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Deep treasury management for banks.
Holger Englisch1, Thomas Krabichler2, Konrad J Müller3
1Department of Treasury, Thurgauer Kantonalbank, Weinfelden, Switzerland.
Frontiers in Artificial Intelligence
|April 10, 2023
Summary
Retail banks can optimize Asset Liability Management (ALM) using deep learning. This approach trains neural networks to create dynamic hedging strategies, outperforming traditional methods for managing interest rate risk.
Area of Science:
- Quantitative Finance
- Computational Economics
- Machine Learning Applications
Background:
- Asset Liability Management (ALM) is crucial for retail banks to manage interest rate risk.
- Balancing profit from maturity transformation with regulatory constraints presents a significant challenge.
Purpose of the Study:
- To develop advanced strategies for Asset Liability Management (ALM).
- To address the complexities of hedging interest rate risk in retail banking.
Main Methods:
- Formulated ALM as a high-dimensional stochastic control problem.
- Utilized neural networks to parametrize decision-making processes for investment and financing.
- Trained models to optimize long-term utility under regulatory constraints and stochastic interest rates.
Main Results:
- The Deep ALM approach successfully deduced dynamic strategies.
- These strategies demonstrated superior performance compared to static benchmarks.
- Provided practical insights for bank balance sheet management.
Conclusions:
- Deep learning offers a powerful tool for sophisticated ALM.
- Dynamic strategies derived from Deep ALM can enhance risk management and bank profitability.
