Non-Maturity Deposit Risk Under Interest Rate Stress: A Behavioral Modeling Framework

Why It Matters

The framework gives banks a regulator‑aligned tool to gauge funding stability under rate stress, improving risk‑adjusted capital planning and liquidity resilience.

Key Takeaways

• Short‑term rates most influential factor in deposit withdrawals
• Yield‑curve steepness magnifies depositor reaction to rate shocks
• Model uses only Basel‑approved shockable variables for consistency
• Seasonal trends and Covid‑19 spikes affect withdrawal probabilities
• Framework integrates liquidity and interest‑rate risk for ALM planning

Pulse Analysis

Banks face mounting pressure to demonstrate how deposit funding behaves when interest rates shift dramatically. Traditional stress‑testing methods often rely on macro‑economic proxies that remain static under supervisory shock scenarios, creating a mismatch between behavioral estimates and regulatory expectations. By anchoring the withdrawal probability model to shock‑able market variables—such as short‑term rates and yield‑curve spreads—the new framework ensures that depositor behavior is evaluated under the same conditions regulators use for IRRBB assessments, delivering a more coherent view of liquidity risk.

The core of the methodology is a multivariate logistic regression that captures autocorrelated withdrawal events across time. Empirical results from a broad dataset reveal that short‑term rates are the primary driver of NMD outflows, while the slope of the yield curve acts as an amplifier, intensifying reactions when curves steepen or flatten sharply. Seasonal cycles and the Covid‑19 pandemic introduce additional volatility, confirming that deposit stability is not purely a balance‑sheet attribute but a market‑driven phenomenon. By quantifying these dynamics, banks can better forecast funding gaps and calibrate capital buffers.

For risk managers, the framework offers a modular, forward‑looking tool that dovetails with existing asset‑liability management (ALM) processes. It enables scenario‑based testing that aligns liquidity projections with interest‑rate stress, supporting more accurate funding strategies and compliance with Basel III’s IRRBB requirements. As regulators tighten scrutiny on funding risk, institutions that adopt such integrated models will gain a competitive edge in resilience planning and stakeholder confidence.