A case study for unlocking the potential of deep learning in asset-liability-management - Krabichler, Thomas; Teichmann, Josef (Frontiers in Artificial Intelligence, 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 optimisation 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 stylised case.