yield on customer credit, diminishing income from loans. On the liability side, while deposit costs may decline, they often do so at a slower pace, leading to a squeeze on NIM.

Despite record profitability in 2024, banks are already experiencing consecutive quarterly declines in NIM. In response, financial institutions have adjusted deposit rates, with new term deposits under one year seeing an 8.7% decline in January 2025, dropping from 2.18% to 1.99%. Meanwhile, the decline in mortgage rates temporarily halted, with a slight increase of 0.01 percentage points, from 3.21% to 3.22%.¹

This dynamic underscores the critical need for optimized, customer-centric pricing strategies. By tailoring pricing models and product offerings to customer needs and behaviors, banks can enhance loyalty and satisfaction, mitigating some of the negative impacts on NII. Even marginal pricing improvements can have a significant financial impact—every additional basis point earned on deposits front book translates into an annualized gain of €1.31 million, based on January 2025's €13.087 billion in new term deposits under one year.

Despite the opportunity we still see banks operating in Portugal relying on pricing strategies mostly oriented per product, considering cost of funds, operating costs and servicing costs, expected revenue, risk of default, market conditions, minimum margin. However, they are not incorporating the most critical piece: the customer!

To navigate these challenges, banks must leverage advanced technologies such as Advanced Analytics. But how? First, it is crucial to have a deep understanding how different customers or segments respond to price changes. For that purpose, a simple statistical analysis of deposits historical data of pricing revisions can help banks to build price elasticity models capturing the dimensions that drive how much a customer is willing to receive. Besides measuring the overall impact of changes in price, it is also critical to understand how factors like customer demographics, customer’s history with the bank, among others can explain the magnitude of those elasticities.

Once gaining insights into price elasticity, optimization techniques can be applied to maximize margin allowing financial institutions to find the optimal trade-off between profitability and competitiveness. These models can factor in market context, customer value, internal constraints (like cost of capital, or regulatory limits), and even competitor benchmarks to generate pricing recommendations that are both personalized and efficient. Pricing optimization should consider a holistic view of customer profitability covering loans, deposits, fees, and cross-sell opportunities.

Some international banks have already begun to capitalize on the benefits of intelligent pricing achieving measurable financial and operational gains. Fifth Third Bank, a major U.S. regional bank, has adopted advanced analytics to model its deposit pricing strategies. It uses analytics to model demand curves, deposit flows within households and interest rates, aiming to find the ideal deposit price based on deposit volume and the maximum amount of interest expense it’s willing to incur².  

This kind of analytics-driven logic can be readily adapted in capital markets. Take ING, for example. The Dutch bank developed an AI-powered tool called Katana to support bond traders in making smarter, faster pricing decisions³. By analyzing large volumes of historical trade data, Katana provides real-time recommendations on optimal price points. The results show traders using Katana win 20% more trades and their prices are 20% sharper. At the same time, it helps investment managers to make better-informed, data-driven decisions.  

Crucially, the path to intelligent pricing is not only about implementing advanced technology. It requires a cultural transformation, one that empowers frontline teams, increases data literacy, and fosters close collaboration between analytics, product, sales, and risk functions. To ensure adoption, banks must clearly communicate the rationale behind pricing recommendations and validate their impact through well-designed pilot programs. Additionally, realigning incentives, for example rewarding pricing quality and profitability instead of just volume, can drive meaningful behavioral change across commercial teams.

In the Portuguese market, where banks face intense competition and limited levers for differentiation, this transformation is not just a strategic advantage, it’s a necessity. By placing the customer at the heart of pricing decisions, banks can unlock more resilient margins, improve loyalty, and build a more sustainable path to profitability, even in the most challenging rate environments.

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