Krabichler Thomas, Teichmann Josef
Centre for Banking and Finance, Eastern Switzerland University of Applied Sciences, St. Gallen, Switzerland.
Stochastic Finance Group, ETH Zurich, Zurich, Switzerland.
Front Artif Intell. 2023 May 22;6:1177702. doi: 10.3389/frai.2023.1177702. eCollection 2023.
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.
深度学习在定量风险管理领域的广泛应用仍是一个相对较新的现象。本文介绍了深度资产负债管理（“深度ALM”）的关键概念，旨在实现资产和负债管理在整个期限结构上的技术变革。该方法对广泛的应用领域产生了深远影响，例如司库的最优决策、商品的最优采购或水力发电厂的优化。作为一个副产品，预计还会出现基于目标的投资或抽象层面的资产负债管理（ALM）中与我们社会紧迫挑战相关的有趣方面。我们在一个程式化案例中展示了该方法的潜力。
