A case study for unlocking the potential of deep learning in asset-liability-management

Area of Science:

• Quantitative Finance
• Machine Learning
• Risk Management

Background:

• Deep learning applications in quantitative risk management are emerging.
• Traditional Asset-Liability Management (ALM) faces challenges with complex financial instruments and long-term liabilities.
• Technological advancements are needed to transform ALM practices.

Purpose of the Study:

• To introduce the concept of Deep Asset-Liability-Management (Deep ALM).
• To explore the potential of deep learning for a technological transformation in ALM.
• To demonstrate the broad applicability of Deep ALM across various financial and operational domains.

Main Methods:

• Conceptual framework development for Deep ALM.
• Application of deep learning techniques to asset and liability management.
• Illustrative case study with a stylized financial scenario.

Main Results:

• Deep ALM offers a novel approach to managing assets and liabilities across the entire term structure.
• The methodology has significant implications for optimal decision-making in treasury functions.
• Potential applications include commodity procurement and optimization of hydroelectric power plants.

Conclusions:

• Deep ALM represents a significant technological advancement in financial risk management.
• The approach has the potential to address complex societal challenges through goal-based investing and abstract ALM.
• Further research and application of Deep ALM are warranted to fully realize its benefits.