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Why Modeling Deposit Balances Matter: The benefits of two models

Learn the differences between top-down and bottom-up data modeling and how these are associated with customer attrition risk model scores.

Gary Class
Gary Class
2024년 10월 4일 4 최소 읽기

Banks prioritize customer loyalty, focusing on attracting and retaining low-cost deposits, which are crucial for profitability. The interest rates on deposits are influenced by the yield curve, with banks managing their assets and liabilities to maximize returns. Demand Deposit Accounts, which don’t pay interest, are particularly valuable. Competitive pressures from traditional and neo-banks have heightened the focus on these accounts. Accurate forecasting of deposit balances, considering factors like the yield curve and deposit rate elasticity, is essential for banks. 

To model Deposit Portfolio Balances, banks use two approaches: top-down and bottom-up. The top-down approach forecasts aggregate deposit balances by product type, assessing seasonality and pricing elasticity. The bottom-up approach focuses on customer retention, acquisition, and product usage, using a robust customer attrition model. Combining these approaches helps banks understand customer behavior, improve retention, and manage deposit balance sensitivity to interest rate changes effectively.

Customer loyalty is a primary concern for banks around the world. While some financial institutions have spent hundreds of thousands of hours trying to tackle this problem, an accurate customer attrition risk model score may be the solution you’re looking for. But before we jump into modeling approaches, let’s start with some background.  

One of the economic functions of traditional banking is to provide “maturity transformation”. Through this process, savers supply funds (often callable on demand) to banks, who then lend the funds to borrowers with long horizons of repayment. For a traditional bank, the ability to fund loans with low-cost deposits is a key driver of profitability. Therefore, banks focus on attracting and retaining low-cost deposits throughout the interest rate cycle.  

The interest rates that banks pay on liabilities, including deposits, are heavily influenced by the yield on risk-free treasury securities, arrayed by maturity, or the “yield curve”. Normally, the yield curve is upward sloping as investors demand a premium for lending at longer maturities. An “inverted yield” curve arises when short rates are higher than longer term rates.  

Banks actively manage the composition of their assets and liabilities, as well as the interest rate received and paid out, respectively, to maximize profitability. Demand Deposit Accounts generally don’t pay interest, so they’re the least costly source of funding for banks and, therefore, highly prized. Acquiring, cultivating, and retaining Demand Deposit Accounts and accumulating balances in these accounts is strategically important for banks. Competitive pressures, both from traditional insured depository banks and from neo-banks, are compelling financial institutions to focus more attention on Demand Deposit Accounts.  

Banks have a pressing need to accurately forecast fund inflows, outflows, and monthly average balances within each deposit product type. It is crucial to be able to model how sensitive these aggregate deposit balances are to fluctuations in the yield curve. Banks also need to determine the elasticity of deposit balances with respect to the deposit rate they offer by balance tier and by maturity in the marketplace. The extent to which a bank modifies its offer rate in response to changes in the yield curve is called the “deposit beta”.  

There are two complementary approaches to modeling Deposit Portfolio Balances: 

  • Top-down product-focused modeling 
  • Bottom-up customer-centric modeling 

Top-down modeling 

The top-down approach focuses on forecasting aggregate deposit balances within deposit product type. The model differentiates between core deposits (comprised of Demand Deposit Accounts), and time deposits (composed of Certificates of Deposit with fixed maturities). The nexus of the forecasting effort is to assess the impact of seasonality of deposit balance flows and the elasticity of deposit balances with respect to the bank’s posted deposit pricing. This is then compared to the bank’s competitors in the same geography.    

Bottom-up modeling 

The bottom-up methodology focuses on the fundamental drivers of customer retention, customer acquisition, and product usage. The forecast of balances within product type is built up from modeling the portfolio dynamics of customer relationships. At the heart of this effort is the deployment of a robust model of customer attrition, as the features that impact retention also impact deposit balance augmentation and diminishment.  

Conclusion 

Banks that prioritize modeling customer attrition recognize the critical role of supporting regular deposit activity in sustaining strong customer relationships. They also understand that broader and more frequent use of payment instruments increases the likelihood of retaining customers. As the model identifies a higher probability of customer retention, the sensitivity of deposit balance changes to fluctuations in the bank's offered deposit rate decreases. 

The customer attrition risk model score serves as a key indicator of customer loyalty and can enhance the analysis of deposit balance sensitivity to yield curve shifts, as well as predict customer reactions to changes in account fees and pricing. The most effective method for forecasting deposit balances and their sensitivity to market interest rates combines both top-down and bottom-up approaches. 

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약 Gary Class

Gary is an accomplished industry strategist with extensive experience in financial services, where he has made significant contributions to advanced analytics and AI. Gary spent over three decades at Wells Fargo Bank as the Director of Advanced Analytics at the forefront of innovation during the transformational era of “anytime, anywhere” banking. His visionary leadership has shaped the landscape of financial services through innovation, data-driven insights, and strategic thinking.

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