AUTHOR=Krabichler Thomas , Teichmann Josef 

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

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=6

YEAR=2023

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1177702

DOI=10.3389/frai.2023.1177702

ISSN=2624-8212

ABSTRACT=<p>The extensive application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. This article presents the key notions of Deep Asset-Liability-Management (“Deep ALM”) for a technological transformation in the management of assets and liabilities along a whole term structure. The approach has a profound impact on a wide range of applications such as optimal decision making for treasurers, optimal procurement of commodities or the optimization of hydroelectric power plants. As a by-product, intriguing aspects of goal-based investing or Asset-Liability-Management (ALM) in abstract terms concerning urgent challenges of our society are expected alongside. We illustrate the potential of the approach in a stylized case.</p>