Demand deposit balance prediction models under the interest rate risk in the banking book guidelines: an empirical analysis integrating time-series models and machine learning predictions in Mexican banks

This paper explores the critical importance of the interest rate risk in the banking book (IRRBB) regulations set forth by the Basel Committee on Banking Supervision. It emphasizes the need for financial institutions to develop robust models for forecasting demand deposit balances while adhering to regulatory guidelines. To ensure these models meet the stringent requirements of risk model validation, we implement comprehensive validation techniques that assess predictive accuracy and robustness. Using a random sample of demand deposit balances in the Mexican banking sector, our research contrasts the forecasting results to robustly and efficiently construct survival rates and attrition curves (harvests) through traditional time-series methods and the use of artificial intelligence algorithms. While the Glosten–Jagannathan– Runkle generalized autoregressive conditional heteroscedasticity models showed the best goodness-of-fit results, time-series models lack the flexibility and robustness needed to capture the complexities of demand deposit balance behavior, especially in negative interest rate scenarios. In contrast, the use of machine learning techniques significantly improves predictive accuracy. The results demonstrate that our long artificial neural network–long short-term memory and random forest models improve the prediction results for demand deposit balances, allowing us to efficiently construct attrition curves and survival rates, providing better risk management tools for financial institutions facing the challenges posed by the IRRBB.